Background: Parkinson’s disease (PD) presents with motor symptoms such as bradykinesia, rigidity, and tremor that can affect gait. To monitor changes associated with disease progression or medication use, quantitative gait assessment is often performed during clinical visits. Conversely, vision-based solutions have been proposed for monitoring gait quality in non-clinical settings. Methods: We use three 2D human pose-estimation libraries  and one 3D library (ROMP) to calculate gait features from color video, and correlate them with those extracted by a Zeno instrumented walkway in older adults with PD. We calculate video-based gait features using a manual and automated heel-strike detection algorithm, and compare the correlations when the participants walk towards and away from the camera separately. Results: Based on analysis of 67 bidirectional walking bouts from 25 adults with PD, moderate to strong positive correlations were identified between the number of steps, cadence, as well as the mean and coefficient of variation of step width calculated from Zeno and video using 2D pose-estimation libraries. We noted that our automated heel-strike annotation method struggled to identify short steps. Conclusion: Gait features calculated from 2D joint trajectories are more strongly correlated with the Zeno than analogous gait features calculated from ROMP. Based on our analysis, videos processed with 2D pose-estimation libraries can be used for longitudinal gait monitoring in individuals with PD. Future work will seek to improve the prediction of gait features using a comprehensive machine learning model to predict gait features directly from color video without relying on intermediate extraction of joint trajectories. I.Individuals with Parkinson’s disease (PD) experience deterioration of gait during the progression of their disease. Specifically, changes in step length, gait speed, axial rigidity, and rhythmicity have been observed as the disease progresses Currently, quantitative gait assessment is commonly performed in the clinic using sophisticated motion-capture systems or instrumented walkways. However, these systems require large and specialized equipment that cannot be easily used outside of the clinic, thus requiring individuals with PD to visit specialized clinics to have their gait assessed. This has motivated the development and evaluation of various tools for measuring quantitative gait parameters outside of the clinic to facilitate more frequent and convenient monitoring of gait.Body-worn inertial measurement units (IMUs) have been proposed as a means to capture gait information in this population Vision-based systems provide an alternative to IMUs for unobtrusive gait assessment outside of the clinic. Gait features calculated using a Microsoft Kinect sensor, which consists of a depth and color video camera, have been validated against both gold-standard motion capture systems and GAITRite instrumented walkways  This work seeks to validate whether gait features calculated from standard color video using joint trajectories extracted using three open-source pose-estimation libraries  can be used to assess gait in individuals with PD. To achieve this goal, the video-based gait features will be correlated with the corresponding gait features extracted from a Zeno instrumented walkway  and PKMAS - ProtoKinetics Movement Analysis Software . The Zeno instrumented walkway and PKMAS system are commonly used for clinical gait assessment and have been previously validated for use in individuals with PD As the calculation of gait features from video is dependent on the detection of heel-strikes, an automated algorithm is compared with manual annotations of heel-strikes. Finally, the correlation of gait features obtained from the two systems is examined separately for walking bouts for which the participant is walking towards and away from the camera. This work serves as a first step towards understanding the capabilities and limitations of quantitative gait assessment of individuals with PD using a single-consumer grade camera.II.A.™ Walkway Gait Analysis System , turn around at the end of the walkway, and then walk 6 m back to the starting position . When possible, participants were assessed under multiple treatment conditions during the same clinical visit, and all walks were analyzed in this study. Walks were assessed on the new version of the UPDRS (MDS-UPDRS) from the video recordings by a specialist clinician (neurologist) affiliated with the study position . A tripoposition  and was position .FIGURE B.Parameters of gait were extracted independently from the data collected by the Zeno (using the PKMAS suite) and from the color videos. Due to the position of and field of view of the camera, the participants’ ankles were not visible in the video frame when they were close to the camera. The videos were thus first manually segmented to exclude the sections where the ankles were not visible, as well when the participant was turning or stationary at the beginning and end of each recording. No exclusion of bouts by step or walk quality  was performed. The first and last steps were retained in each walking bout as the planned analysis sought to correlate features from video and Zeno, rather than calculate exact values during constant walking speed. Each walking bout was divided into two walking segments: one where the participant was walking towards the camera and one where the participant was walking away. The same criteria were applied to filter the steps detected by the Zeno-PKMAS system, ensuring that the same steps were selected for analysis by both modalities.1)Three open-source human pose-estimation libraries  were used to extract the positions of body joints from the color videos The predicted joint positions were combined across frames to create trajectories of joint positions over time. Joint positions with low confidence scores were removed and interpolated using adjacent timesteps. As the confidence scores output by the three pose-estimation libraries are not calibrated, the threshold denoting “low” confidence varied , but was selected such that on average less than 10% of the timesteps were interpolated for each joint in each pose-estimation library. Finally, a zero-phase, second-order low-pass Butterworth filter with an 8 Hz cut-off was used to smooth the joint trajectories to remove noise and jitter.As a first step toward calculating gait features from video, heel-strike events were identified using two methods: automatically using a clustering algorithm and through manual annotation. To automatically identify heel strikes, the spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN) algorithm was used The joint trajectories and heel-strike labels were used to calculate five features of gait for each walking bout: cadence, number of steps, average step width, and the coefficient of variation (CV) of step width and time. Two feature sets were computed, one using the ST-DBSCAN heel-strike annotations and the other using manual annotations. A comprehensive description of these gait features and how they were calculated is presented in previous work 2)As a comparison to the three 2D pose-estimation libraries, ROMP, a state-of-the-art 3D pose-estimation library operating on monocular video was also explored as a means of extracting joint trajectories from video 3)Gait features were extracted for the same steps in the same walking bouts using the data collected by the Zeno walkway. Calculation of gait features from the data recorded by the Zeno instrumented mat was performed using the PKMAS software suite. Although more gait features are available from PKMAS, the ones selected for evaluation in this study were: the number of steps, cadence, velocity, step length, the mean and coefficient of variation (CV) of stride width, and the CV of step and swing time. As the distance measures calculated from video were unitless and normalized, the stride width obtained from the Zeno was analogously divided by the foot length. In subsequent sections of this manuscript, stride width refers to this normalized metric.C.A correlation analysis was performed between pairs of gait features extracted from video and using the Zeno system. The pairs of gait features correlated from each feature set were selected through discussion with a neurologist specializing in movement disorders and are presented in The correlation analysis was performed independently for the video feature sets extracted using automated (ST-DBSCAN) and manual heel-strike annotation, and for each pose-estimation library. Furthermore, the subsets of bouts where the participants were walking towards or away from the camera were analyzed separately.The D’Agostino-Pearson test was first used to check for normality of each of the video and Zeno features. Tables A and B of the Supplemental Material present the p-values for this normality test. At an alpha level of.05, most feature sets were not normally distributed, so Spearman’s rank correlation analysis was performed between each gait feature pair. Right-tailed p-values were calculated to assess whether the correlation coefficient was greater than 0 indicating positive correlation between Zeno and video features. Bonferroni correction was used to adjust for the number of comparisons with each Zeno gait feature.Furthermore, a Wilcoxon signed-rank test was used to assess whether there was a significant difference between gait features in different medication and DBS treatment states. Further details are presented in the Supplemental Material.All statistical analysis was performed using MATLAB 9.9.0 . An external open-source package was used to perform the normality tests III.A total of 67 walking assessments from 25 participants were analyzed in this study. A.The 67 recorded videos were divided into 134 walking bouts in which the participants were continuous ambulating. The participants were walking toward the camera in half of the walks and away from the camera in the other half. Gait features from the Zeno were successfully extracted for 133 bouts (the returning phase of one bout was not recorded). Based on the heel-to-heel measurements between the first and last step (as measured by the Zeno), the average length per walking bout was 329.0 ± 58.6 cm .The correlation between each pair of video and Zeno gait features was evaluated on all walking bouts. From 2 and slope of the linear regression model between the number of steps detected by the Zeno and vision-based ST-DBSCAN heel-strike detection method.To determine the maximum number of steps for which the ST-DBSCAN method can accurately identify the number of steps in each walking bout in this dataset, the cut-off for the maximum number of steps as detected by the Zeno was varied and the correlation of the walks included under this threshold was examined. Fig. A of the Supplemental Material displays the RBased on the results in Fig. A, the strongest correlation was