The aim of the present study is to compare the short- and long-term effects of video-gaming by using the same measurements. More precisely, habitual and occasional video-gamers were compared so as to analyze the long-term effects. An ABABABA design was used to analyze the short-term effects. The first A refers to baseline measurements: Visual RT, Auditory RT, Aim trainer RT, Go/No-Go RT and N-Back RT. The first B refers to 30 min of gaming, the second A refers to the measurements used in the baseline, the second B refers to 60 min of a video game, the third A refers to the same measurements used in the baseline, the third B refers to a 30-min rest, and finally, the fourth A refers to the measurements used in the baseline. Seventy participants, twenty-nine habitual video-gamers and forty-one occasional video-gamers, participated in the study. The results showed a temporary improvement of cognitive functions  in the short term and a strong enhancement of cognitive functions in the long term. The results are discussed in light of Flow Theory and the automatization process. Contribution of the study: The contribution of this research is to highlight that despite there being a transient enhancement of executive and cognitive functions through the use of mobile video games in the short-term period, with a decrease of performance after a 30-min rest, there is a strong increase of cognitive performance in the long-term period. Flow Theory and the automatization process together can explain this apparent inconsistency between the positive increase of long-term performance and the transient increase of short-term performance. One limitation of the present research is that it is not possible to distinguish whether the long-term enhancements can be attributed either to continued practice in the use of video games compared to non-gamers, or to the possibility that gamers are already predisposed to video game playing. Future research should address this issue.  From a cognitive point of view, it is important to study both the long-term impact of video game playing and the short-term impact through intervention studies. To study the long-term impact, normally the cognitive profile of habitual and occasional video game players is analyzed. To study the short-term impact, the possibility of causally inducing changes in cognition via playing action video games is analyzed. Meta-analytical data converge to consistently demonstrate that video game players outperform non-video game players in a wide range of cognitive tasks ,3,4,5,6.With reference to the second issue, the possible short-term impact of causally inducing changes in cognition via playing action video games, recently, Kozhevnikov, Li, Wong, Obana and Amihai  focused Studies on the short-term effect of video games provide us with some information, but there is a lack of consistent evidence that long-term exposure to video games improves performance on non-game measures . Since vTo analyze the short-term effects of video games, we exposed both habitual and occasional video game players to repeated measurements (ABABABA) of cognitive performance after video-gaming  and after a 30-min rest to understand if the short-term empowerment obtained from the previous phases persisted.To analyze the long-term effects of video game exposure, we decided to compare habitual video game players  with occasional video game players (who play once or twice per week). The underlying logic is that by repeating the same activity, as happens for habitual players, some processes can become automatized and actual performance can show this long-term automatization.The subjects who took part in the research come from different areas of Italy: from both the north and south, and 70 subjects  between the ages of 20 and 32 participated in the research. The average age of the subjects was 25 years old (SD = 6.22). Participants were recruited through social networks and voluntarily participated in the study. All subjects are of Italian nationality.Twenty-nine healthy right-handed participants were habitual video-gamers , while forty-one healthy right-handed participants were occasional video-gamers . Of the habitual video-gamer participants, 15.8% spend more than 5 h playing video games each day, 57.9% spend between 4 and 5 h and the remaining 23.6% spend 2–3 h each day. Gamers have been playing on average for about 10 years. Of the occasional video-gamers, 72.7% spend between 0 and 1 h per week, and 27.3% spend between 1 and 2 h per week. The subjects declared that the devices most often used for gaming were the Smartphone (61%), consoles (Sony’s Play Station and Microsoft’s Xbox) (22%), PC Gaming (12.2%), and finally, with a tablet (4.9%). All data were collected in compliance with Italian legislation on the protection of personal data, thus respecting and safeguarding the privacy and anonymity of the participants (Legislative Decree 196/2003), and Art. 9 of the Deontological Code of Italian Psychologists. Informed consent was obtained from all participants, and they were also asked to sign a form for the processing of personal data after full explanation of the procedure. Participation in the experiment was voluntary. The procedure used is described in https://new.cognitivefun.net/ accessed on 10 November 2021). During the visual search task, in the center of the white screen background, the participant was presented with a fixation cross with a red dot in the center that was followed after variable time intervals by a target stimulus, that is, a green circle. The subject was asked to concentrate on the fixation cross and press the “space bar” key as soon as possible once the green circle (target stimulus) appeared on the screen [The test was performed using the Cognitive Fun! Site  was heard from the audio source .The AIM Trainer test consists of hitting a moving target in the shortest time possible. It is a tool used above all to train competitive players in being able to “catch” and “hit” the target in the shortest possible time. The test was performed using the Human Benchmark site. During AIM Trainer, the subject had to visualize a moving target in the center of the screen, lock it with the mouse and hit it as quickly as possible with each movement. The test ended when the subject hit all 30 targets.The two-choice procedure and the Go/No-Go procedure use RT and accuracy as dependent variables. The two-choice task is the most widely used of all RT-based procedures within the field of cognitive psychology ,30.The test was performed using the Cognitive Fun! Site. In this test, there is a stimulus that must be answered and an inhibitory stimulus that must not be responded to. The test consists of 15 trials. Each test began with the presentation of a red dot in the center of a white background which had to be fixed for about 4 s. The request was to click the space bar when a solid green dot was displayed and ignore the dot with different shades of green and with a segmented texture. During the procedure, only the wrong answer came with a red cross. https://new.cognitivefun.net/website (accessed on 25 April 2020) was available for 175 s for each of the two tests. When introducing the auditory n-back test, it was explained to the participants that it was necessary to proceed similarly to the previous activity, but instead of the 50 images, they would hear a sequence of 50 sounds; then, they had to click the space bar when they heard the specific sound of two positions before. The average completion time for participants was from 120 to 175 s for the visual n-back and from 112 to 175 for auditory n-back tests.In order to evaluate the performance of working memory (WM), n-back was used, a continuous performance activity commonly adopted in the field of psychology and cognitive neuroscience. This paradigm has been widely used in the literature and has shown good psychometric properties ,32,33. T2) was used, and for the t-test, Cohen’s d Effect Size was used. The Greenhouse–Geisser adjustment for non-sphericity was applied to probability values for repeated measures.Data were analyzed using SPSS Version 24.0 for Mac. Measurement parameters varied according to the type of task, means of correct responses (CR) and means of correct reaction times (RT) obtained for each participant, for each experimental condition. The parameters were submitted to 2 × 2 × 4 mixed analysis of variances with Group  and Video-gamer  as between-subjects factors, and Phase  as within-subject factors. Descriptive statistics of the dependent variables were tabulated and examined. RT data referring to correct replies were cleaned, and outliers were removed . Alpha level was set to 0.05 for all statistical tests. In the case of significant effects, the effect size of the test was reported. For ANOVA, partial eta-squared  of cognitive performance to understand if the short-term empowerment obtained in the first three A phases persisted after resting.p < 0.004, pη2 = 0.08. The Group X Phase interaction was also statistically significant, F  = 3.78, p < 0.01, pη2 = 0.08. The RT of the control group exhibited a stable performance over time, while the experimental group decreased RT after video-gaming and increased RT after the break phase. These data may indicate that there is a short-term activation induced by exposure to video games, however this activation is not maintained over time after the subject has finished the break phase  = 2.63, p < 0.01, d = 0.77, and t (40) = 4.16, p < 0.0001, d = 0.99, but it was not statically different compared to phase 4, t (40) = 1.15, p < 0.09, d = 0.55. Phase 2 was not statistically significant compared to phases 3 and 4. Comparing phase 3 and phase 4, the t-test was again significant, t (29) = 3.30, p < 0.002, d = 0.74. With reference to the Auditory Search task, RT was considered, and Post-hoc analysis with paired p < 0.04, pη2 = 0.08, and the RT of the control group exhibited a stable performance over time, while the experimental group decreased RT after video-gaming and increased RT after the break phase. These data may indicate, again, that there is a short-term activation induced by exposure to video games, however this activation is not maintained over time after the subject has finished the break phase  = 3.44, p < 0.01, pη2 = 0.068. With regard to the Working Memory N-Back test, As can be seen in t-test showed that phase 1 was statistically different compared to phase 2 and phase 3, respectively, t (29) = 3.97, p < 0.0001, d = 0.77, and t (29) = 5.57, p < 0.0001, d = 0.99, but it was not statistically different compared to phase 4, t (40) = 1.15, p < 0.09, d = 0.55. Phase 2 was not statistically significant compared to phases 3 and 4. Comparing phase 3 and phase 4, the t-test was again not significant. These data confirm that there was a significant decrease in RT in the N-Back Working Memory test already after playing for half an hour, and this decrease was stable. Post-hoc analysis with the paired p < 0.0001, pη2 = 0.09. This means that habitual video-gamers have faster reaction times in Visual Search than occasional video-gamers. With reference to the Auditory