Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes.

Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.This work was sponsored by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF) across projects RTC-2017-6321-1 AEI/FEDER, UE, TEC2016-76021-C2-2-R AEI/FEDER, UE and PID2019-107270RB-C21/AEI/10.13039/501100011033, UE

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Background: Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective: The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods: This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results: Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions: A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients

Home-based risk of falling assessment test using a closed-loop balance model

The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly (68.4 ± 5.5 years), seven Parkinson’s disease (PD) subjects (66.28 ± 8.9 years), and twelve healthy young adults (28.27 ± 3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure (COP) measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool

Instrumented shoes for daily activity monitoring in healthy and at risk populations

Daily activity reflects the health status of an individual. Ageing and disease drastically affect all dimensions of mobility, from the number of active bouts to their duration and intensity. Performing less activity leads to muscle deterioration and further weakness that could lead to increased fall risk. Gait performance is also affected by ageing and could be detrimental for daily mobility. Therefore, activity monitoring in older adults and at risk persons is crucial to obtain relevant quantitative information about daily life performance. Activity evaluation has mainly been established through questionnaires or daily logs. These methods are simple but not sufficiently accurate and are prone to errors. With the advent of microelectromechanical systems (MEMS), the availability of wearable sensors has shifted activity analysis towards ambulatory monitoring. In particular, inertial measurement units consisting of accelerometers and gyroscopes have shown to be extremely relevant for characterizing human movement. However, monitoring daily activity requires comfortable and easy to use systems that are strategically placed on the body or integrated in clothing to avoid movement hindrance. Several research based systems have employed multiple sensors placed at different locations, capable of recognizing activity types with high accuracy, but not comfortable for daily use. Single sensor systems have also been used but revealed inaccuracies in activity recognition. To this end, we propose an instrumented shoe system consisting of an inertial measurement unit and a pressure sensing insole with all the sensors placed at the shoe/foot level. By measuring the foot movement and loading, the recognition of locomotion and load bearing activities would be appropriate for activity classification. Furthermore, inertial measurement units placed on the foot can perform detailed gait analysis, providing the possibility of characterizing locomotion. The system and dedicated activity classification algorithms were first designed, tested and validated during the first part of the thesis. Their application to clinical rehabilitation of at risk persons was demonstrated over the second part. In the first part of the thesis, the designed instrumented shoes system was tested in standardized conditions with healthy elderly subjects performing a sequence of structured activities. An algorithm based on movement biomechanics was built to identify each activity, namely sitting, standing, level walking, stairs, ramps, and elevators. The rich array of sensors present in the system included a 3D accelerometer, 3D gyroscope, 8 force sensors, and a barometer allowing the algorithm to reach a high accuracy in classifying different activity types. The tuning parameters of the algorithm were shown to be robust to small changes, demonstrating the suitability of the algorithm to activity classification in older adults. Next, the system was tested in daily life conditions on the same elderly participants. Using a wearable reference system, the concurrent validity of the instrumented shoes in classifying daily activity was shown. Additionally, daily gait metrics were obtained and compared to the literature. Further insight into the relationship between some gait parameters as well as a global activity metric, the activity âcomplexityâ, was discussed. Participants positively rated their comfort while using the system... (Please refer to thesis for full abstract

Are older people putting themselves at risk when using their walking frames?

Background Walking aids are issued to older adults to prevent falls, however, paradoxically their use has been identified as a risk factor for falling. To prevent falls, walking aids must be used in a stable manner, but it remains unknown to what extent associated clinical guidance is adhered to at home, and whether following guidance facilitates a stable walking pattern. It was the aim of this study to investigate adherence to guidance on walking frame use, and to quantify user stability whilst using walking frames. Additionally, we explored the views of users and healthcare professionals on walking aid use, and regarding the instrumented walking frames (‘Smart Walkers’) utilized in this study. Methods This observational study used Smart Walkers and pressure-sensing insoles to investigate usage patterns of 17 older people in their home environment; corresponding video captured contextual information. Additionally, stability when following, or not, clinical guidance was quantified for a subset of users during walking in an Activities of Daily Living Flat and in a gait laboratory. Two focus groups (users, healthcare professionals) shared their experiences with walking aids and provided feedback on the Smart Walkers. Results Incorrect use was observed for 16% of single support periods and for 29% of dual support periods, and was associated with environmental constraints and a specific frame design feature. Incorrect use was associated with reduced stability. Participants and healthcare professionals perceived the Smart Walker technology positively. Conclusions Clinical guidance cannot easily be adhered to and self-selected strategies reduce stability, hence are placing the user at risk. Current guidance needs to be improved to address environmental constraints whilst facilitating stable walking. The research is highly relevant considering the rising number of walking aid users, their increased falls-risk, and the costs of falls. Trial Registration Not applicable