observed when the maximum number of steps per walk was approximately 20, and the correlation declines quickly with walks with greater than 20 steps. Walks with more than 20 steps were excluded and the correlation analysis was repeated. A total of 8 out of 133 walking bouts  were excluded under the 20-step threshold. The mean step length (±SD) of the excluded walks was 10.8 ± 5.4 cm (range: 4.8 – 19.2 cm).B.The correlation analysis was repeated between Zeno and the gait features extracted using joint trajectories extracted using ROMP, the 3D pose-estimation library, for walks with a maximum of 20 steps. A total of 48 feature pairs were correlated and a Bonferroni adjustment factor of 6 was used to adjust for multiple comparisons between each feature pair. The results of this analysis are presented in For gait feature pairs that were also compared in the 2D analysis , the corA post-hoc analysis comparing the mean, mean absolute, and percent difference between the directly comparable gait measures of number of steps, cadence, and step time CV estimated from the Zeno and video are presented in Table C of the Supplemental Material. This analysis confirmed that the features extracted using the ROMP library had a significantly larger margin of error than any of the 2D pose-estimation libraries for these three directly comparable features.C.A subset of bouts in this data set were collected during the same clinical visit, but under different medication and DBS treatments. The Supplemental Material presents an analysis examining whether the gait features of paired walking bouts are significantly different when collected under ON and OFF treatment states.From Table D of the Supplemental Material, it can be noted that gait features calculated using manual step annotation were more consistently different when measured in ON and OFF treatment states. Differences between ON and OFF treatment states were also more consistently different when both directions of walk (towards and away from the camera) were analyzed.IV.In this work, Spearman’s rank correlation coefficient between pairs of gait features extracted independently from a Zeno instrumented walkway and from standard color video was examined. Three 2D pose-estimation libraries  were the focus of the correlation analysis, however, this analysis was also repeated with a state-of-the-art 3D pose-estimation library (ROMP). A key step in the calculation of gait features from video is heel-strike detection; so, two methods for detecting these events were examined: manual annotation and automatic annotation using the ST-DBSCAN algorithm.The initial correlation analysis between Zeno and video gait features calculated using 2D pose-estimation libraries highlighted that the automated ST-DBSCAN heel-strike method failed to detect short steps , Fig. 3.A 20-step cut-off was thus selected to facilitate a comparison of gait features for which both the manual and ST-DBSCAN methods were able to analyze roughly the same steps. This threshold was selected empirically for the data collection scenario used in this experiment and is likely to vary depending on the camera, as well as its angle and position with respect to the participant. In this investigation, the data collection set-up and selected 20-step threshold resulted in the exclusion of walks with a mean step length of 10.8 ± 5.4 cm, suggesting that the presented ST-DBSCAN method struggles with short, shuffling steps. However, the number of step or step length threshold at which the method fails will vary depending on the experimental set-up and should thus be determined empirically for future studies in different settings.A.As presented in Conversely, the calculation of step width relies on the positions of the heel-strikes as well. The ST-DBSCAN algorithm requires that the underlying joint trajectory signal be sufficiently noise-free to facilitate detection of heel-strikes. Therefore, this method only identifies heel-strikes that are clearly captured by the joint trajectory obtained by the pose-estimation library. No heel-strikes will be detected when the joint trajectory is very noisy and more likely to be inaccurate. In comparison, manual annotation of heel-strikes was done directly from video, so it is possible that heel-strikes are selected at times where the underlying joint trajectory signal is noisy, resulting in less accurate estimations of step width.From Due to the use of different units for distance measures, it is only possible to directly compare the number of steps and two temporal gait parameters calculated from video and from Zeno. Table C of the Supplement presents the mean difference, mean absolute difference, and percent difference between the Zeno and video features for the directly comparable features explored in this study . This analysis further confirmed that the manual annotation method generally led to a lower percentage difference for the temporal measures.Another key observation from Table C and 1)As seen in Comparing the correlations of Zeno and video gait features across the different 2D pose-estimation libraries, no consistent trends were observed across all gait feature pairs and directions of walk. However, there are significant fluctuations depending on the direction of the walking bout and by gait feature pair. For example, the difference in the strength of the correlation of step/stride width mean and CV varies less for walking bouts towards and away from the camera for the Detectron library than for the AlphaPose or OpenPose libraries. This difference in correlation strength is particularly pronounced when the ST-DBSCAN method is used. Furthermore, a similar difference in gait features calculated using the two footfall detection methods is noted when paired walks collected under different medication and DBS treatment states were analyzed. As observed in Table D of the Supplemental Material, gait features calculated using the manual footfall detection method were more commonly associated with expected significant differences between ON and OFF treatment states. For these reasons, it may be beneficial to use multiple pose-estimation libraries to calculate gait features and select the appropriate one based on the direction of movement, heel-strike annotation method, and gait feature of interest.B.As seen in C.The videos used in this study were collected during clinical visits but were analyzed retrospectively. For this reason, changes in camera position and orientation were common as the data collection procedure was not standardized in this regard. However, as demonstrated by our results, the gait feature extraction methods used in this work are robust to such changes in video viewpoints, with moderate to strong correlations between video and Zeno features across many gait feature pairs. As camera position was not controlled or varied between pre-defined positions during data collection, it was not possible to do a robust analysis with respect to the correlations change with respect to camera viewpoints. However, previous work by our group comparing the correlation between gait features calculated from video and those obtained from a body-worn 3D motion capture system demonstrated that there was not a significant difference between cameras recording at different heights and angles V.In this work, we examined the correlation between six pairs of gait features calculated from color video and a Zeno instrumented walkway. Feature pairs that only require the timing of heel-strikes are more strongly correlated between video and Zeno, while step/stride width mean and CV, which also require the spatial positions of the heel-strikes are only moderately correlated. In our experiments, we note that the ST-DBSCAN automated heel-strike detection algorithm leverages the underlying joint trajectory signal when identifying the timing of heel-strikes, and was thus similarly correlated to Zeno gait features as when manual annotation is used.In our analyses, the automatic ST-DBSCAN heel-strike annotation method struggled to identify short, shuffling steps. Using an experimentally selected cut-off threshold of 20 steps for our dataset, walks with an average step length of 10.8 ± 5.4 cm (range: 4.8 – 19.2 cm), and representing less than 7% of all walking bouts were omitted from further analysis. This suggests that manual annotation is necessary to analyze gait of individuals with severe PD who have very short steps or experience freezing of gait. We also note that there were differences in the video recording methodology for this dataset, leading to differences in the viewpoint from which the videos were recorded. This suggests that the proposed method of calculating gait features from video is robust and shows promise as this work moves out of the clinic and is applied to videos collected in less controlled non-clinical environments.trends within features estimated by the video modality. This information can be used by clinicians to assess the impact of medication and DBS therapies and make appropriate adjustments to minimize gait impairments.Overall, the gait feature values calculated from video and Zeno cannot be used interchangeably as they were calculated using different methods and have different units (in the case of step/stride width). However, the gait features are significantly correlated when 2D pose-estimation libraries and appropriate heel-strike annotation methods are used. Our video-based gait analysis tool can thus be used for longitudinal monitoring of changes in gait in a PD population by comparing A limitation of this work is that gait features such as step length that rely on data in the anterior-posterior direction could not be calculated with the trajectories extracted using the 2D pose-estimation libraries. For this reason, ROMP, a state-of-the-art 3D pose-estimation library operating on monocular video was also explored as a means of extracting joint trajectories from video. However, the ROMP library struggled to accurately track the position of joints in the anterior-posterior direction for the duration of each walking bout, leading to inaccurate calculations of gait features that rely on data in this direction. For this reason, there is currently no advantage to using ROMP over the three 2D pose-estimation libraries investigated in this work.Future work will seek to improve the estimation of 3D gait parameters from standard video. The features calculated in this work relied on the extraction of skeleton trajectories from video as a preliminary step. Instead, the estimation of gait features, including those that require information from the depth dimension such as gait speed and step length, could be incorporated directly into a machine learning model that operates on the input videos. This would allow for the development of an end-to-end framework that is finetuned for predicting more accurate gait features, rather than relying on general pose-estimation libraries to first extract joint trajectories.