Search task, the factor Video-gamer again showed significant effects, F  = 13.05, p < 0.001, pη2 = 0.11. This again means that habitual video-gamers have faster reaction times in Auditory Search than occasional video-gamers. With regard to the Aim Trainer test, the factor Video-gamer showed significant effects, F  = 6.2, p < 0.01, pη2 = 0.09. This means that habitual video-gamers have faster reaction times in the Aim Trainer task than occasional video-gamers, with this also being one of the basic activities in the interaction of shooter video games, such as the one used in this work. Finally, with reference to the Go/No Go task and n-back task, the factor Video-gamer showed significant effects, respectively, F  = 9.39, p < 0.003, pη2 = 0.082, and F  = 12.92, p < 0.0001, pη2 = 0.075. This means that even with reference to more complex cognitive processes, video-gamers tend to have better performances than occasional video-gamers. With reference to the second question, the long-term effects of video games, we compared habitual video game players with occasional video game players. With reference to the Visual Search task, the factor Video-gamer  showed significant effects, F  = 18.42, With this study, we aimed to shed light on the association between the long- and short-term effects of video gaming, examined in the same experiment and with the same measurements. Data from the present research show that there is enhancement of attention and executive functions in the short term for gamers and non-gamers of the experimental group. These findings are in line with those of prior works ,19,20,21With reference to the second question posed, the long-term effect, the results of the present research show that gamers are better in terms of performance in all cognitive tests than non-gamers, and these findings are in line with those of prior works .Now, the open question is: how do the results that appear to be transient show strong effects on long-term performances ? It may be that habitual video-gamers are working on their routine exercises ,42,43,44The results of the present study are two-fold: they demonstrate that there is an enhancement of cognitive functions through the use of mobile video games in the short term, and this implies the existence of temporary cognitive states in which auditory and visual aspects of attention are dramatically enhanced for limited durations. This is similar to what happens in flow experiences. With reference to the long-term effect, habitual video-gamers show better cognitive performances than occasional video-gamers. In summary, the present work documented two pathways for cognitive training: the first acts in the short term and temporarily amplifies cognitive performances, whereas the second takes place in the long term and strongly increments cognitive performances. One limitation of the present research is that it is not possible to distinguish from the results if the enhancements of cognitive performances can be attributed either to a lasting improvement due to continued practice in the use of video games compared to non-gamers, or to the possibility that gamers are already predisposed  and thus more likely to be activated. Consequently, when subjected for the first time to the video game world, they become gamers who are capable of better basic performance than those who do not have these characteristics, and this could create self-reinforcement that works as a virtuous circle that in turn makes further progress possible. This first limitation could be a starting point to be explored for expanding this research, to then study what the variables are that identify gamers and their ability to develop better skills of executive functions than other non-gaming subjects. Future research should examine groups of video-gamers with different years of experience  to identify when the cognitive abilities show these enrichments. The complete range of attentional capacities might also be analyzed through diverse attention tasks: general executive and distributed attention tasks. Assessing attention might further examine certain aspects in detail, for instance, how long empowerment lasts. Furthermore, factors also need to be considered in detail, including baseline attentional capacities, perception of challenge during the activity and overall experience of the activity. Finally, it would be interesting to understand how to best reach efficient learning levels and, since arousal seems to be a critical prerequisite for reaching enhanced cognitive states, future studies should examine the physiological measures of arousal and how to heighten it. Since Looi et al.  have shoFinally, we found several works that addressed issues such as motivation and preference related to video games, showing that younger players prefer action games, and older players games of skill ,46,47. MApart from theoretical findings, there were also four practical findings. Firstly, video-gaming is put forward as a way for neuroscientists and cognitive psychologists to examine enhanced states. Secondly, the implications for the general population could be very interesting. Albeit brief, focused attention, no matter how short, could be of benefit in critical moments. Thirdly, the possibility that creativity may be associated with an enhanced cognitive state  is an inUndoubtedly, enhanced states need further investigation, along with how to stimulate them. Retrieving dormant cognitive capacities and enhancing them at will could thus be greatly increased.


Brain AVMs are rare but serious vascular lesions that often pose a management dilemma between the risk of various treatment modalities and uncertain natural history during observation. We describe preliminary data on the use of focused ultrasound as a novel therapeutic strategy. In an AVM model, one session of ultrasound gradually reduced flow through the lesion without inducing rupture. Due to its non-invasive yet immediate ablative effects, focused ultrasound may allow safer treatment of AVMs. However, further studies are needed to clarify its efficacy and side effect profile. The current treatment of brain arteriovenous malformations (AVM) consists of surgical resection, stereotactic radiosurgery (SRS) and/or endovascular embolization. A specific set of advantages and limitations applies to each approach or combination thereof, depending on the angioarchitecture and other patient factors . High inHIFU induces tissue ablation in a rapid, non-invasive, and non-ionizing fashion that may overcome some limitations of established brain AVM treatments by transmitting sound waves through the intact cranium onto a targeted volume of interest. Surgical resection is predicated on access to the malformation, which is untenable in deeply located lesions such as the thalamus. Location is less problematic in SRS, although ionizing radiation often leads to perilesional leukoencephalopathy and radiation necrosis. The cognitive effects of radiation in pediatric patients significantly limit its utilization. Furthermore, the year's long delay from treatment to obliterative vasculopathy prolongs hemorrhage risk, which persists until complete occlusion of the shunt. Endovascular embolization rarely produces complete AVM occlusion and rather serves a more adjunctive role to surgery and/or SRS in larger or acutely ruptured lesions.With the limitations of conventional therapies in mind, we sought to assess whether HIFU ablation is feasible in a swine model of brain AVM.The study was approved by the local Animal Research Council (ARC) prior to study inception. The porcine AVM model was surgically created, as described previously , in a juBaseline MRI was performed, including T1W MPRAGE, FSE T2, and T1W contrast enhanced time-resolved MRA (CE-MRA), on a 3 Tesla MR scanner . CE-MRA was obtained after intravenous injection of 0.5 mmol gadobutrol  diluted in normal saline with 1.18 s/frame and a total duration of 30.68 s. The rete mirabile was identified on anatomic sequences and high flow shunting confirmed by CE-MRA. An MR Thermographic sequence (single slice spoiled gradient echo) was initially performed through the rete, however, it was found to be non-diagnostic because of susceptibility artifact from the skull base. HIFU trajectory was planned through a ventral approach to the skull base where an acoustic window to the rete was identified . The bilPost procedure imaging was obtained with T2 and CE-MRA sequences. At the conclusion of the study, a lethal dose of anesthetic was administered intravenously per University veterinary protocol.The rete mirabile was clearly visualized on pre-procedure imaging . CE-MRA p < 0.01 for Spearman r). Post-ablation T2 weighted MRI and necroscopy demonstrated thermal injury to the facial soft tissues along the ultrasound trajectory.Following sequential HIFU ablation, CE-MRA showed progressive flow reduction through the rete shunt as evidenced by increasing mean transit time and time to peak, as well as decreasing blood flow . The finDespite advancements in the treatment of brain AVM over the past decades, many lesions still pose a challenge to safe and complete obliteration. Treatment related complications have in fact been shown to result in worse outcomes than the natural history, at least on short term follow-up . From thIn contrast to applications in functional neurosurgery and neuro-oncology , 6 HIFU in vivo AVM model for SRS (To this background, we describe preliminary experience applying HIFU to a brain AVM model. The swine carotid rete is an established  for SRS  that recHowever, several challenges were evidenced by incomplete obliteration of the nidus and thermal soft tissue injury. First, the rete mirabile is difficult to insonate due to adjacent bone and air containing structures. Unfortunately, wide-array transducers currently in use for human brain HIFU applications are not amenable to the rete trajectory. We have since developed a practice of percutaneously accessing the tympanic cavities and infusing saline to widen the acoustic window . AdditioFurther studies are needed to continue development of HIFU for brain AVM treatment and to assess long term effects. The current swine model will aid this development, albeit with several limitations which must be improved upon. Heat deposition by the ultrasound beam could potentially result in tissue damage along the treatment path. In addition, serious complications could result if vital blood vessels are inadvertently damaged during treatment. Unlike current neurological HIFU applications , this moThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by UCLA Animal Research Council.JJ conceived the experiments, supervised the HIFU procedure, and edited the manuscript. MB analyzed the experimental data, created figures, and edited the manuscript. HU primarily wrote the manuscript. NK performed the rete anastomosis surgery and edited the manuscript. ST oversaw project design and implementation, assisted with grant funding, and edited the manuscript. All authors contributed to the article and approved the submitted version.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.


