Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> The ability to discriminate between falls and activities of daily living (ADL) has been investigated by using tri-axial accelerometer sensors, mounted on the trunk and using simulated falls performed by young healthy subjects under supervised conditions and ADL performed by elderly subjects. In this paper we propose a power-aware real-time fall detection integrated circuit (IC) that can distinguish Falls from ADL by processing the accelerations measured during 240 falls and 240 ADL. In the proposed fixed point custom DSP architecture, a threshold algorithm was implemented to analyze the effectiveness of Programmable Truncated Multiplication regarding power reduction while maintaining a high output accuracy. The presented system runs a real time implementation of the algorithm on a low power architecture that allows up to 23% power savings through its digital blocks when compared to a standard implementation, without any accuracy loss. <s> BIB001 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> Falling is a serious health problem for many elderly. To investigate whether the higher fall incidence in elderly is due to a higher probability of experiencing near falls in daily life, it is necessary to evaluate the stumble incidence of elderly in daily life. Accelerometers are already frequently used for in vivo activity monitoring. The current study investigates whether an ambulant and unobtrusive accelerometer can identify stumbles from treadmill walking using a wavelet based detection approach. Seventy nine healthy subjects walked on a treadmill with a triaxial accelerometer attached at the level of the sacrum. Stumbles were induced using a specially designed braking system (The TRiP). The TRiP evoked 30 stumbles at different phases of the swing phase. A wavelet-based detection algorithm is used to isolate the stumbles from treadmill walking, with a specificity of 99.9% and a sensitivity of 98.4%. <s> BIB002 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> Fall prevention in elderly subjects is often based on training and rehabilitation programs that include mostly traditional balance and strength exercises. By applying such conventional interventions to improve gait performance and decrease fall risk, some important factors are neglected such as the dynamics of the gait and the motor learning processes. The EU project "Self Mobility Improvement in the eLderly by counteractING falls" (SMILING project) aimed to improve age-related gait and balance performance by using unpredicted external perturbations during walking through motorized shoes that change insole inclination at each stance. This paper describes the shoe-worn inertial module and the gait analysis method needed to control in real-time the shoe insole inclination during training, as well as gait spatio-temporal parameters obtained during long distance walking before and after the 8-week training program that assessed the efficacy of training with these motorized shoes. <s> BIB003 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> In developed countries, sedentary lifestyle is a major health risk factor. In elderly people, such mobility limitation is worsened by the reduced self-confidence and the fear of falling, leading to a further motor deterioration. This work presents an application of a wireless Body Sensor Network as a simple and easy-to-use individual motor function assessment tool for elderly. The wearable nodes have been exploited to monitor the body during the Six-Minute Walk Test and a set of stability tests. During the exercises, wearable sensors inertial data, along with the real-time orientation of the platforms, have been exploited to obtain gold-standard indicators (such as total distance) and some additional gait parameters. Stability tests consist of a series of single and double stance exercises aimed to assess the balance of the subject. This paper presents the system, the processing and the preliminary results on two subjects groups of different ages (31±6 and 70.8±7). <s> BIB004 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> Monitoring biosignals has become more people-friendly since the development of wearable electro-conductive cloth. This paper proposes an application using wearable EMG sensors that estimates the fall risk of elderly people based on co-contraction of the lower limb during walking. Fifty-two healthy elderly people participated in a gait test with continuous EMG of the thigh (rectus femoris and biceps femoris) and cruris (tibialis anterior and gastrocnemius). The participants were categorized into a "faller group" and a "non-faller group" based on their experience of falls in the previous year. Co-contraction of thigh and cruris were determined based on EMG, and were then used to estimate the experience of falling using a linear discrimination method. The results showed that thigh co-contraction during the stance phase can be predictive of falling experience with 65% accuracy. <s> BIB005 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> BackgroundWearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data.MethodsA convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling.ResultsThe best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier.ConclusionsFeature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors. <s> BIB006 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Participants <s> Wearable sensors can provide quantitative, gait-based assessments that can translate to point-of-care environments. This investigation generated elderly fall-risk predictive models based on wearable-sensor-derived gait data and prospective fall occurrence, and identified the optimal sensor type, location, and combination for single and dual-task walking. 75 individuals who reported six month prospective fall occurrence (75.2 ± 6.6 years; 47 non-fallers and 28 fallers) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensinginsoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Fall-risk classificationmodels were assessed for all sensor combinations and three model types: neural network, naive Bayesian, and support vector machine. The best performing model used a neural network, dual-task gait data, and input parameters from head, pelvis, and left shank accelerometers (accuracy = 57%, sensitivity = 43%, and specificity = 65%). The best single-sensor model used a neural network, dual-task gait data, and pelvis accelerometer parameters (accuracy = 54%, sensitivity = 35%, and specificity = 67%). Single-task and dual-task gait assessments provided similar fall-risk model performance. Fall-risk predictive models developed for point-of-care environments should use multi-sensor dual-task gait assessment with the pelvis location considered if assessment is limited to a single sensor. <s> BIB007
In Figure 4 , we report the number of participants for each study (grouped in four categories) for the considered articles, excluding the ones that do not specify the population age and/or participants' number, and that discuss a specific sensor/methodology only qualitatively, without any population involved. From the inspection of the figure, the most frequent bins are very different from a numerical viewpoint. Additionally, only a few articles have more than 100 participants in the study, reflecting the difficulty in recruiting a wide audience for these studies. From the conducted review of the literature, it emerges that there are mainly two lines of research: (a) studies concerning the comparison between old versus young subjects BIB003 BIB004 BIB001 BIB002 and (b) works aiming at underlining some peculiar differences between groups of elderly fallers and non-fallers BIB005 BIB006 BIB007  . In both cases, there is a prevalent presence of healthy elderly groups. This observation is confirmed by Figure 4 , where it is shown that most of the participants in all studies are 64 years old or more, which is not surprising provided that this was used as an inclusion criterion. On the other hand, the prevalence of the young and middle (30 to 64) groups in studies with less than 10 subjects reflects the need of a fast validation in the case of a novel instrumental technology.