This study highlights the role of comparing different metal ions on synthetic nanoapatite in regulating the antibacterial properties and toxicity. The comparisons between infrared spectra and between diffractograms have confirmed that metal ions do not affect the formation of HA phases. The results show that after doped Cu2+, Mg2+, and Zn2+ ions replace Ca2+, the ionic radius is almost the same, but significantly smaller than that of the original Ca2+ ions, and the substitution effect causes the lattice distance to change, resulting in crystal structure distortion and reducing crystallinity. The reduction in the length of the nanopatites after the incorporation of Cu2+, Mg2+, and Zn2+ ions confirmed that the metal ions were mainly substituted during the growth of the rod-shape nanoapatite Ca2+ distributed along the longitudinal site. The antibacterial results show that nanoapatite containing Cu2+ (0.1%), Mg2+ (3%), and Zn2+ (5–7%) has obvious and higher antibacterial activity against gram-positive bacteria Staphylococcus aureus within 2 days. The antibacterial effect against the gram-negative bacillus Escherichia coli is not as pronounced as against Staphylococcus aureus. The antibacterial effect of Cu2+ substituted Ca2+ with an atomic ratio of 0.1~1.0% is even better than that of Mg2+- and Zn2+- doped with 1~7% groups. In terms of cytotoxicity, nanoapatites with Cu2+ (~0.2%) exhibit cytotoxicity, whereas Mg2+- (1–5%) and Zn2+- (~1%) doped nanoapatites are biocompatible at low concentrations but become cytotoxic as ionic concentration increases. The results show that the hydrothermally synthesized nanoapatite combined with Cu2+ (0.2%), Mg2+ (3%), and Zn2+ (3%) exhibits low toxicity and high antibacterial activity, which provides a good prospect for bypassing antibiotics for future biomedical applications.The objective of this study was to prepare hydroxyapatite (HA) with potential antibacterial activity against gram-negative and gram-positive bacteria by incorporating different atomic ratios of Cu However, several types of bacteria can develop antibiotic resistance due to a combinatorial drive of bacteria exposed to antibiotics . The curoceramic . Apatiteoceramic . Differeoceramic ,20,21,22oceramic ,9, sol-goceramic , hydrothoceramic ,9,10 andoceramic ,16, or ioceramic ,18,19. Roceramic ,24,25. TAlthough apatite offers benefits as a biomaterial implant, it can affect the proliferation rate of osteoblasts and the expression of cytokine genes ,27. Howe2+ sites, but some bind to negatively charged P sites [2+ can target bacterial cells; they penetrate and damage the cell membrane, promote protein alkylation, and cause oxidative stress, thus resulting in genetic material damage and death [2+, Mg2+, Zn2+, Ag+, Sr2+, Li+, and Si4+) to the HA lattice does not affect the structural configuration and biocompatibility and can effectively change the lattice, crystallinity, solubility, bacteriostatic, and angiogenesis of nanoapatites [2+, Mg2+, and Zn2+ metal ions with nearly the same anion radius were investigated as targets for the hydrothermal synthesis of doped nanoapatites. The metal ion Cu2+ is an essential trace element for the human body and exerts an antibacterial effect while promoting bone formation, inhibiting osteoporosis, and promoting human endothelial cell proliferation [2+ in the human body is stored in the bones, and Mg2+ regulates osteoblast or osteoclast activities [2+ deficiency can cause skeletal growth arrest and osteopenia. Mg2+ induces endothelial cells to produce nitric oxide, leading to migration and growth and thereby accelerating the formation of new blood vessels [2+ increases the activities of osteoblasts, activates bone formation, promotes the synthesis of collagen, and inhibits the bone resorption of osteoclasts [A nanoapatite crystal has a hexagonal structure with two distinct charged sites . Normally added positively charged metal ions enter the lattice to displace Ca P sites ,29,30. Nnd death . Adding apatites ,35,36,37feration ,39,40. Ativities . Thus, M vessels ,42,43; Zeoclasts ,45,46.2+, Mg2+, and Zn2+ with Ca2+ for the preparation of nanoapatites with antibacterial properties through hydrothermal wet synthesis and compared their antibacterial properties and biocompatibility. The composition, phase identification, morphological changes, antibacterial activity and cytotoxicity of synthesized nanoapatite doped with Cu2+, Mg2+, and Zn2+ ions were investigated.Here, we proposed the co-precipitation of CuL) and asymmetric stretching absorption (ν3) of hydroxyl (OH−) groups observed at wavenumbers of 632 and 3568 cm−1, respectively. The apatite PO43– absorption bands at wavenumbers 563 and 603 cm−1 were observed and attributed to the bending vibration of O−P−O (triply degenerate δ4 bending in PO43−). The symmetric stretching band (ν1 mode in PO43−) at 964 cm−1 and asymmetric stretching bands (ν3 mode in PO43−) at 1034 and 1097 cm−1 were detected [4+ functional group was found at 1385 cm−1, and the C–H symmetrical and asymmetric stretching vibration bands were observed at 2854 and 2924 cm−1, respectively, which were attributed to the diammonium hydrogen phosphate ((NH4)2HPO4) and residual functional groups of sodium citrate (Na3C6H5O7). The C=O stretching vibration bands of CO2 were observed at 1448 and 1635 cm−1 [The characteristic apatite absorption bands are shown in detected ,48,49,50635 cm−1 .2+, Mg2+, and Zn2+ ion doping levels are illustrated in 2+ (1.14 Å) by Cu2+ (0.87 Å), Mg2+ (0.86 Å), and Zn2+ (0.88 Å) ions, resulting in lattice distortion in nanoapatite crystalline structures [To elucidate the phase structure and crystallinity produced by the hydrothermal reaction, the XRD patterns of nanoapatites with different Cuructures ,53,54.2+, Mg2+, or Zn2+). Each group was in the form of rod-shaped nanoapatites. In SAED analysis, the diffraction patterns of the characterized (002), (211), and (112) planes and the lattice distances in HRTEM were observed, and HA was observed to correspond to 0.34 and 0.28 nm of the (002) and (300) planes, respectively. This result was verified with the XRD patterns. The doped and undoped rod-shaped nanoapatites had average lengths of 31–49 nm, average widths of 6–12 nm, and average aspect length-to-width ratios of 2–5. No significant differences in the plane ratios between length (002) and width (300) in the lattice calculated from the FWHM value of the XRD pattern were found, but the plane ratio values of Cu2+, Mg2+, and Zn2+ were greater than the value of the Ca2+-only control group (p < 0.05).ol group . Except 2+ (1.14 Å) at the longitudinal sites of a nanoapatite were mainly replaced by Cu2+ (0.87 Å), Mg2+ (0.86 Å), and Zn2+ (0.88 Å) with small ionic radii. The average transverse width of 3Mg−nHA (6.80 ± 1.74 nm) was smaller than that of nHA  and more elongated, but the average width of 7Zn−nHA (12.05 ± 2.62 nm) was larger than that of nHA (p < 0.05), making it short and wide.According to Shannon’s ionic radii in a previous study , which r2+, Mg2+, and Zn2+ ions on S. aureus are shown in 2+, Mg2+, and Zn2+ ions exhibited better bacteriostatic ability than the control nHA group without metal ion addition, especially on day 1 (p < 0.05). As for the individual antibacterial strength of each ionic group, the antibacterial ability of the 0.1Cu−nHA group at a low concentration was comparable to that of 1Cu−nHA at a high concentration until day 3 in terms of strength  compared with that in the nHA control group on day 2  is much smaller than that of Ca2+ (1.14 Å), which causes the possibility of free entry and exit of bacterial cells, destroys cell wall synthesis, and reduces the protective effect of bacteria. For example, literature suggests that Cu2+ ions can bind to thiol bonds in the cytoplasm, disrupting bacterial metabolism and differentiation [The cell wall structure of . aureus , and thuc effect ,58. In aand Zn2+ . The parntiation . Hence, 2+, Mg2+, and Zn2+ showed no toxicity. The nanoapatite groups with Cu2+ showed cytotoxicity, whereas nanoapatites with Mg2+ and Zn2+ and the 7Mg−nHA, 3Zn−nHA, 5Zn−nHA, and 7Zn−nHA groups still showed cytotoxicity [toxicity . The celS. aureus resistant to asmethicillin and vancomycin, in implants and related in vitro and in vivo medical devices [Enterococcus, Staphylococcus, and Streptococcus) and gram-negative bacteria (E. coli and Pseudomonas). However, our study and some other studies have shown that nanoapatites are more active against gram-positive bacteria than against gram-negative bacteria 6(OH)2 (x = 0.05–2.0), and the antibacterial ratio against S. aureus is 0.2. Their results were confirmed in the present study 2 ∙2H2O), which is the most favorable condition for the occurrence of Zn2+ in tetrahedral coordination and Ca2 sites with obvious local structure distortion. In the coordination of Ca1 and P sites with Zn, all higher shells move to longer and shorter distances and lead to elongated crystallites. These results contradict our results in 2+ at the Ca2 site leads to the overall shrinkage of the local structure, most of the higher shells move toward Zn, and thus the high degree of Zn2+ incorporation resulted in the shorter morphology of the nanoapatite 2 ∙4H2O, purity > 98.0%, KATAYAMA CHEMICAL Co., Ltd., Osaka, Japan), diammonium phosphate ((NH4)2HPO4, purity > 98.0%, HSE PURE CHEMICALS, Ahmedabad, India), sodium citrate , copper (II) nitrate (Cu(NO3)2 ∙3H2O, purity > 99.0%, PANREAC, Barcelona, Spain), magnesium nitrate (Mg(NO3)2 ∙6H2O, purity > 98.0%, PANREAC, Barcelona, Spain), zinc nitrate (Zn(NO3)2 ∙6H2O, purity > 98.0%, PANREAC, Barcelona, Spain), acetic acid , and sodium hydroxide .The materials used in this study were calcium nitrate 2 ∙4H2O and different amounts of Cu2+, Mg2+, and Zn2+ ions (as indicated in 2O). The pH of the resulting solution was adjusted to 7. Then, 0.713 g of (NH4)2HPO4 and 2.647 g of Na3C6H5O7 were stirred with 70 mL of ddH2O for 30 min to form an anion solution. The aforementioned two cation and anion solutions were mixed and stirred for an additional 30 min. The nanoapatites were synthesized through hydrothermal synthetic reaction at 180 °C and 220 psi (l b/in2) for 12 h. After the reaction, the nanoapatites were washed three times with ethanol and ddH2O at 3000 rpm for 5 min each time and then dried at 60 °C for 12 h. The yield of the resulting precipitate in this study was about 30% (w/w).The hydrothermal synthesis of nanoapatite (nHA), Curocedure . The conThe functional groups of the nanoapatites were analyzed using a Fourier transform infrared (FTIR) spectroscopy  to confirm the structural composition .