Delirium is one of the most common complications in older surgical patients. Although previous studies reported that preoperative malnutrition was related with postoperative delirium (POD), there was lack of evidence to illustrate the relationship between malnutrition and emergency delirium (ED). The objective of this study was to investigate the relationship between preoperative malnutrition and ED in older patients undergoing noncardiac surgery.The study was carried out in accordance with STROBE guidelines. This was a secondary analysis of a prospective cohort study. Older patients (65–90 years) who underwent noncardiac surgery under general anesthesia were enrolled in Peking University First Hospital.915 patients were enrolled. The incidence of malnutrition was 53.6 % (490/915). The incidence of emergency delirium was 41.8 % (205/490) in malnutrition group and 31.5 % (134/425) in control group, P < 0.001. After adjusting confounding factors , duration of surgery, pain score, low body temperature and allogeneic blood transfusion), malnutrition was not associated with increased risk of emergency delirium .Malnutrition was common in older patients undergoing non-cardiac surgery, but it’s not related with emergence delirium after adjusted for confounders.http://www.chictr.org.cn) .Chinese Clinical Trial Registry (The online version contains supplementary material available at 10.1186/s12877-021-02270-2. Malnutrition has been considered as a great challenge to patient’s safety in perioperative settings , 2. It iDelirium is one of the most common complications in older surgical patients. It is manifested as acute disturbance of attention and cognitive function, abnormal sleep cycle and disorganized thinking . AccordiAlthough previous studies reported that preoperative malnutrition was related with POD, there was lack of evidence to illustrate the relationship between malnutrition and ED. Two cohort studies reported that preoperative malnutrition was associated with increased risk of postoperative delirium in patients undergoing on pump coronary artery bypass grafting , 14. TheThis study was designed to investigate the relationship between preoperative malnutrition and emergence delirium in older patients after non-cardiac surgery.Present study was secondary analysis of a prospective observational study which was approved by the Clinical Research Ethics Committee of Peking University First Hospital on August 4, 2017  and registered with Chinese Clinical Trial Registry on September 19, 2017  [, BeijingOlder patients (aged 65–90 years) were included if they were scheduled to undergo noncardiac surgery with an expected duration ≥ 2 h under general anesthesia. Patients who met any of the following criteria were excluded: (1) refused to participate in the study; (2) previous history of schizophrenia, epilepsy, Parkinson’s Disease, or myasthenia gravis; (3) unable to communicate due to severe dementia, comatose or language barrier; (4) traumatic brain injury or neurosurgery; (5) an American Society of Anesthesiologists (ASA) classification of IV or above; or (6) Emergency surgery.Preoperative malnutrition was defined as nutritional risk screening 2002 (NRS2002) ≥ 3. NRS 2002 contains two components: nutritional status and disease severity, giving a total score of 0–6 (Supplementary Table SEmergence delirium was defined as any episode of delirium during PACU stay and was assessed by the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) at 10 and 30 min after PACU admission, and before PACU discharge , 18.Before assessing delirium assessment, the level of sedation/agitation was evaluated with the Richmond Agitation Sedation Scale (RASS) . If the All investigators who were in charge of delirium assessment were trained to use the CAM-ICU by a psychiatrist . ConfusiAll patients received standard monitoring on arrival in the operating room including electrocardiogram, non-invasive blood pressure, pulse oxygen saturation, and urine output. During general anesthesia, end-tidal carbon dioxide and bispectral index (BIS) were monitored. Invasive arterial pressure and central venous pressure were used if considered as necessary.Anesthesia induction was completed by propofol and/or etomidate, opioids (sufentanil and/or remifentanil) and muscle relaxants (rocuronium or cisatracurium). Anesthesia maintenance was conducted by infusion of propofol and/or sevoflurane inhalation. Nitrous oxide could be used as supplementary in necessary. Opioids and muscle relaxants were administered when considered necessary. The target was to maintain bispectral index between 40 and 60.Muscle relaxants were stopped for at least 30 min before the end of surgery; propofol infusion and/or sevoflurane inhalation were decreased or stopped according to BIS monitoring; sufentanil was administered in necessary. At the end of surgery, Neostigmine (0.05 mg/kg) and atropine (0.02 mg/kg) were used to reverse residual effect of neuromuscular blockade. Patients were extubated when they met the following criteria: (1) easy to wake up; (2) sufficient reflexes that protect the airway; (3) adequate gas exchange ; and (4) acceptable hemodynamic status (systolic blood pressure ≥ 90 mmHg and heart rate ≤ 100 beats per minute).As a routine practice, patients were transferred to the PACU after extubation. Patients were monitored in PACU for at least 30 min and then transferred to the general ward when the Aldrete score was higher than 9. Pain severity was assessed with the numeric rating scale . Moderate to severe pain (NRS pain score > 3) was managed with intravenous opioids and/or non-steroid anti-inflammatory drugs (NSAIDs). Tympanic temperature was measured with an infrared ear thermometer. Patients with hypothermia (< 36 °C) were managed with warm air blanket.Primary outcome was to investigate the relationship between malnutrition and occurrence of emergence delirium.Baseline data included demographics, education, diagnosis, comorbidities, smoking, alcoholism, Charlson Comorbidity Index , and ASAPostoperative data during PACU stay included NRS pain score, tympanic temperature, and length of PACU stay. In the general wards, patients were followed up twice daily until the 5th day after surgery for the occurrence of delirium and non-delirium complications. From the 6th day after surgery, patients were followed up weekly until discharge. For those who were discharged from the hospital, follow-ups were performed by telephone interview.As a secondary analysis, we calculated the statistical power based on available data. The incidence of ED was 41.8 % (205/490) in malnutrition group and 31.5 % (134/425) in control group. Assuming significance at 0.05, this would yield a power of 0.90.Normality of continuous data was tested by Kolmogorov-Smirnov method in prior. Data with normal distribution were presented as mean ± standard deviations (SD) and differences between groups were compared by independent t sample. Data without normal distribution were presented as median (IQR) and differences between groups were compared by Mann-Whitney U test. Categorical data was presented by number (percentage) and differences between groups were compared by Chi-square test.The relationship between malnutrition and ED was firstly analyzed by univariate analysis, followed by multivariable logistic regression analysis adjusted for confounding factors including the baseline characteristics and perioperative variables that showed an imbalance between patients with and without ED .Two-sided P < 0.05 was considered as statistical significance. Statistical analysis was performed using SPSS 24 Inc. Chicago, IL, USA.From September 21, 2017 to April 10, 2019, 984 patients met with inclusion criteria. Finally, 915 patients were included with mean age of 71.6 ± 5.2 years, Fig. The mean age of patients with malnutrition was higher than that of patients in control group , Table According to NRS 2002, the incidence of malnutrition was about 53.6 % (490/915) in elderly patients undergoing non-cardiac surgery. Median score of NRS 2002 in malnutrition group was 3  whereas 2  in control group (P < 0.001).The incidence of ED in malnutrition group was 41.8 % 205/490) which was higher than 31.5 % (134/425) in control group (P = 0.001), Fig. 05/490 whIn univariate analysis, malnutrition was related with increased risk of ED , Table Present study found that malnutrition was common in elderly patients undergoing non-cardiac surgery but it’s not associated with occurrence of emergence delirium after adjusted for confounders.Perioperative malnutrition has raised up more concern because it’s highly related with poor patient’s outcome , 2, 23. CAM-ICU was employed to diagnose ED in present study. One strength of this study was that we assessed the phenotype of ED . It seemAlthough previous studies had reported the association between malnutrition and postoperative delirium, this study did not find the relationship between malnutrition and ED , 30, 31.Beyond our result, the association between malnutrition and ED and POD should be investigated by further studies. First, metabolic abnormalities (such as ω-fatty acids insufficiency and glutamate–glutamine cycle dysfunction) possibly increase the vulnerability of the brain . The vulMultivariable analysis showed that age, cognitive impairment, allogeneic blood transfusion, temperature < 36 °C at PACU admission and average NRS pain score in PCAU were related with increased risk of ED. This finding was in accordance with previous studies , 34, 35.The strengths of present study included a relatively large sample size and strictly assessed delirium. This study had two limitations. First, our result was generated from a single center study which might limit its generality. Second, this was a cohort study which could not answer the causal relationship between malnutrition and delirium. Third, we did not collect the information of long-term medications. Previous studies showed that medications  might be associated with delirium . As manyOur result showed that malnutrition was common in older patients undergoing non-cardiac surgery. The incidence of emergence delirium was higher in patients with malnutrition than control group. However, malnutrition was not related with emergence delirium after adjusted for confounders. Further studies are needed to elucidate the relationship between malnutrition and emergence delirium.Additional file 1:Table S1. Nutritional risk screening 2002 scale (NRS-2002).


