Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <s> The study of postural sway during quiet stance has proved a useful approach to investigate the function of the balance system. Recent studies have suggested that providing information on postural sway to vestibular patients through various biofeedback devices may improve their balance awareness and therefore reduce their risk of falling. One drawback common to these approaches is related to timing: informing a patient about current balance conditions may not allow enough time to react and avoid a fall. Here we propose a new technique for predicting relevant balance related variables based on the recording of inertial information on trunk movement using a wearable device. Our preliminary results show that such approach may allow predicting critical balance conditions with few hundreds of milliseconds advance and is thus promising for the development of a fall prevention device. <s> BIB001 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <s> Biofeedback is known to improve postural control and shorten rehabilitation periods among the young and elderly. A biofeedback system communicates with the human central nervous system through a variety of feedback modalities. Vibrotactile feedback devices are gaining attention due to their desirable characteristics and simplistic manner of presenting biofeedback. In this study, we investigate the potential of incorporating a real-time biofeedback system with artificial intelligence for wobble board training, aimed at improving ankle proprioception. The designed system utilizes vibrotactile actuators to provide forewarning for poor postural control. The biofeedback system depended on Euler angular measurements of trunk and wobble board displacements, from inertial measurement units (IMUs). A fuzzy inference system was used to determine the quality of postural control, based on IMU-acquired measurements of trunk and wobble board. The designed system integrates: 1) two IMUs, 2) a fuzzy knowledge base, and 3) a feedback-generation module. Tests were conducted in eyes-open and eyes-close conditions while standing on the wobble board to assess viability of the system in providing accurate real-time intervention. The results observed an improvement in postural control with biofeedback intervention, demonstrating successfulness of the prototype built for improving postural control in rehabilitative and preventive applications. <s> BIB002 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <s> BACKGROUND: Frailty is a geriatric syndrome resulting from age-related cumulative decline across multiple physiologic systems, impaired homeostatic reserve, and reduced capacity to resist stress. Based on recent estimates, 10% of community-dwelling older individuals are frail and another 41.6% are prefrail. Frail elders account for the highest health care costs in industrialized nations. Impaired physical function is a major indicator of frailty, and functional performance tests are useful for the identification of frailty. OBJECTIVE instrumented assessments of physical functioning that are feasible for home frailty screening have not been adequately developed. OBJECTIVE: To examine the ability of wearable sensor-based in-home assessment of gait, balance, and physical activity (PA) to discriminate between frailty levels (nonfrail, prefrail, and frail). METHODS: In an observational cross-sectional study, in-home visits were completed in 125 older adults (nonfrail: n = 44, prefrail: n = 60, frail: n = 21) living in Tucson, Ariz., USA, between September 2012 and November 2013. Temporal-spatial gait parameters (speed, stride length, stride time, double support, and variability of stride velocity), postural balance (sway of hip, ankle, and center of mass), and PA (percentage of walking, standing, sitting, and lying; mean duration and variability of single walking, standing, sitting, and lying bouts) were measured in the participant's home using validated wearable sensor technology. Logistic regression was used to assess the most sensitive gait, balance, and PA variables for identifying prefrail participants (vs. nonfrail). Multinomial logistic regression was used to identify variables sensitive to discriminate between three frailty levels. RESULTS: Gait speed (area under the curve, AUC = 0.802), hip sway (AUC = 0.734), and steps/day (AUC = 0.736) were the most sensitive parameters for the identification of prefrailty. Multinomial regression revealed that stride length (AUC = 0.857) and double support (AUC = 0.841) were the most sensitive gait parameters for discriminating between three frailty levels. Interestingly, walking bout duration variability was the most sensitive PA parameter for discriminating between three frailty levels (AUC = 0.818). No balance parameter discriminated between three frailty levels. CONCLUSION: Our results indicate that unique parameters derived from objective assessment of gait, balance, and PA are sensitive for the identification of prefrailty and the classification of a subject's frailty level. The present findings highlight the potential of wearable sensor technology for in-home assessment of frailty status. © 2014 S. Karger AG, Basel. Language: en <s> BIB003 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <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> BIB004 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <s> Abstract Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals’ fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4 m walk. The follow-up tests with the same protocol were performed after one year. Altogether 43 features were extracted from the accelerometer signals. Sequential forward floating selection and ten-fold cross-validation were applied to determine models for 1) estimating the outcomes of BBS, TUG and 4 m walk tests and 2) predicting decline in balance during one-year follow-up indicated as decline in BBS total score and one leg stance. Normalized root-mean-square errors (RMSE) of the assessment scale result estimates were 0.28 for BBS score, 0.18 for TUG time, and 0.22 for 4 m walk test. Area under curve (AUC) was 0.78 for predicting decline in BBS total score and 0.82 for one leg stance, respectively. The results suggest that the gait features can be used to estimate the result of a clinical balance assessment scale and predict decline in balance. A simple walk test with wearable monitoring could be applicable as an initial screening tool to identify people with early signs of balance deficits. <s> BIB005 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> One Task <s> Abstract Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting. <s> BIB006
Among the papers selected in this study, roughly 23 works include in the experimental protocol the execution of one task only. As it can be see in Table 1 , 15 papers focus on the analysis of dynamics using one task, such as a walk at self-selected velocity, the six minute walk test, falling test or the "time up and go". These tests are simple to manage, facilitate patients collaboration by being tolerable and also reflect most of the activities that are frequently performed by elderly people. Through the use of sensors, such as accelerometers or pressure sensors, the simple walk allows not only to detect the functional mobility of elderly people, but also to quantify their spatio-temporal parameters (speed, walking time, length and width stride etc.), the joint range of motion, and to identify a small set of features for the classification of falls. Indeed, Howcroft et al. BIB004 proposed the use of accelerometers and plantar pressure sensors during the 7.62 m (25 feet) test to detect, from a broader set of features, a smaller one to be used for falls classification, which could further improve falls classification. Although a greater walking distance might be a more suitable indicator of everyday walking activities, the assessment of brief gait distance is commonly used in clinical practice and could allow to find a small set of features in order to improve the clinical fall risk prediction. In addition, Similä et al. BIB005 suggested that gait characteristics can be used to estimate the outcome of clinical assessment of dynamic stability and predict balance decline. Furthermore, walking monitored by wearable sensors could be a useful screening tool to identify people with early signs of balance deficit BIB003 . As shown in Figure 7 , 28% of the papers includes the analysis of static condition by employing a single task, such as standing in an anatomical reference position, with open or closed eyes. These tasks allow for detecting age-related postural changes in antero-posterior or mid-lateral directions. In Turcato et al. BIB001 , an inertial sensor has been useful to detect the angular velocity and the linear accelerations of trunk in the static condition. The results of the study highlight that it is possible to reliably predict, with a resolution by up to 400 ms, the higher frequency CoM displacements. Since the loss of static balance occurs when the CoM goes beyond the BoS, the approach described can be useful to develop wearable devices able to warn subject of the risk of falling, so that they can implement strategies to keep the balance. Furthermore, as suggested by Gopalai et al. BIB002 , with vibrotactile feedback tools, the static evaluation of body allows to monitor and correct the postural control. In fact, they found that the use of an intelligent biofeedback system integrated by a vibrating platform is useful to improve postural control. Additionally, as it can be seen in Table 1 , two studies implement daily activities and two other studies analyze falling task. Specifically, Di Rosa et al. BIB006 evaluated the performance of free-activities of daily life, in order to develop a self-learning and wearable system to identify the walking decline and to detect the risks of falls at home. Furthermore, a recent paper  proposed a portable device able to detect falls with accuracy and to provide monitoring and timely help for the the elderly.