θ was 20–60°, and the phases were identified by comparing the diffraction patterns with the patterns in the Joint Committee on Powder Diffraction Standards (JCPDS).The crystalline phases, crystallinity, and preferred orientation changes in the nanoapatites were investigated through X-ray diffraction (XRD) pattern analysis. Diffraction analysis was performed using an X-ray diffractometer , and the diffraction conditions were as follows: Kα diffraction using a Ni-filtered Cu target at a voltage of 30 kV, current of 20 mA, and scan speed of 2°/min. The scanning range of 2The sample was mixed with 99.8% alcohol  and dispersed uniformly with an ultrasonic oscillator . The dispersion solution was dropped onto a copper mesh . The morphology of HA was analyzed using transmission electron microscopy  at 200 kV and bright-field images. The microstructure of HA was observed using a selected area electron diffraction (SAED) system with an aperture size of 160 nm, and the crystal structure was analyzed through high-resolution transmission electron microscopy (HRTEM). Digital Micrograph (Gatan) software was used to measure the diffraction plane distance (d). Image-J program (software version 1.53e) was used to analyze the TEM images of each group of samples and calculate the lengths, diameters, and aspect ratios of the nanoapatites.Staphylococcus aureus (ATCC No. 25923) and Escherichia coli (ATCC No. 10798) were cultured in Luria–Bertani broth [595) of 0.2  and this value was confirmed with an enzyme-linked immunosorbent assay (ELISA) reader . The bacterial suspensions of S. aureus and E. coli were subsequently diluted to achieve an OD595 value of 0.2. For quantitative antibacterial testing, 0.03 g of a sample was immersed in 2 mL of bacterial suspension and incubated at 37 °C for 1–4 days. Approximately 100 µL of the bacterial solution was collected from each group, and its OD595 value was obtained with the ELISA reader (n = 3).ni broth ,68,69. T2 and subcultured when the cell concentration was between 0.8 × 106 and 1.0 × 106 cells/mL. The medium used was a minimal essential medium alpha medium  containing 10% horse serum and was changed every 2 days of culture.The L929 cell line from newborn mouse fibroblasts was provided by the National Institute of Health in Taiwan and used for the cytotoxicity tests. The testing procedures were conducted by ISO 10993-5:2009. L929 cells were grown with media in an incubator at 37 °C and 5% CO2 . The extraction solution was prepared by placing the sterilized nanoapatites, a negative control group of high-density polyethylene (HDPE) and a positive control group of 15 vol.% dimethyl sulfoxide  in the culture medium. Owing to the hydrophilicity of the nanoapatites [The samples prepared for cell culture were sterilized by autoclaving at 121 °C and 1.05 kg/cmapatites , the ext4 cells was transferred to a 96-well microliter plate and cultured at 37 °C for 24 h in an incubator. The medium was removed, and then 100 μL of the sample extract was added. L929 cells were incubated in a 37 °C incubator for 24 h and then mixed with 50 μL of a XTT cell proliferation assay kit  for a 4 h extension reaction before treatment with another ELISA reader . The measured OD490 absorbance was proportional to cell viability.For the quantitative cytotoxicity test, 100 μL of suspension with an L929 cell concentration of 1 × 105 cells/well. The original medium was cultured in an incubator at 37 °C and 5% CO2 for 1 day, and then the medium was removed. Sample extract (100 μL) was added and cultured for 24 h, and cell morphology was observed under an inverted microscope .In the qualitative cytotoxicity test, the preparation of the extracts and the culture medium of the control group were the same as those in the quantitative test. Approximately 1000 μL of cell suspension was collected, and L929 cells were transplanted into a 48-well microliter plate at a cell concentration of 1 × 10Analysis of variance (ANOVA) was performed using IBM SPSS Statistics version 20  for the measurement of average longitudinal length, transverse width, length-to-width aspect ratio and antibacterial activity. ANOVA was used in determining whether differences among the means of multiple groups were significant. The estimates of two different variables were used in comparing the differences.2+, Mg2+, and Zn2+) exhibited initial antibacterial activity, the magnitude of which was Cu2+, Mg2+, and Zn2+ in descending order. After the addition of metal ions, the main characteristic peaks were slightly shifted due to the smaller size of the HA crystal structure. In particular, the length shrinkage of the nanorods. Cu2+, Mg2+, and Zn2+ replaced Ca2+, resulting in changes in the lattice structure, deformation of the crystal structure, and a decrease in crystallinity. In this formulation, Cu2+, Mg2+, and Zn2+ ions replaced Ca2+ in the apatite mainly at the longitudinal positions of the nanorods. Antibacterial test results showed that adding Cu2+ ions with an atomic ratio of 0.1% was more effective than doping Mg2+ (3%) and Zn2+ (5–7%) ions, although they both showed gram-positive antibacterial activity. Nanoapatite with all Cu2+ ion concentrations was toxic, but 0.2% Cu2+ showed relatively little cytotoxicity in the group. After adding Mg2+ ions to the nanoapatite, only the 7% Mg2+ group with the highest doping amount showed slight cytotoxicity and was the test group with the highest biocompatibility. For Zn2+ ionic groups that exhibit moderate cytotoxicity, doping at 1% of the Zn2+ groups is expected to be biocompatible, while 3% of the Zn2+ groups have little cytotoxicity. Therefore, in terms of biocompatibility and antibacterial synergistic function of nanoapatite, the best group is 3% Mg2+-doped nanorods. Future studies should consider expanding the testing range; for example, if the 0.2% Cu2+ doped nanorods with the strongest antibacterial activity in this study were incorporated into other biocompatible medical devices, it might reduce cytotoxicity but maintain bacteriostatic properties.In summary, almost all nanoapatites with metal ions (Cu


The aim of this study was to analyze the shoulder functional profile (rotation range of motion [ROM] and strength), upper and lower body performance, and throwing speed of U13 versus U15 male handball players, and to establish the relationship between these measures of physical fitness and throwing speed.One-hundred and nineteen young male handball players (under (U)-13 (U13) [n = 85]) and U15 [n = 34]) volunteered to participate in this study. The participating athletes had a mean background of sytematic handball training of 5.5 ± 2.8 years and they exercised on average 540 ± 10.1 min per week including sport-specific team handball training and strength and conditioning programs. Players were tested for passive shoulder range-of-motion (ROM) for both internal (IR) and external rotation (ER) and isometric strength  of the dominant/non-dominant shoulders, overhead medicine ball throw (OMB), hip isometric abductor (ABD) and adductor (ADD) strength, hip ROM, jumps (countermovement jump [CMJ] and triple leg-hop [3H] for distance), linear sprint test, modified 505 change-of-direction (COD) test and handball throwing speed (7 m [HT7] and 9 m [HT9]).d: 0.76–2.13) and lower body  performance measures. Regarding shoulder ROM outcomes, a lower IR ROM was found of the dominant side in the U15 group compared to the U13 and a higher ER ROM on both sides in U15 (d: 0.76–1.04). It seems that primarily anthropometric characteristics  and upper body strength/power (OMB distance) are the most important factors that explain the throw speed variance in male handball players, particularly in U13.U15 players outperformed U13 in upper (i.e., HT7 and HT9 speed, OMB, absolute IR and ER strength of the dominant and non-dominant sides; Cohen’s Findings from this study imply that regular performance monitoring is important for performance development and for minimizing injury risk of the shoulder in both age categories of young male handball players. Besides measures of physical fitness, anthropometric data should be recorded because handball throwing performance is related to these measures. Handball is an intermittent sport which requires high levels of different physical fitness components such as endurance, strength, speed, and coordination . In termThe ultimate aim of a handball match is to score more goals than the opponent. Accordingly, the handball throw is a fundamental movement skill that should be already developed during the early stages of long-term athlete development . As withAs a result of the physical demands induced by training and/or competition, handball players are susceptible to a range of injuries including chronic overuse conditions and acute traumatic injuries . ShouldePrevious studies analyzed the relationship between upper and lower limbs muscles’ strength in handball players of different expertise level and sex , 17, 18.Thus, the purpose of this study was to analyze the shoulder functional profile (rotation ROM and strength), upper and lower body performance, and throwing speed in U13 versus U15 handball players. In addition, we aimed to examine the relationship between measures of physical fitness and throwing speed in the same cohort. With reference to the relevant literature , 21, we A convenience sample of 135 young healthy male handball players volunteered to participate in this study. All players were recruited from the youth academy of Abanca Ademar León handball club  and were selected by their respective coaching staff. Teams from this youth handball academy play at top national level. For the purpose of this study, players