Malnutrition and inadequate nutrient intake are associated with functional decline, frailty, and bad clinical outcomes among community-dwelling older adults. Despite the growing proportion of the elderly population in Indonesia, data on the prevalence of malnutrition and adequacy of macronutrient and micronutrient intakes among Indonesian older adults are scattered and vary between studies. Therefore, our study aims to obtain data on malnutrition prevalence, level and distribution of nutrient intakes, and prevalence of macronutrient and micronutrient inadequacies in Indonesian community-dwelling older adults.We carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in PROSPERO. A systematic electronic database search of MEDLINE, CENTRAL, EMBASE, ProQuest, HINARI, IMSEAR, GARUDA, and Indonesian Publication Index was undertaken. Additional searches were conducted in gray literature sources, hand-searching, retrospective searching, and personal communication with authors of the relevant publication. Observational studies presenting the malnutrition prevalence of habitual dietary intakes of older adults (60 years or older) were included. The risk of bias of studies was assessed using the Joanna Briggs Institute critical appraisal form. Sex-specific mean (and standard deviation) habitual macronutrient and a selection of micronutrients  intakes were extracted from each article to calculate the percentage of older people who were at risk for inadequate micronutrient intakes using a proxy of estimated average requirement (EAR) cut-point method, which is calculated from the national guideline of recommended dietary allowance (RDA). Prevalence of malnutrition, based on body mass index (BMI) categories and mini-nutritional assessment (MNA) criteria. and the population at risk of malnutrition were presented descriptively.Nine studies retrieved from electronic databases and gray literature were included in the pooled systematic analysis. According to BMI criteria, the underweight prevalence ranged from 8.0 to 26.6%. According to the MNA, the prevalence of malnutrition ranged from 2.1 to 14.6%, whereby the prevalence of at risk of malnutrition amounted to 18–78%. Our systematic review identified a high prevalence of nutrient inadequacies, most markedly for protein, calcium, vitamin D, and vitamin B12.We signal a high risk of malnutrition along with poor macronutrients and micronutrients intakes among Indonesian community-dwelling older adults. These findings provide important and robust evidence on the magnitude of malnutrition and nutrient inadequacy concerns that call for appropriate nutrition, as well as public health policies and prompt intervention.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018102268. The world's population is aging. In recent years, the elderly population is growing rapidly in developed and developing countries, including Indonesia. In Indonesia, more than 7% of 250 million residents is over the age of 60. This percentage is higher than it was in 1980 when it amounted to 5.4% . InadequDespite the growing proportion of the elderly population in Indonesia, data on the prevalence of malnutrition and low nutrient intake among Indonesian older adults are scattered and vary between studies . A largeThis systematic review was registered on the 24th of July 2018 in the international prospective register of systematic review (PROSPERO): CRD42018102268, and was written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist . The study selection was performed using Covidence®.After the search was performed, studies were independently screened by two authors (ED and RSF) on titles and abstracts to assess eligibility based on population and outcomes. The screening took place following predefined selection criteria: Studies were eligible if they contained data on the prevalence of malnutrition or nutrient intake data, participants' age was ≥60 years, and had data originating from Indonesian populations. Due to the scarcity of data and insubstantial difference between older adults and pre-older adults, we made an amendment to PROSPERO regarding the definition of our population: older adults were defined as people aged 60 years or older. We also considered studies which enrolled participants 50–59 year old age as pre-older adults. The title and abstract of the studies were screened to remove duplicates, animal studies, The Indonesian community-dwelling older adult population was the target population of this review. As age cut-offs for “older adults” varied across studies, a cut-off of 60 years or older for older adults, and 50–59 years old for pre-older adults was chosen to enable a broad inclusion. Community-dwelling older adults were defined as those living at home, living in private households, independently living, free-living, or non-institutionalized . MalnutrQuality assessment comprising critical appraisal and risk of bias of the individual full-text articles was performed independently by three independent reviewers  making use of the JBI critical appraisal checklist for studies reporting prevalence data  12). Th. Th12). The JBI data extraction form for prevalence studies  was usedData on the prevalence of malnutrition as well as the level, distribution, and prevalence of inadequacies of habitual intake of energy, macronutrients, and micronutrients were described in summary tables. Prevalence was presented as a percentage. For each of the included studies, the mean (and standard deviation) habitual dietary intakes of macronutrients and micronutrients were extracted. When necessary, means and measures of dispersion were estimated from the figures in the reports. Standard errors of nutrient intake as published in some studies were converted standard deviations by using the derivation formula of standard error = standard deviation/√ (sample size) beforehand , to makeTo assess the prevalence of inadequacies, the reported mean intakes were compared to the recommended dietary intakes of the Indonesian population (Indonesian RDA). The Indonesian RDA for age group 65–80 years old was used as the dietary reference intake. The results of nutrient intake of all of the studies included in this review were compared to the RDA, as most Asian countries did and as estimated average requirements (EAR) values are lacking . FurtherWe calculated the prevalence of inadequacy using the following calculation: z = (x–μ)/SD, with x as the dietary reference intake, μ the mean nutrient intake, and SD the standard deviation of the nutrient intake . Then, wThe search resulted in a total of 291 publications from several electronic databases. Nine of these publications were eligible, one of them  was retrCharacteristics of the nine included studies are presented in The risk of bias was assessed using the Joanna Brigg's Institute (JBI) critical appraisal checklist for studies reporting prevalence data, attached as an 2 as a criterion for underweight, whereby underweight prevalence ranged from 8.0 to 26.6%. According to the MNA, the prevalence of malnutrition ranged from 2.1 to 14.6%, whereby the prevalence of at risk of malnutrition amounted from 18 to 78%.Most studies presented the prevalence of malnutrition based on BMI categories and MNA criteria. The prevalence of malnutrition is shown in Data on the level and distribution of energy and nutrient intakes  were ext*RDA as a proxy of the EAR, estimated proportions of inadequacies were highest for vitamin D and Calcium, followed by protein and vitamin B12  with comparable characteristics and dietary patterns.The inception of this systematic review started before the concept of the Global Leadership Initiative on Malnutrition (GLIM) criteria for diagnosis of malnutrition was introduced in 2019 . Therefo≥65 years was 3.6% , or 2–15% (according to MNA). Hereby, we acknowledge that in most of the studies reviewed, the prevalence of malnutrition is either below 16% (six out of seven studies using the BMI criterion) or below 5% (four out of five studies using MNA criterion). Yet, in most studies, the risk of malnutrition was pronounced, amounting to over 44% in four out of five studies, including the MNA. Prevalences found in the literature are well within these ranges or even higher. In Singapore, the prevalence of malnutrition using the Mini Nutritional Assessment (MNA) in community-dwelling people aged was 3.6% . Anotherwas 3.6% . Wei et was 3.6% . Two stuwas 3.6%  while thwas 3.6% . Among Awas 3.6% . This coWe observed a wide variability in the intake of energy and nutrients and a high prevalence of inadequacies for all dietary intake parameters compared with RDA values and with specific nutrients compared to the EAR estimates. Nutrient standards that specify a mean requirement, such as the EAR, are preferably used to estimate the prevalence of nutrient adequacy for a group , 16. ThiWe found limited data to compare our data with studies in Southeast Asian countries, so we compared our findings to those of other Asian countries. A literature review conducted by Park et al. in Korean older adults found that the average total intake of energy was between 7,700 and 8,800 KJ/day . They reported inadequate intake of several micronutrients, mostly calcium and vitamin A . TherefoBsmI polymorphisms in the vitamin D receptor gene in the Indonesian–Malay race, contributing to the high prevalence of vitamin D inadequacy  D concentrations among community-dwelling older adults. The functional outcome of Ca intake is often bone density, where higher intakes of Ca (500 mg/d) plus vitamin D3 are associated with a higher bone density. However, calcium absorption is dependent on vitamin D intake because vitamin D facilitates the intestinal absorption of calcium. While biomarkers for calcium are generally thought to be problematic because they have no specific proper measurement technique, the blood level of vitamin d 25 (OH) D has been routinely checked in clinical practice . ConcernInadequacies of nutrient intake are determinants of malnutrition. Malnutrition leads to various health problems, mainly frailty, morbidity, and mortality. Nutrition interventions might effectively treat these conditions . DevelopIn conclusion, we signal a high risk of malnutrition in Indonesian older adults along with poor macronutrients and micronutrients intakes. The present review provides important and robust evidence of the magnitude of the malnutrition problem and nutrient intake concerns among Indonesian community-dwelling older adults. The promotion of nutrient-rich foods consumption needs to be explored. Strategies to tackle malnutrition and inadequacies of energy and nutrients intakes are most relevant to open the door for more nutrient-dense foods in the diet of Indonesian older adults.The original contributions presented in the study are included in the article/ED, RA, SFS, AP, LG, and SS contributed to development of study concept and design. SFS and AP contributed to acquisition of data. ED, LG, FH, MK, and SS contributed to analysis and interpretation of data. ED, RA, SFS, AP, FH, MK, LG, and SS contributed to drafting of the manuscript. All authors contributed to the article and approved the submitted version.The funding source of this study was supported by a grant from the Judith Zwartz Foundation, Wageningen, the Netherlands .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.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.


A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety. In this paper, we use multi-camera video analytic methods to design a system that recognizes hand-hygiene actions for food safety. Food safety is a discipline that describes scientific methods to prevent contamination and foodborne illness at different stages of food production. The stages include, but are not limited to, food handling, food storage, equipment cleaning, and staff hygiene. In recent years, where the burden of foodborne illnesses is increasing, evidence indicates that the majority of food contamination is caused by inappropriate food manufacturing practices, involving workers with poor food handling skills . TherefoIn food handling, there are many steps to achieve good manufacturing practices (GMPs). Hand-hygiene is one of the most critical steps. Effective hand-hygiene can reduce food contamination by human pathogens, since this step reduces the likelihood that food handlers harbor pathogenic microorganisms on their hands and transfer them to food products . AccordiThere has recently been significant progress in automated methods for analyzing video content, a process called video analytics. Stationary cameras placed in a so-called third-person perspective have been used for surveillance, person and vehicle detection and re-identification, activity recognition, and anomaly detection. When recognizing activities of a person, third-person cameras have the advantage of viewing actions from the side view. First-person, or egocentric cameras are mounted on the person performing the activity, often on their head or chest . These cRecognizing activities in videos is a rapidly growing field with wide-ranging applications. Activity recognition techniques have been developed for both trimmed, and untrimmed videos. For trimmed videos, the task is to identify which isolated activity happens in a video clip that has been trimmed to contain only a single activity. For untrimmed videos, the task is not only to recognize the target action, but also to localize it temporally within the clip that may contain unrelated background actions. This process is termed temporal action localization and is often addressed by identifying temporal action proposals ,6,7. In pre-hygiene period, before the participant engages in hand-hygiene, they are near the sink but may be doing any non-hygiene activities. During the second hand-hygiene period, the participant stands in front of the sink and performs hand-hygiene actions such as rubbing hands and applying soap. During this period, the participant is likely to stand relatively still and perform one hand-hygiene action after another. Finally, in the third post-hygiene period, the participant is likely to move away from the sink and look for a paper towel to dry their hands. Thus, actions occurring in this final period may be a combination of non-hygiene actions and “dry hands”.Our goal was to design an automated video system that processes untrimmed video to assess the effectiveness of hand-hygiene activities. From the observations of We define different levels of tasks for hand-hygiene action recognition, according to their detection difficulty. In this paper, we focus on recognizing standard-level hand-hygiene actions.We record our own hand-hygiene action data set with 100 participants using three camera views.We compare and evaluate the performance of deep-learning models on three different camera views to recognize trimmed hand-hygiene action video clips. We select the best two camera views for our final system design.We empirically explore the required complexity of a CNN model when applied to recognize our selected types of hand-hygiene actions. These empirical evaluation results enable us to deploy appropriate models for our final system.We propose a two-stage framework to recognize hand-hygiene actions from untrimmed video sequences. We combine two camera views to localize and recognize hand-hygiene actions. Taking advantage of the static third-person view camera, we use a low-complexity CNN model to localize the hand-hygiene period within an untrimmed video. Then we apply more complex CNN models to recognize the actual hand-hygiene action types within these candidate locations.Comparing our two-stage system and a baseline system on untrimmed video sequences, we demonstrate that with the support of low-complexity CNN models, our two-stage system achieves nearly equivalent performance by only processing In this paper, we propose a framework to recognize hand-hygiene actions inside untrimmed video sequences with the following contributions:These contributions are relative to our previous work , the staIn In this paper, our goal is to identify the hand-hygiene steps a user performs within an untrimmed input video. Due to the nature of hand-hygiene activities, highly salient aspects that distinguish hand-hygiene from non-hygiene actions are the person’s hand and arm movements. In addition, different types of hand-hygiene actions have distinctly different motion strength and body parts involved, and these aspects provide important clues to distinguish actions. Therefore, in this section we provide an overview of prior work in activity recognition using computer vision.Activity recognition focuses on detecting the events inside a video clip and categorizing each into its activity category . TraditiHowever, in recent years, methods based on deep learning have largely replaced these traditional methods. In general, because traditional hand-crafted feature extraction algorithms are designed by human experts, it is difficult to customize them to different applications. In contrast, deep learning methods can overcome this limitation by using sufficient training data to learn “customized” features. Convolutional neural network models (CNNs) such as AlexNet , VGGNet However, for actions related to hand motions, which are prevalent in hand-hygiene activities, using an LSTM has been shown to be less effective. Köpüklü et al.  compare In addition, as portable camera equipment becomes available, researchers have begun to explore action recognition in egocentric videos. Egocentric videos in daily living scenes is explored in , using rResearchers continue to explore what key information is needed to recognize egocentric activities. One contribution  analyzesMoreover, in addition to the exploration of actions that involve hand-to-object interaction, actions that only involve pure hand gestures have also been studied. An important approach in this field is to use multi-modalities instead of solely RGB image as the input to the system. One of the crucial modalities is the skeleton joints, which offer a neat and accurate representation of human pose. A multi-model system was designed in  for gestFrom the studies above, the importance of skeleton joints in recognizing pure hand gestures without the presence of objects is clear. However, skeleton joints extractions are not always feasible. From a camera perspective, all the videos recorded with a special camera like Kinect can naturally provide human skeleton information. While this type of camera has been used in research, it is still not widely used in practical video monitoring. An alternative approach for gathering skeleton data is to through video processing algorithms. OpenPose  and DeepBased on the above works, most of the egocentric activity recognition tasks focus on the same category of daily living videos, which involves many hand-to-object interactions. For those works that target pure hand-gesture recognition, the videos are recorded using third-person views with additional depth and skeleton joint information provided. In this paper, we examine how different camera views influence hand-hygiene action detection and design a method that incorporates information from both cameras for hand action recognition in untrimmed video sequences.Hand-hygiene specifically has been considered by ,43. HoweIn this section, we introduce our hand-hygiene dataset for hand-hygiene action recognition. We start by introducing the video collection procedure. Then, we define the set of actions we consider in this paper, by describing the choice of our specific task. Finally, we describe how a hand-hygiene system should be evaluated in the context of a real application.As the area of egocentric video becomes popular, researchers have published datasets ,33 for eTo ensure that our dataset includes enough variation between samples, we invited 100 participants and recorded the videos in two separate public restrooms with similar environments (All data collection took place in August 2018 and was done within the context of Purdue IRB # 1804020457.). All participants were allowed to wear any type of clothing, including watches and hand jewelry, and had varied ages, genders, and races. Each participant was recorded