were grouped into two age groups: U13  and U15 . The participating athletes had a mean background of systematic handball training of 5.5 ± 2.8 years and they exercised on average 540 ± 10.1 min per week including sport-specific team handball training and strength and conditioning programs. Table This cross-sectional study was conducted to compare the performance in measures of physical fitness of two different age-categories of handball players (U15 and U13), from a top-level Spanish handball club, following a similar previous experimental design . TestingBody height was measured using a fixed stadiometer , sitting height with a purpose-built table , and body mass with a digital scale . Pubertal timing was estimated according to the biological maturation of each individual using a predictive equation as described previously . The ageDominant and non-dominant passive glenohumeral rotation were assessed with a manual inclinometer  as previously described . BrieflyIsometric shoulder IR and ER strength of the dominant and the non-dominant side were assessed with a portable handheld dynamometer  as previously described . PlayersThe players stood on a line with their feet side-by-side and slightly apart, facing the direction to which a 2-kg medicine ball had to be thrown. The ball was brought back behind the head and then thrown vigorously forward as far as possible without the player crossing the line. The distance from the line to the point where the ball landed was measured, and the best performance among 3 efforts was recorded to the nearest 5 cm. There was a 45-s rest period between trials. ICC and CV were 0.94 and 4.3%, respectively.Before the performance of the throwing test, a standardized warm-up was allowed to the participants which included upper body mobility, active stretching, ballistic exercises and 5 throws performed at progressive velocities. For the 7-m throw test (HT7), 5 throws were performed from the seven-meter line, allowing only one foot to be lifted without stepping on the seven-meter line. By doing so, a penalty throw in handball was simulated, with a 45-s rest between each throw. For the 9-m throw test (HT9), 5 throws were performed after a 3-step running throw from the 9-m line with a vertical jump and a rest of 45-s between throws . The thrFor the measurement of maximal isometric hip ADD and ABD strength in the dominant and non-dominant limbs, a handheld dynamometer  was used which was calibrated prior to each test. The applied test method was in accordance with Thorborg et al. . In brieThe passive hip IR and ER and passive hip ABD ROMs were measured at an angle of 90° hip flexion of the dominant and non-dominant limbs, using an inclinometer  with a telescopic arm. The applied methods were reported previously . A 30-s A bilateral CMJ weas performed on a contact-time platform  according to the protocol as described previously by Nuñez et al. . During The triple leg-hop test requires participants to perform 3 consecutive hops on the same leg aiming for maximum distance . The toeTime during a 20-m linear sprint (with 5 and 10 m split times) was measured by means of single beam photocell gates placed 1.0 m above the ground level . Each sprint was initiated 1.0 m behind the first photocell gate, which then started a digital timer. Players started the linear sprint test in a standing split position, with their preferred foot behind the starting line, followed by accelerating forward at maximal effort until they passed the last photocell gate placed at 20 m. Each player performed three maximal 20-m sprints with at least 2 min of passive recovery in between the trials , and theDEF) for the 505 test was calculated using the following formula: CODDEF = (modified 505 time – 10-m time) ) after normality of data was confirmed using the Kolmogorov–Smirnov test with the Lilliefors’ correction. ANCOVAs were performed to assess between-group differences for each parameter, using AGE as between-subject factor  and maturity offset (MO) as covariate. ANOVAs instead of ANCOVAs were used when the MO did not show a significant effect as a covariate. Hedges’ mparison . This inrison [d , but it to Cohen , effect is (RWA)  was usedF = 0.4; power = 80%; α = 0.05). Taking into account the high heterogeneity shown by PwMS  as well as the high within-subject variability [dg = 0.8; power = 80%; α = 0.05). Finally, regarding the RWA and based on previous results [f2 = 0.35; power = 80%; α = 0.05) for the multiple linear regression model with 12 potential predictors.Before performing the ANOVAs and RWAs, the sampling software package GPower 3.1.  was usediability , 43, larTable age for both handball throws   and medicine ball throw test (OMB) . U15 also showed higher absolute IR and ER strength for the dominant and non-dominant shoulder than U13 . However, no age effect was observed for the normalized shoulder strength, ER/IR ratio or bilateral strength deficits. Finally, for shoulder ROM, U15 showed lower IR of the dominant arm  and larger ER ROM in both arms than U13 . No age effect was observed for the shoulder ER/IR ROM ratio and bilateral ROM deficits.For the upper body performance Table , ANOVAs age for most of the analyzed parameters. More specifically, U15 showed higher CMJ values , larger hop distance (3H) , faster linear sprint  and COD times  than U13. U15 also showed higher hip ABD and ADD strength for both, the dominant and non-dominant lower limbs . No main effects of age were observed for bilateral hip ABD/ADD strength ratios and CODDEF.Results of the lower body performance tests are shown in Table 2 ≤ 70.2%) in the handball throw speed when the whole sample and the U13 were analyzed (Table 2 ≤ 20.2%) to the variation in throwing speed. Lower limbs performance factors  (3.0% ≤ R2 ≤ 7.2%) and shoulder strength also contributed to explain throwing variance (3.2% ≤ R2 ≤ 8.3%).The RWA models explained a large variance (66.1% ≤ Red Table . However2 ≤ 12.4%) was the most important factor in explaining throw speed variance. Shoulder IR strength, OMB distance and linear sprint time over 20-m were also significant factors to explain HT7 variance in the U15 group (8.1% ≤ R2 ≤ 8.4%).Regarding the U15 group, participants’ performance during the 3H test , upper and lower body performance, and throwing speed of U13 versus U15 male handball players. Another research goal was to examine the relationship between these physical variables and handball throwing speed. The main results showed that chronologically older (U15) players outperformed younger players (U13) in upper  and lower body  performance measures Tables . RegardiOur findings are mainly in agreement with the literature in regards of the effect of chronological age and/or maturity on physical fitness  and motor skill development  in youth. In this regard, it is not surprising that U15 players outperformed the younger players (U13) in almost all performance measurements, especially in the lower body Table . This isRegarding the upper body characteristics Table , handbalOur results are in line with previous literature related to research with athletes performing overhead sports , 28, 48.Furthermore, U15 players were stronger than U13 players in terms of absolute values Tables . HoweverIn throwing sports, reduced IR and increased ER ROMs have been reported for the dominant arm of asymptomatic overhead athletes , 50, 51.The ability to sprint over short distances, as well as to change direction during side-stepping movements is of great importance for achieving higher performance levels, allowing players to be faster during transitions , and during fast breaks and offensive breakthroughs . MoreoveComparable to speed development, the COD capacity appears to improve from childhood through adolescence . During Regarding the COD deficit, results from this study showed no differences between age categories Table , which iLower limb muscle strength and power is a basic requirement to perform explosive actions in intermittent sports  . More spTo the authors’ knowledge, there are no studies available that analyzed hip strength in different age groups of handball players. Therefore, we cannot compare our findings with results from previous research. Studies investigating different sport disciplines showed that hip strength deficits appear to impede the ability to eccentrically control sudden, powerful over-stretching of the adductors during lateral stride maneuvers involving abduction and external rotation, resulting in increased susceptibility to groin injuries , 61. We This study revealed large explained variance for throwing speed for the whole sample of handball players  and for the U13 age group  Table . A perfeWe must acknowledge some limitations of the current study, including the lack of a larger sample of young athletes, especially in the U15 group, or having more distinct age groups to examine the evolution of these parameters throughout the players’ development. The observed findings on the variance of throw speed could be related to lower body performance  or core-strength that were not tested. Moreover, the fact that both handball throws are dynamic movements, the use of isometric shoulder strength tests as applied in this study, could be a limiting factor due to incongruency of muscle contraction velocity during the handball throw versus the isometric contraction. Finally, findings from this study cannot be translated to female handball players or younger/older age groups. Accordingly, more research is needed to verify our findings in these populations.In summary, this study revealed that chronologically older (U15) male handball players outperformed their younger counterparts in most measures of physical fitness. In U13 players, handball throw variance was explained by anthropometric and selected physical fitness factors. In U15 players, variance was lower and included only factors of physical fitness. Findings from this study imply that regular performance monitoring, including an individual shoulder profiling , is important for performance development and for minimizing injury risk of the shoulder in youth male handball players.