twice while they washed their hands. Initially, each participant performed a naive hand washing in the first room, according to their typical hand washing style. Then, the participant was asked to read the instructions for hand-hygiene shown in Our overall goal was to design a method that will operate in any indoor environment, including temporary environments with portable wash-stations. A single camera mounted on the ceiling created a top-down view for the previous work in ,43. HoweTwo type of cameras, egocentric and third-person, are applied in our data collection. An egocentric camera is capable of capturing subtle motions of hands and fingers, which provides supportive information to classify different types of hand actions. In contrast, a third-person camera is efficient at capturing a participant’s body motion as well as any interaction with the surroundings.To explore the efficiency of third-person video and egocentric video in hand-hygiene actions, we used both egocentric cameras and a static third-person view camera for video recording. Each participant wore a GoPro camera with a harness on their chest as one egocentric camera and an IVUE glasses camera on their nose as another egocentric camera. The third-person view camera was a GoPro camera placed on top of a flat platform near the sink. We will refer these three camera views as “chest camera view”, “nose camera view”, and “wall camera view” for the rest of this paper. Each video has 1080p resolution, 30 FPS, and a wide viewing angle.detail-level, standard-level, and detection-level tasks. In this paper, we will only explore the standard-level task.To the best of our knowledge, we are the first project to apply video analytic methods on hand-hygiene actions. Therefore, we want to define the different types of hand-hygiene actions and the purpose of analyzing these actions. We defined three different video analytic tasks to explore in hand-hygiene actions. Based on the level of difficulties of these tasks, we define them as In the detail-level task, the goal is to strictly follow each of the steps outlined by the World Health Organization in This detail-level task has the highest difficulty compared to other hand-hygiene tasks, especially for those actions that involve subtle motions of the hand and fingers, such as those illustrated in Even if the entire hand regions are clearly recorded, recognizing actions with subtle finger and hand motions is still difficult. The temporal boundaries between actions of rub cross finger, rub palm, and rub thumb are difficult to distinguish even by a human expert. The method in  to recogIn the standard-level hand-hygiene task, we focus on analyzing only the components of the 12 steps in As mentioned above in In addition to the previous two task levels we defined, there exists a detection-level hand-hygiene task, which simply analyzes whether or not the hand-hygiene action happened. For this task, egocentric camera information regarding the hands is not necessary, and directly analyzing the third-person camera should achieve an acceptable result.In this paper, we focus on the standard-level hand-hygiene task, which considers 7 different action types. In a real application for hand-hygiene verification, a user would like to know if some actions happened for a sufficiently long time, and if other actions happened at all. Therefore, performance evaluation of a system requires distinct measurements depending on the action type. For example, both “rub hands with water” and “rub hands without water” reflect the participants’ effort to clean their hands. Thus, it is important for us not only to detect the existence of these actions, but also to determine how long they last. Three actions, “touch faucet with elbow”, “apply soap”, and “dry hands”, help sanitize and prevent re-contamination; therefore, we only need to confirm the existence of these actions. However, “touch faucet with hands” actually re-contaminates hands, and should be identified if it happens. Meanwhile, the background non-hand-hygiene actions do not influence the hand washing quality, and these are included in the set of actions for completeness.Based on these observations, we evaluate the hand-hygiene performance of a participant by evaluating whether a correct decision was made regarding which action occurs during each second of the video. Thus, we divide each target video into non-overlapping units of 30 consecutive frames, which corresponds to 1 s in time. Each unit is labeled with only one action type by counting the most frequent action type of each frame among all 30 frames. This assumes a detector makes one prediction during each unit. To achieve this, a detector can predict an action for 30 frames individually and average the confidence scores to create a prediction result for the unit, for example.The top and bottom of In this section, we describe a basic two-stage system to detect standard-level hand-hygiene actions, namely, the 7 actions described in Recall that most hand-based actions are densely located in the hand-hygiene period and partially distributed within the post-hygiene period. All the remaining video content consist of non-hygiene actions with unknown and variable duration. Thus, it would be inefficient to densely process the entire untrimmed video with a computationally-complex CNN model. Recall that an untrimmed video clip contains more than one action type, while a trimmed video clip contains only one action.The first stage of the system consists of two so-called coarse classifiers; one processes the wall video and one processes the chest video. They each densely process the entire untrimmed video and localize potential candidates for the temporal regions that might contain standard-level hand-hygiene actions. In the second system stage, we apply two so-called refinement classifiers that only process the candidate locations identified in the first stage.Specifically, in the first system stage, as shown in Additionally in the first system stage, the chest coarse classifier processes only the region identified as “post-hygiene”. Its goal is specifically to identify whether the action of “dry hands” happened or not. Further detail on its design is provided in In the second system stage, shown in The final classifier uses the chest view to refine the actions that take place in the hand-hygiene region. Its goal is to label every time unit according to each of the 7 actions in the standard-level hand-hygiene task described in However, some actual hand-hygiene periods may have been misidentified as non-hygiene in the first system stage. This is illustrated in To summarize, the overall system takes as input the untrimmed video that contains both hygiene and non-hygiene actions. Four actions must be identified as to whether they happen or not. These are the actions of “dry hands”, “touch faucet with elbow”, “touch faucet with hand”, and “apply soap”. The first is detected by the chest coarse classifier, the second by the refinement wall classifier, and the latter two by the refinement chest classifier. For the remaining two actions, “rub hands without water” and “rub hands with water”, both of which are identified by the refinement wall classifier, it is important to verify that they lasted for at least 20 s. During our final system evaluation in Question 1: Which camera is most informative for which actions? Question 2: How much computational complexity is required for the standard-level hand-hygiene task? What models should we use? How deep? How much accuracy do we lose if we use classifiers with lower computational complexity? Question 3: Is RGB information sufficient or should we include motion information? Question 4: To coarsely recognize hand-hygiene temporal regions from untrimmed input video, what model should be used for the wall camera? Question 5: To coarsely recognize the single action of “dry hands”, can we use hand-crafted features or would a CNN perform better? In this section, we explore the design of each individual classifier in the the two-stage system described in the previous section. We take an experimental approach to address the following questions:First in As indicated in Our goal in this section is to better understand which camera view is most effective for each action. Therefore, we apply a single model for each camera; the input to each model the set of RGB images from a trimmed video, and the output is one of the 7 actions for the standard-level hand-hygiene task. Since, for hand-hygiene actions, the majority of the content is composed of hand-to-hand and hand-to-arm interactions and there are no salient objects to help distinguish each action type, we choose to use a single deep-learning based model to learn an efficient representation. In this paper, we follow the idea of the two-stream network  to consiIn the rest of the subsection, we describe our experimental design, the model training, and finally the results of applying the model to our dataset.To solve the standard-level task defined in For each camera view, we begin with a ResNet152 model that was pre-trained on ImageNet . We fineThe training hyperparameters for each camera view are set to the same for comparison, although they could be further optimized. However, our goal for this experiment was to compare the efficiency of each camera when processed by the same CNN architecture. Each model was trained for 250 epochs using a Stochastic Gradient Descent (SGD) optimizer with learning rate Testing is performed on each trimmed video clip. The trained model is applied on every frame of a test video clip, followed by a softmax function. The average score among all frames indicates the prediction result. The testing results of all 7 actions averaged over the 5 trials is shown in Comparing across the rows of the table, we can see that the chest view outperforms the other views for recognizing actions with detailed hand-hand interactions, such as “rub hands without water”, “touch faucet with hands”, and “dry hands”. However, we observe a small performance drop for the chest view on the actions “rub hands with water” and “apply