The COVID-19 pandemic has had an enormous impact on traditional tourism. Influenced by the uncertainties of the current epidemic, to revive the development of tourism and local economics, local governments have used short video accounts to release tourist promotion short videos for publicizing and marketing. This study takes official promotion short videos as the research object, establishes a structural equation model based on the SOR theory, and explores the influencing factors of official short videos on tourists’ destination decisions through empirical analysis. It finds that the official promotion short videos can positively stimulate users’ perception and sentiments significantly due to the three unique features of authority, interactivity and interest, thus influencing tourists’ destination decision-making behavior. On this basis, this study proposes to improve the operation of an official short video from three aspects: enhancing the authority of official short video accounts, attaching importance to the interactive mechanism, and strengthening the creativity of short videos. It is hoped that the study may help enhance the influence of official promotion short videos and promote the high-quality development of local tourism. The Covid-19 epidemic spreading all over the world in 2020 not only significantly influences traditional service industries such as catering, accommodation, and tourism, but also changes consumers’ consumption habits and psychology to some extent. Affected by the COVID-19 epidemic, the number of China’s domestic tourists and tourism income both dropped sharply in 2020, and the loss of total tourism revenue was more than $600 billion . Rural tAt present, the uncertainties of epidemic development make all industries in China take normalized epidemic prevention and control measures. Therefore, the traditional tourism publicity and promotion mode can no longer match the objectives of normalized epidemic prevention and control policy, and the tourism marketing problem has become prominent increasingly. On the other hand, after long-term home quarantine, residents may have some revenge spending, and there will be a great travel demand during the stable epidemic period. How to promote tourism through a brand-new model at a time of heightened uncertainties and risks and then revitalize the tourism industry and increase tourism revenue has become an urgent problem for local governments to solve.Technological advances and the use of social media play an important role in driving economic development . After tLive streaming, as a new feature of the short video platform, is of significance to the recovery of tourism . For exaTherefore, this study aims to explore how to better use the official promotion short videos to promote tourism resources and effectively guide potential consumers’ travel consumption under the background of the epidemic uncertainties. Based on the stimulus-organism-response (SOR) theory, this study constructs a structural equation model of the official promotion short videos’ influence on tourists’ destination decision-making behavior. With the online questionnaire data, the relationship between variables was tested by the structural equation model, and then the influencing factors of official promotion short video on tourist destination decision were analyzed. It is hoped that the present study may provide decision-making reference for promoting the development of local tourism.The image of a tourist destination refers to the overall impression of a tourist destination interwoven with various tourism products and elements . In subsTourists’ destination decision-making behavior is influenced by many factors, including tourists’ internal psychological quality and external social environment . The touWith the development of new media platforms, short videos have gradually become the main channel of constructing, publicizing, and marketing the image of tourist destinations in recent years. From the perspective of tourist perception, To sum up, short video, an emerging media communication platform, has a significant impact on tourists’ destination choice, and the official-projected image of local destination will also affect tourists’ perception. However, few studies took the official short video account as the research object to discuss its impact on tourist destinations. In the post-epidemic era, China’s tourism industry is also actively seeking for transformation and development and attracting consumers with new ways of travel. The government, as the regulator of the tourism industry, has the responsibility and obligation to promote the high-quality development of tourism while taking epidemic prevention and control measures and creating a safe consumption environment. Driven by the intelligent and digital transformation of the tourism industry, official promotion short videos become the first choice for the government to involve in tourist destination marketing and promotion. Through the planning and operation of official short video account and the shooting and release of official promotion short videos, the government can not only set up a good government image, but also promote the development of local economy, using government affairs new media to boost local tourism industry. Under the background of uncertain epidemic development, it is of great significance to use the official short video account to publicize and market the development of local tourism. Therefore, this study uses empirical research methods to analyze the influencing factors of official short videos on tourists’ destination decision-making. It is hoped that this study may provide a reference for local governments to use short video platforms efficiently to attract tourists, publicize tourism resources, and promote the development of local tourism.Specifically, the official promotion short videos’ production quality and the image of tourist destinations they construct will affect the psychological reaction of tourists who then make consumption decisions. When consumers, driven by certain travel purchase motives, purchase tourism products and produce a series of post-purchase behaviors in order to meet certain travel needs, it is a complete travel purchase decision. Therefore, this paper takes the official short videos as the research object and discusses the influence of its stimulus on tourists’ destination decision-making combined with the SOR model.Based on the SOR theory, this paper constructs a structural equation model of the influence of official promotion short videos on tourists’ destination decision-making behavior .The characteristics of official short video accounts and those of short video platforms jointly determine the characteristics of official promotion short videos. In terms of official short video accounts, they have an official background. The government authorizes the official accounts to produce and release official promotion short videos . Thus, tCombined with the SOR model, the official promotion short video, with its special communication mechanism, will be used as an external stimulus to affect the perception or sentiment of potential consumers in the process of watching and then make them produce the decision-making behavior reaction of tendency or avoidance. Taking this model mechanism as a reference, according to the existing research literature results, this study proposes the hypothesis that tourists, as potential consumers, can get stimulation from watching official promotion short videos, which are characteristic of authority, interactivity and interest. After obtaining such an external stimulus, the tourist organism will generate the cognitive reaction of obtaining perceived value, producing perceived pleasure and perceived trust. After the perception reaction is generated, it will further influence the final decision of tourists as potential consumers to accept or reject the destination . This study verifies and analyzes the above hypotheses based on the questionnaire data.The Order of Discourse. In any society, once discourse comes into being, it is immediately controlled, screened, organized, and redistributed by power, and discourse serves power .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer MG declared a shared affiliation with the author DL to the handling editor at the time of review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Collisions with windows on buildings are a major source of bird mortality. The current understanding of daytime collisions is limited by a lack of empirical data on how collisions occur in the real world because most data are collected by recording evidence of mortality rather than pre-collision behaviour. Based on published literature suggesting a causal relationship between bird collision risk and the appearance of reflections on glass, the fact that reflections vary in appearance depending on viewing angle, and general principles of object collision kinematics, we hypothesized that the risk and lethality of window collisions may be related to the angle and velocity of birds’ flight. We deployed a home security camera system to passively record interactions between common North American bird species and residential windows in a backyard setting over spring, summer and fall seasons over 2 years. We captured 38 events including 29 collisions and nine near-misses in which birds approached the glass but avoided impact. Only two of the collisions resulted in immediate fatality, while 23 birds flew away immediately following impact. Birds approached the glass at variable flight speeds and from a wide range of angles, suggesting that the dynamic appearance of reflections on glass at different times of day may play a causal role in collision risk. Birds that approached the window at higher velocity were more likely to be immediately killed or stunned. Most collisions were not detected by the building occupants and, given that most birds flew away immediately, carcass surveys would only document a small fraction of window collisions. We discuss the implications of characterizing pre-collision behaviour for designing effective collision prevention methods. Collisions with plate glass on buildings, transportation shelters, noise barriers and fences are a major source of bird mortality, killing hundreds of millions of birds each year in North America and resulting in uncounted mortality worldwide . Most biafter-the-fact approach. Numerous field studies have involved monitoring or surveying buildings and documenting bird carcasses and injured birds at buildings, including combinations of residential homes, institutional, and mid-rise and high-rise buildings  and the angle of the objects’ initial velocity vector leading up to impact. A moving, soft object will experience more force in a perpendicular collision as all of the force is directed outward at that object, compared to a glancing collision where only a portion of the force is directed back toward the object. While there is little empirical information to indicate how much of the force generated by a collision is absorbed by the body of a bird, there is reason to expect that angle of a bird’s approach to the window as well as its flight velocity will influence the magnitude of force experienced upon impact and the resulting effects on the bird’s tissues.Previous research has linked the risk of lethal bird window collisions with environmental variables such as building characteristics, surrounding urbanization and time of day e.g., . Yet, ite.g., motion or audio detection). We used a commercial home security system in a residential backyard setting to passively record interactions between birds and window glass over 2 years. We analyzed footage to examine birds’ behaviour in the moments leading up to potential collisions as well as outcomes of collision events.To gain insight into how bird-window collisions occur at real buildings, we used passive video monitoring to record bird behaviour at windows. Because the behaviour of wild birds and the timing of potential collisions are unpredictable, observing collisions by passive video recording system requires a mechanism for detecting the onset of target events and filtering out noise  by a collision with a window, are related to the bird’s flight velocity and angle of approach to the window (see above). We predicted that higher flight speed, and angles of approach approximately perpendicular to the window, are more likely to result in injurious or lethal collisions. We also considered time of the day as a potential confounding factor as it can change the reflection of ambient light on the window.Our analyses aimed to test the hypotheses that collision risk and the risk of being killed or incapacitated (etc.) that are not well-understood. Therefore, we consider that our study can provide some novel insights for future testing despite the limited spatial representation.We filmed in the backyard of a residential property in London, Ontario, Canada from May to October in 2019 and in 2020. The property is located immediately adjacent to a mature eastern deciduous forest nested within a suburban neighbourhood. We expected there to be a relatively