soap” compared to the wall view, because the chest camera does not always capture the hand regions.Compared to the chest camera view, the nose camera view achieves less accurate results for all action types except “apply soap”. This is because the nose camera is mounted above the chest camera, so it is easier for the nose camera view to capture the scene when the user applies soap.On the other hand, the wall camera can also predict many of the action types within its viewing range, especially when an action contains the body motion from a participant. As the shown in the heatmaps in The last row of In the last section, we applied the ResNet152 model to explore the relative advantages of using the wall and chest camera views for a standard-level hand-hygiene task. ResNet152 is a relatively complex model; therefore, in this subsection, we evaluate the performance of different CNN models on the standard-level hand-hygiene task. In addition, we also evaluate whether adding optical flow improves performance for the chest camera. Together, these experiments inform the design of the refinement classifiers in the second stage of our system.The models we consider are VGG19, VGG16, and VGG11, which are variants of the VGG network  with higThe testing results of data from trial 1 (only) are listed in Based on the result from Training: Similar to the chest camera RGB model, the chest camera optical flow model still takes a pre-trained ResNet 152 network and fine-tunes the last fully-connected layer for our 7 action classes. The first convolutional layer is revised to take as input 10 frames of horizontal and vertical optical flow images. The model is trained with 350 epochs and learning rate Testing: The testing step follows the same procedures as We see from the table that for all 7 actions classes, using optical flow does not provide significant improvements for recognition. In addition, in our previous work , fusing In this section, we develop classifiers to recognize coarse hand-hygiene actions in the first system stage described in The wall camera (a third-person view camera) is placed on a flat platform in our experiment, so it captures the participant’s actions near the sink from a close range. Due to the limitation of the camera angle, this camera view cannot capture hand actions in detail; however, we showed in Based on the observations in ,42, shalModel for RGB images: To ensure a low computational cost and fast processing speed for this coarse processor, we are inspired by the tiny image dataset CIFAR 10 [CIFAR 10 . Thus, wTo explore how variations of the CNN architecture affect the detection performance, we evaluate three structures. The first structure consists of 5 groups of convolutional layer, max pooling layer, and RELU non-linearity followed with a fully connected layer and softmax as indicated in Features to describe supplemental silhouette information: Since our wall camera is stationary, foreground–background segmentation is a powerful method to understand moving objects in the scene. Three methods we consider here are an adaptive background learning [learning  method, learning  and SuBSlearning . We complearning .In addition, we consider the motion history image (MHI), which is an efficient method to create a temporal template that indicates motion . MHI enceshold ξ . In our Model for both RGB images and MHI: In addition to the three RGB-only models mentioned above, we also consider a model that takes RGB and the motion history image as indicated in Model training: All the training and testing are performed on the first data trial defined in Testing: To compare the performance of these structures, we consider untrimmed testing videos and predict 3 action types: “faucet elbow”, hand-hygiene, and non-hygiene. We evaluate the performance of each structure with two metrics: the frame-level accuracy and the unit-level accuracy. Frame-level accuracy is computed by the sum of true positive and true negative predictions over all frames in the video. Instead of predicting every single frame, the unit-level prediction starts by cutting the untrimmed video into non-overlapping units of consecutive 30 frames, which is 1 s in our video. Then, we make a prediction of an action class for each unit by averaging each frames prediction confidence score. The unit-level accuracy is also computed as the sum of true positive and true negative units over the total number of units in the video.The prediction results of the three structures for RGB-only, as well as for the RGB + MHI model are shown in As described in Several options are available for low-complexity models. For temporal segmentation of egocentric videos, Bolaños et al.  apply coTraining: Similar to the experiments in the previous section, all training and testing are performed on the first data trial defined in We compute the hand-crafted features on an RGB image with size 224 × 224. We compute the color histogram separately on 4 non-overlapping spatial regions of the image using the hue, saturation, value (HSV) color space. The histogram for the lightness  channel has 8 evenly-spaced bins while the histograms for the other two each have 3 evenly-spaced bins. Concatenating all the histograms yields a final color feature with 288 dimensions. We also extract HOG  and LBP Testing: We perform frame-level prediction to compare both the deep-learning model and hand-crafted feature Random Forest classifiers for the two ground-truth actions labeled either “dry hands” or “non-hygiene”. We evaluate the methods using precision, recall, and accuracy at the frame level by considering “dry hands” as a positive sample and “non-hygiene” as a negative sample.From the results in In the previous In the first stage of the system, the wall coarse classifier uses the CNN model with RGB and MHI as inputs, and the chest coarse classifier applies a CNN model for binary classification on the “dry hands” action. In the second system stage, both the CNN model for wall and chest camera view are applied with VGG11 network architecture.To create a point of comparison, we consider a baseline system that applies the second-stage VGG11 networks to the entire chest camera video and wall camera video. Each classifier densely processes the entire untrimmed video using non-overlapped 30-frame units. The classifier for the wall camera is only responsible to detect the action “touch faucet with elbow” action, and the classifier for the chest camera is applied to detect all other actions.Recall that our goal for the two-stage system was to achieve similar performance to the baseline system, but with less computation. The baseline system applies VGG11 on every 30 consecutive frame units throughout the entire video, while the two-stage system applies VGG11 only when a simpler classifier would be insufficient. Therefore, we expect that both systems will achieve very similar detection accuracy. If so, it demonstrates that our coarse classifier successfully localizes the crucial hand-hygiene temporal parts, reducing the overall system complexity without sacrificing performance.The overall performance of the two systems is evaluated on the first trial defined in We evaluate the overall performance of this system in two parts. In the first part, we evaluate only the two actions that require an estimate of how long they last; these actions are “rub hands with water” and “rub hands with no water”. We measure their detection performance using the Jaccard index, applied for units of 30 consecutive frames. In addition, we measure the average prediction error by computing the absolute difference between the detected time duration and ground-truth duration, averaged across all test videos. In the second part of evaluation, we consider the remaining actions for which we only need to confirm whether or not they happened. We evaluate these simply using the accuracy across all test videos, computed by dividing the number of correct predictions divided by the total number of predictions.Overall, the two-stage system and the baseline system achieve similar performance for recognizing each action and for estimating the duration of rubbing. However, with the support of the low-complexity CNN models for localization, the two refinement classifiers in our two-stage system only process In this paper, we introduce the task of hand-hygiene action recognition from untrimmed video. We approach this problem by defining different levels of the task and design a system that performs hand-hygiene recognition at the standard level. To explore the efficiency of using different camera views on recognizing 7 hand-hygiene actions, we collected a dataset using three cameras and 100 participants. Using this dataset we are able to explore different deep-learning models on our hand-hygiene dataset with both egocentric and third person camera views. The results indicate both these camera views have their own unique advantages for recognizing certain action types. Thus, it is important to use both camera views for hand-hygiene action recognition.Moreover, we also explore the realistic scenario in which we recognize hand-hygiene actions inside untrimmed video. We design a two-stage system to localize the target hand-hygiene regions and we apply deep-learning models from two camera views for the final recognition. In the first stage, a low-complexity CNN model is applied on the third-person view to segment the untrimmed video into three temporal periods. In the second stage, we assign these temporal periods to more complex CNN models trained for different camera views, so that each model only has to recognize the actions suited for that camera view. In the final evaluation, our two-stage system achieves similar performance to the baseline, which applies CNN models to densely process every second in the entire untrimmed video. We demonstrate that the two-stage system can efficiently filter out non-hygiene regions so that it only needs to apply complex CNN models to the crucial hand-hygiene temporal regions.In the future, we plan to extend our current hand-hygiene system to different environments. The work presented in this paper focuses on system for hand-hygiene in a small enclosed restroom. However, other realistic hand-hygiene scenarios may also happen in industrial kitchens, restaurants, or laboratories. It is worth exploring how to refine our system to extract features that are robust against different illumination and background scenarios. This would allow the system to be easily applied to a new target environment to detect hand-hygiene actions without significant modifications. Furthermore, our current hand-hygiene system can also be extended to be applied on other aspects of food handling, like washing vegetables, by exploring the interaction between hands and objects. Moreover, the hand-hygiene system could also be expanded to consider the detail-level hand-hygiene task, as we described in