high abundance of resident and migratory bird species in study area based on the adjacent forest’s designation as Significant Wildlife Habitat by the Province of Ontario, results of long-term window building monitoring conducted within a few kilometers of the site, and the property owners’ observations over several years of feeding birds in the backyard. The property owners maintain a variety of bird feeders and bathing stations in the backyard that were included in the setup for the present study. We are aware that at a spatial level, we only gathered information from a single location, so our results and conclusions cannot be generalized to all window collisions contexts. However, we focused this study on the role of variables related to the behaviour of the animals . The cameras recorded in 1,080 p resolution at 24 frames per second. Fluctuations in wireless signal strength occasionally reduced the resolution to 720 p. We positioned three of the cameras along the back wall of the house immediately adjacent to three sets of large glass sliding doors . We usede.g., a bird passing in front of the lens) and audio detection . The sensitivity of each type of trigger was adjusted within each camera to optimize the signal-to-noise ratio and reduce the frequency of recordings being triggered by unrelated environmental events such as falling leaves, background noise or sudden changes in ambient light. Audio detection sensitivity was tested by simulating the sound of a collision by clapping hands in front of the windows. The audio trigger sensitivity of three cameras was set to three or medium; the camera placed on the tripod had audio triggers disabled to avoid detecting sounds produced by birds at the feeders. Motion sensitivity on all four cameras was set to “7” or medium-high. The cameras uploaded footage directly to a cloud server. In addition to the motion and audio-triggered recordings, we programmed two of the cameras to record video on a continuous rolling basis to provide a failsafe in case events failed to trigger the cameras.The cameras were triggered synchronously to record video of a fixed duration  using a combination of infrared motion detection . The frames included in the flight analyses ended either when the bird impacted a window or at the frame when the bird was closest to the window. We estimated the duration of each flight by multiplying the total number of video frames included in the analysis by 0.042 s (the duration between frames at the 24 fps recording setting used by the cameras). We estimated the distance covered by the flight by marking the change in the position of the bird across consecutive video frames and comparing the difference relative to known dimensions of the study area. For each event that was included in the analyses, video clips recorded by multiple cameras  was examined by the rater in combination to reduce bias in visual approximation of changes in the birds’ position over time. The distance traversed by a bird between consecutive video frames was noted in a spreadsheet . We then calculated average velocity within each flight as the total distance covered in flight divided by the total flight duration. We compared changes in the position of the bird between video frames with dimensions of the backyard to produce an overhead map showing the approximate flight paths followed by birds as they approached the windows  for each flight path relative to the normal angle from the window  to the nearest 5° angle. To include horizontal angle of approach in our statistical analyses, we used the absolute value of the estimated angle from perpendicular thus controlling for direction .A single rater analyzed the video clips using a standardized protocol in VLC Media Player . All video clips that were saved throughout the study period were processed together in one batch. For each saved video clip, events were classified as either a collision or near-miss. An event was coded as a collision if the bird made physical contact with the glass. We excluded any events in which birds contacted the glass repeatedly at close range, which might indicate they were attacking their reflections on the glass rather than colliding. An event was coded as a near miss if the bird initially appeared to fly along a path that would lead to it colliding with a window, but suddenly changed its trajectory (reducing velocity or sharply shifting direction) within 2 m of approaching the building, and subsequently did not make impact. A change in trajectory preceding a near miss was determined by drawing a straight line along the initial flight path of the bird and confirming that the line projected toward a window, then comparing the bird’s observed displacement across subsequent frames. Examples of collision and near miss events are provided in the dows see . We visui.e., the same individual birds approaching the windows multiple times over the duration of the study). The probability of collision when birds were on a collision approach included two values: 0, no collision (near miss), and 1, collision. The probability of visible injury considered only birds that collided with the window, including two values: 0, animals flew away immediately, and 1, animals were stunned  or died right after the collision. We used the absolute difference value of horizontal angle of approach relative to the perpendicular, ignoring left or right direction. We excluded vertical angle of approach as most birds flew approximately parallel to the ground and our camera set up did not allow us to quantify vertical angle accurately. Time of the day was transformed into decimals. None of our independent continuous factors showed any significant correlation between them . We focused the results on assessing whether the independent factors were significantly (P < 0.05) accounting for the variation in the dependent variables as well as presenting their effect size . Effect sizes were expressed in the odds ratio scale  and all animal handling procedures were approved by the institution’s Animal Care Committee (protocol 2019-118).In total, we recorded 38 events comprising 29 collisions, in which a bird contacted the glass, and nine near-miss events, in which birds approached the glass but appeared to change flight trajectory to avoid it. We excluded two recorded collisions from the flight velocity analysis because it seemed likely that the birds were attacking their reflection on the glass and not attempting to fly through; one additional recording (#19) was excluded because it did not capture enough footage before impact to be useful for measuring changes in the position of the bird. At least nine other recorded events in which a bird flew close to the window, but did not contact the glass nor appeared to be on a collision course, were excluded from the analyses. The flight paths taken by each bird shown as collisions and near-miss events are provided in the n = 26), mean flight velocity leading up to impact was 7.24 m/s, median = 7.02 m/s, SD = 2.45 m/s, range 2.31 to 12.25 m/s; mean absolute angle of approach was 16.53° from perpendicular to the window, median = 10.00°, SD = 19.45°, range 0° to 45°; and mean time of the day  was 12.39 h, median = 11.46 h, SD = 4.32 h, range 2.47 to 20.18 h.Of the recorded collision events, two collisions resulted in an immediate fatality  as captured by the cameras and confirmed by the building occupants. Three birds were stunned and grounded upon impact but flew away within a few minutes. The cameras stopped recording within seconds following impact, but the homeowner confirmed observing when birds recuperated and flew away minutes later, out of the recording range for the videos. The other 24 collision events resulted in birds immediately flying out of the frame; the outcome for those birds could not be determined. For analyzed collision events , angle of approach , or by time of the day . Of the three variables, velocity of the approach had the highest effect size, with an increase in 1 m/s speed leading to a 5.37 increase in the odds of a collision. However, the effects of velocity were non-linear as shown by probability of collision . We found that the probability of a collision leading to a visible injury was significantly affected by velocity  and angle of approach  but not by time of the day . The statistical results should be interpreted with care because the model yielded a singular solution. Again, velocity of the approach had the highest effect size, with an increase in 1 m/s speed leading to a 12.8 increase in the odds of a visible injury. The probability of visible injury showed it highest rate of increase at speeds above approximately 7 m/s from our visual assessment of the output .Velocity of approach yielded the highest effect size of the independent variables studied. The probabilities of collision increased with velocity faster in the range of 2 to 9 m/s and then leveled off. Additionally, birds flying faster than 7 m/s faced a higher probability of suffering a visible injury. However, our study involved a small sample size, especially for video recordings of birds that were stunned or killed by a collision and for birds that nearly avoided making impact, so the importance of the observed difference in flight velocity should be interpreted with caution. Our results support the idea that the flight velocity and angle at which a bird makes impact with a window may influence the odds of a collision as well as the outcome of a collision  strikes a harder object , most of the energy available for producing post-collision  velocity is stored in the soft object. Part of that energy pushes the bird outward from the window , while the rest of the force is absorbed by the bird’s body, compressing its tissues, and potentially causing injuries such as ruptures of blood vessels and broken bones. In theory, the ratio between these two components of force acting on the bird post-collision may vary with the duration of the impact, resulting from the extent that the bird’s body compresses. The angle at which a bird approaches the window may also affect the amount and direction of force its body experiences upon impact. A collision at a perpendicular angle to the window  will result in momentum being conserved along only one axis. In other words, the momentum of the bird as it flies forward into the window will be transferred and redirected outward at the bird. However, a collision at an oblique angle, either in the horizontal or vertical plane, would result in components of momentum being conserved along multiple axes independently , it is possible that events involving smaller birds may be less likely to be picked up by the cameras. To limit detection bias, future studies using cameras for monitoring bird collisions should conduct preliminary trials to calibrate the camera trigger sensitivity.Recording bird-window interactions passively in the field allows for behavioural observations that might otherwise require prohibitively invasive methodology with captive animals. In the present study, we captured nine near-miss events in which birds approached but did not collide with the windows. The total number of near-miss events was probably higher, but some events did not trigger video recording because the camera lenses and sensors were not oriented to observe birds several metres from the glass where detection may first occur. Future studies aiming to record near-misses more effectively could deploy more cameras oriented outwards, or combine cameras with other positional sensors. Given that most of the collision and near-miss events that produced triggers and were successfully recorded involved medium to large passerines  = bird collided with the upper transom windows above the door, (GD) = bird collided with the glass door, (S) = bird collided with the screen over a glass door.Click here for additional data file.10.7717/peerj.14604/supp-2Supplemental Information 2Colours are used to distinguish individual birds. Events are ordered chronologically by date. In the legend, (U) = bird approached the upper transom windows above the door, (GD) = bird approached the glass door, (S) = bird approached the screen over glass door.Click here for additional data file.10.7717/peerj.14604/supp-3Supplemental Information 3.042 s (duration between frames at 24 fps) to determine momentary velocity. Take the mean velocity across frames up to the point when the bird was nearest to the window. Note that images shown here are composites of video frames provided for illustration purposes; birds in motion are more clearly visible in video footage.[b]A, B and C show overlaid video frames from three cameras of the same collision event, a Northern Cardinal #15 in . D, E, aClick here for additional data file.10.7717/peerj.14604/supp-4Supplemental Information 4Each row in the dataset represents a single collision or near miss event. Blank cells indicate variables of interest could not determined (see methods).Click here for additional data file.10.7717/peerj.14604/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.14604/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.14604/supp-7Supplemental Information 7Click here for additional data file.


The computer vision community has taken a keen interest in recent developments in activity recognition and classification in sports videos. Advancements in sports have a broadened the technical interest of the computer vision community to perform various types of research. Images and videos are the most frequently used components in computer vision. There are numerous models and methods that can be used to classify videos. At the same time, there no specific framework or model for classifying and identifying sports videos. Hence, we proposed a framework based on deep learning to classify sports videos with their appropriate class label. The framework is to perform sports video classification using two different benchmark datasets, UCF101 and the Sports1-M dataset. The objective of the framework is to help sports players and trainers to identify specific sports from the large data source, then analyze and perform well in the future. This framework takes sports video as an input and produces the class label as an output. In between, the framework has numerous intermediary processes. Preprocessing is the first step in the proposed framework, which includes frame extraction and noise reduction. Keyframe selection is carried out by candidate frame extraction and an enhanced threshold-based frame difference algorithm, which is the second step. The final step of the sports video classification framework is feature extraction and classification using CNN. The proposed framework result is compared with pretrained neural networks such as AlexNet and GoogleNet, and then the results are also compared. Three different evaluation metrics are used to measure the accuracy and performance of the framework. The processing of images and videos is the core focus of computer vision research. A dynamic area of computer vision is video classification (VC) . Due to In recent years, a video categorization task has shown tremendous success. This study gives a very detailed and technical strategy for sports video classification in order to acknowledge the significance of the video classification task and to highlight the accomplishments of deep learning models for this work. Generally, people all over the world generate and handle huge amounts of video and share it through social media like Facebook, WhatsApp, Signal, and so on. Currently, on YouTube alone, over one billion hours of video are being watched by different people every single day . BusinesThe ultimate aim of this work is to classify sports videos based on their content using the proposed model. This model begins with preprocessing the input sports video. Preprocessing includes both frame extraction and noise reduction. Then, the keyframes are extracted using the proposed enhanced key-frame extraction algorithm. Finally, keyframes are given to CNN to extract feature sets based on its trained knowledge of CNN. Given the input, sports video is categorized as a specific class at last.Frame extraction and noise reduction are carried out in preprocessing.keyframe selection technique is used to extract key-frames.The framework classifying sports videos are built using convolutional neural networks and deep learning techniques.Run the framework using the test data.This work's primary contribution is as follows:The efficiency of the suggested technique is then confirmed by comparing the findings to an existing model. The suggested framework for classifying sports video is novel in that it uses a specially designed keyframe selection method, a fuzzy adaptive window-based mean filter (FAWMF) to eliminate noise, and hyper-parameters that are adjusted depending on the two datasets stated above. There are many frameworks or models available for categorizing videos in general. As an illustration, consider the classification of moving objects, the recognition of human movement, action recognition, etc. They are mentioned in the literature review section in detail. Recent advancements in deep learning models have shown how effective these methods can be at categorizing videos. However, the majority of the popular deep learning models for video classification have been mostly adapted from those in the image/speech domains. Existing works are stated with the name of the model, optimization techniques or algorithms, a dataset, and the outcome of the model. The framework proposed in this research is specifically for sports video classification and also delivers outstanding performance and good accuracy, and the results are compared with the existing work.The rest of this paper is organized as follows: video classifications and literature reviews are included in Atiqur Rehman and Samir Brahim Belhaouari explored a review of video classification in different categories of approaches, like hand-crafted approach and 2D-CNNs. 3D-CNNs, spatiotemporal convolutional networks, and recurrent spatial networks . MoumitaYunjun Xu et al. presented the event matching method is used to match the convolutional neural network output to complete the sports training video classification . Hana etMachine learning is a subset of artificial intelligence, while deep learning is a subset of machine learning. It is a very important element of data science and certainly includes statistical approaches and predictive models. Data scientists are mostly preferred for deep learning architecture for the task of collecting the data, analyzing the data, and also interpreting a very large amount of data. The biggest advantage of using deep learning is that it makes the above process smarter, faster, and easier. DL is a class of algorithms and topologies, not a single method, which can be used to solve a variety of problems. Many architectures and algorithms are used in deep learning. Generally, deep learning architecture is classified into two categories. There is supervised and unsupervised learning. LSTM and CNN fall under supervised learning. These two are the oldest approaches and most widely used architectures in various applications. In this paper, we experimented with the pretrained networks AlexNet and GoogleNet, and the results are compared with the proposed model.CNN is a class of ANN in deep learning. It is especially useful for analyzing visual content like images and videos. AlexNet, DesnseNet, GoogleNet, LeNet, ResNet, and VGGNet are predefined and widely used architectures for image and video content analysis . The CNNCNN can be partitioned into different types of layers, and each layer performs various missions. Among these layers, one of the most important is the convolutional layer. It handles feature extraction with the support of convolution maps. Then, the remaining layers are the input layer, the ReLU layer, the pooling layer, and the fully connected layer.The working principle of the input layer in CNN is similar to the way we use it to give input for a model. The only difference is that it takes three-dimensional values. The height and width of the layer represent the horizontal and vertical pixels of the image, respectively, while the depth represents the RGB color channel values.Activation map sizeFilter sizeStridePaddingThe major component of CNN is the convolution layer. This layer is where the convolution occurs, which means the layer tries to find features in the input like images or frames in the case of videos. Frames and images are constant, which means that one component of the image is created similarly to all other components . As a reMore convolution layers in a CNN are also possible. When we need high-level features, we need to use more than one convolution layer. In the first layer, the network could detect simple edges, and then in the next layer, those edges could be filtered into simple shapes, and so on. Formal convolution layer activity is shown in In general, the ReLU layer is useful for activation functions. It helps the network maintain the minimum computational cost.Subsampling or downsampling is nothing but a pooling layer. The pooling layer is a mediator between two consecutive convolution layers. The method of downsampling of an image is well-known as pooling. The convolution layer (CL) output is subsampled for a single output using a small amount of it as an input . PopularThis layer is the only layer that is fully connected to the previous layer, and it is the last layer in CNN. It classifies the feature data extracted and downsampled in previous layers. It takes feedback from the previous layer and produces output.UCF101 is a dataset of realistic action videos with 101 action categories that were gathered from YouTube. The UCF50 dataset has been expanded to create this data collection. There are 50 activities in UCF50.13320 videos from 101 activity categories are included in the UCF101 data collection. With huge differences in camera movements, object appearance and position, object scale, viewpoint, cluttered backgrounds, illumination conditions, and other factors, UCF101 provides the most diverse range of sports in terms of actions. UCF101's major purpose is to promote more action recognition research by learning and exploring new realistic action categories.The Sports-1M dataset contains over a million YouTube videos. The collection contains over a million videos, divided into 487 sports-related categorise with 1,000 to 3,000 videos each. By examining the text metadata connected with the videos, the YouTube Topics API is used to automatically categories the videos into 487 sports classes.A video is a three-dimensional signal in which the horizontal axis corresponds to the frame width and the vertical axis corresponds to the frame height; the third axis depicts the evolution of frame content over time. Preprocessing is the very first step for every research and its implementations. Hence, sports video classification also begins with preprocessing. It involves a process known as frame extraction, which entails converting the given sports video into frames. As mentioned in section, UCF101 has 101 different categories of sports videos with sports names as folder names.SV1 is sports video category1, and F1, F2, F3,…, F101 are various folders for different sports categories. Each and every folder has many types of videos with different styles.In , SV1 is Vi represents each video from the dataset. fi is the number of frames in a video, which are indexed from 1 to n. n is nothing but the number of frames. There are a large number of sports video datasets available on social media and other sources. But very few datasets are considered standard. In In this model, frame extraction is one of the major courses of action in video preprocessing. A video is a collection of pictures that are taken and then shown repeatedly. However, a single video frame, or image, is obtained by pausing the sequence at a particular frame. The mathematical approach for frame extraction is mentioned in equation . Frame cWhen video signals are acquired, transmitted, and received, noise is a significant element that can significantly reduce the quality of the signals . We may In this paper, a fuzzy adaptive window-based Mean Filter (FAWMF) is used for preprocessing the sports video after the frames are extracted. The pixels are categorized as “noise” based on how each pixel in the picture is compared to its neighbors. After the noise intelligence test, these pixels are then replaced by the value of mean pixel value in accordance with their neighbors. FAWMF improves the quality of video frames and removes impulse noise.w, where i and j = 5. Then, travel the filter matrix over the video frame f and w must go along with the current frame position . Apply the product term to each frame element using the corresponding filter coefficient w. Find average of sum of products. Finally, the current position value is replaced by an average value.Keyframe selection is one of the most crucial and significant works in the video classification model since the processing of all the frames in a video will boost both time and space complexity, which may degrade the performance of the model. A mathematical formula for candidate frame selection is given as follows:Once candidate frames are extracted, an apply enhanced threshold-based frame difference algorithm for keyframe extraction. The The convolutional layer is the most important element of the convolutional neural network (CNN). We applied CNN to classify objects in all the frames from the sports video dataset into various classes. We trained over 1,800 videos in the UCF101 dataset, which are identified with 91% accuracy. The working model of CNN is shown in For the above purpose, we apply ReLU, which initializes all of the negative values in a frame that is a two-dimensional matrix with zero. The value “zero” means the specific picture element has no value. The maximum value from the matrix is obtained using the max-pooling layer. The softmax algorithm is then used to assign decimal probabilities to all classes using the output of the fully linked layer. Experiments were performed on the UCF101 and sports1M datasets for sports video classification. F1-Score, precision, and recall are frequently used evaluation indices based on multilabel classification  [30 [3029] [