The random model we use is based on a symplectic form on moduli space due to Atiyah, Bott and Goldman. Let  If surface group of genus g. For Let ibid.).The space of homomorphisms (ibid.) that the action of moduli spacerandom representations of n vector bundles on There is an action of  view of , this isSeshadri , if a co(ibid.), Goldman shows that there is a natural symplectic form Atiyah–Bott–Goldman measure.To describe the law of the random representation, we need to recall some further results of Goldman. In The statistics of random representations we are interested in come from functions ongeometric functions . With the normalization of word mapw. We write g factors.In fact, Sengupta also provided a method to compute the integral of any continuous function on One has the following corollary of Sengupta’s main result , Thm.1.if the sum on the right-hand side of n. In fact, the results of this paper can be viewed as a far generalization of this result and accordingly, the just-mentioned result is established as a byproduct of our proofs in §§The limiting value arises from the trivial representation in ; it is pn increases. We aim to approximate  in  (Proposi}U(n) in . The maiAfter this initial integral, we then apply the Weingarten calculus through a novel strategy cf. §§ making hFor e sum in  is absolw| denotes the length of w as a reduced word.As Suppose that B sufficiently large and fixed depending on w, the contribution to The point of  is not tSuppose that The existence of an asymptotic expansion of G the representation space G is finite, this is simply the uniform probability measure. For any character G and rational function of n for n sufficiently large. Indeed, for fixed n for χ(n)=fix , LP10G(n) is a family of generalized symmetric groups, e.g., hyperoctahedral groups, and entation }χ(n)=tr }χ(n)=tr .Similarly, if instead of using a surface group t is a coupling constant.  It is t, with the disclaimer that the author is by no means an expert in the concepts of theoretical physics.The emphasis in the physics literature is not on the rigorous analytic approximation of integrals such as , but rations’ of  obtainedions’ of ; nonethe case of  via Corochiral expansion means that the n rows. This is referred to as the finite-n expansion. However, in some cases this restriction is lifted and  is a collection of left-aligned rows of identical square boxes in the plane, where the number of boxes in each row is non-increasing from top to bottom. Hence, we use the English convention for Young diagrams throughout the text. Any YD ith (from top to bottom) row of partition. We think of partitions and YDs interchangeably in this paper via the above correspondence. For example, the partition (k) corresponds to the Young diagram with one row consisting of k boxes. The empty YD with no boxes is denoted by size of a YD A skew Young diagram (SYD) is a pair Given two Young diagrams k boxes. We say We will have use for the following relations between Young diagrams. We say semistandard tableau of shape S is a filling of the boxes of S such that the numbers in the boxes are strictly increasing along columns from top to bottom and non-strictly increasing along rows from left to right. If n]. There is by convention a unique semistandard tableau (with any entry set) of shape the empty YD (or an empty SYD).Given a SYD If weight of T is the functionj in T.Given T with numbers T with numbers Given Here we clarify the language of representation theory that will appear throughout the paper.G consists of a Hilbert space4A unitary representation of a compact Lie group3G, an intertwiner between linear map linearly isomorphic. If there is an invertible isometric intertwiner between the two, we say they are unitarily equivalent or isomorphic as unitary representations.If G and the spaces In many cases, the maps G, and -isotypic subspace ofWfor the subgroupH to be the subspace spanned by the images of all elements ofrestriction of H. Any such isotypic space is itself a unitary subrepresentation of H). If V is an irreducible representation of H, the dimension of multiplicity with which V appears in For Let W with inner product induced by that on W; this representation is also irreducible if g on a linear functional W is by G, the tensor product G that is ‘diagonal’ by G under the diagonal action. This extends to tensor powers of W; any G under the diagonal action.There is a dual unitary representation on the space of complex linear functionals on G acts ‘diagonally’ on the left-hand side, and by conjugation . Suppose that This is a standard fact but normally presented slightly differently. To obtain this version, one needs to know that Here we give a brief account of the representation theory of found in .signature of the representation. If all The equivalence classes of irreducible representations of Every irreducible representation of To be less verbose, in the rest of the paper we will tend to refer to representations simply by the vector space whenever the module structure can be inferred.Schur polynomial associated with the YD g. As such, the dimension of hook content formula. Given a YD content of the box to beg is any element of g. The hook content formula  is an irreducible representation of We always understand that . Let g with base point Recall that w as a combinatorial word in Given w, for each w representing The commutator subgroup We first follow Goldman  to definmbedding . The diairreducible as linear representations. The conjugation action of Let o proper , pg. 205)dimSU(n), §1.3. TBy , §1.4 foWe normalize the Killing form on ble  measure (n)  Let (Deduction of Corollary w denoting the word map of w) so that Under the same assumptions as Theorem  Theorem  we obtaiB as fixed and assuming n is large enough depending on BIf If In light of Corollary ;n) from , viewingn behavior of At various points, it is convenient to execute the integral defining If On the other hand, if   w are ‘balanced’ and the inner integrand is This proves Part3.1.By uniqueness of the Haar measure on w is not in w are not balanced, and for at least oneFor the second part, suppose This means the main theorem is proved when w representing the conjugacy class of We observe that the series defining As Theorem1.1has now been proved forin the rest of the paper, we may assumeand hence, anywrepresenting the conjugacy class ofis int with coefficients in f. Let Here we prove the following theorem. Let There is a rational function n, it now suffices to prove that each n for fixed n with coefficients in Fix Claim: For eachwhenagrees with a rational function ofnwith coefficients inProof of claim. We first use Proposition n byFor I.For w in reduced form:I. Most of the rest of the section will be devoted to estimating In the rest of this ation in , we obtaI and hence write all integrals over groups are done with respect to the probability Haar measure and this will be denoted by dg where g is the group element.We keep the assumptions of the previous section and also assumeOur goal is to calculate lines as , §4.4. WLet For any projection operator. Indeed, let Q denote the orthogonal projection in The purpose of Lemma We haveBy Lemma 4.2Recalling the definition of μ,m from , we are We haveAn orthonormal basis for the Now combining , Lemma 4For R of This is progress because now the integrand is The only method we really have to proceed from Proposition w from §§a, b, c, d, and whereSince we assume estimate .X, P is the self-adjoint orthogonal projection on We can write integral  is that For any In Proposition We now calculate We estimate On the other hand, after suitable identifications, we have tes into , we obtates into  |, as deWe now outline the strategy that we will follow to estimate the integral . We can It does not seem possible to exactly evaluate  using thThe components of this strategy will be carried out in the following sections.Now fix s yields a sequence of tableauxs containing numbers t. We haveRecall the weight functions sm as in  for \docLetUnder For Consider the linear map by which The proof relies on the fact that up to scalar multiplication, Claim A. For allis in theProof of Claim A. We prove by induction on j the following statement: S : For  Consider the base cases S is true for some The proof of Claim A is complete.rticular , the facClaim B.commutes withor in other words, is invariant under the diagonal subgroupProof of Claim B. Using the intertwining properties of the maps This completes the proof of Claim B.a and b, we must haveNow by Claim A with rence to ,\documenThe goal of this section is to calculate We haveWe do this calculation in stages. Firstly, the norm of Now for each e insert , the onlla Lemma  \documen1(r). So  togetherWe now calculate ms as in  and the not haveThe Weingarten calculus Following our strategy, applying Proposition g, Next using Proposition ce, from  we obtaiThe main task of this section is to estimate the sum side of . Let \doTheorem Let given Theorem robenius  when \dog=1; see , §2 or [n convergence of the Witten zeta function as in Theorem w). The key estimate we take from (ibid.) is the following. Given a YD Even in the simple case of ent}γ=id  shows th and can be expanded as a Laurent polynomial as in (M must be coherent, i.e., arise from a fixed infinite sequence By Theorem ombining  with (5.al as in  up to or