A generalizable methodology for stability assessment of walking aid users

To assist balance and mobility, older adults are often prescribed walking aids. Nevertheless, surprisingly their use has been associated with increased falls-risk. To address this finding we first need to characterise a person’s stability while using a walking aid. Therefore, we present a generalizable method for the assessment of stability of walking frame (WF) users. Our method, for the first time, considers user and device as a combined system. We define the combined centre of pressure (CoPsystem) of user and WF to be the point through which the resultant ground reaction force for all feet of both the WF and user acts if the resultant moment acts only around an axis perpendicular to the ground plane. We also define the combined base of support (BoSsystem) to be the convex polygon formed by the boundaries of the anatomical and WF feet in contact with the ground and interconnecting lines between them. To measure these parameters we have developed an instrumented WF with a load cell in each foot which we use together with pressure-sensing insoles and a camera system, the latter providing the relative position of the WF and anatomical feet. Software uses the resulting data to calculate the stability margin of the combined system, defined as the distance between the CoPsystem and the nearest edge of the BoSsystem. In addition, our software calculates the weight supported through the frame and when the feet (user and/or frame) are on the floor. Our pilot work showed feasibility for our approach

Doctor of Philosophy

dissertationThis thesis analyzed biped stability through a qualitative likelihood of falling and quantitative Potential to Fall (PF) analysis. Both analyses were applied to walking and skiing to better understand behaviors across a wider spectrum of bipedal gaits. For both walking and skiing, two types of locomotion were analyzed. Walking studies compared normal locomotion (gait) to an unexpected slip. Skiing studies compared wedge style locomotion (more common to beginning and intermediate skiers) to parallel style locomotion (more common to advanced and expert skiers). Two mediums of data collection were used. A motion capture laboratory with stereographic cameras and force plates were used for walking studies, and instrumented insoles, capable of force and inertial measurement, were used for skiing studies. Both kinematics and kinetics were used to evaluate the likelihood of falling. The PF metric, based on root mean squared error, was used to quantify the likelihood of falling for multiple subjects both in walking and skiing. PF was based on foot kinematics for walking and skiing studies. PF also included center of pressure for skiing studies. The PF was lower for normal gaits in walking studies and wedge style locomotion for skiing studies

Développement et étude de la validité d'une semelle instrumentée pour le comptage de pas

Les semelles instrumentées sont des dispositifs pouvant être utilisées pour la quantification de pas et la reconnaissance des activités. Il existe plusieurs modèles de semelles instrumentées, avec des niveaux de validité variables. Ce mémoire comprend trois objectifs : 1) faire une revue systématique de la littérature sur la validité de critère des semelles instrumentées existantes pour identifier les postures, les types d’activités et compter les pas, 2) développer une semelle instrumentée et 3) étudier sa validité pour le comptage de pas. Pour l’objectif 1, cinq bases de données ont permis de sélectionner 33 articles sur la validité de critère de seize modèles de semelles instrumentées pour la détection de posture, de type d’activités et de pas. Selon les indicateurs utilisés, les validités de critère varient de 65,8% à 100% pour la reconnaissance des activités et des postures et de 96% à 100% pour la détection de pas. En somme, peu d’études ont utilisé les semelles instrumentées pour le comptage de pas bien qu’elles démontrent une très bonne validité. Pour les objectifs 2 et 3, nous avons équipé une semelle commercialisée de cinq capteurs de pression et testé trois méthodes de traitement des signaux de pression pour la quantification de pas. Ces trois méthodes sont basées sur le signal de chaque capteur de pression, la moyenne ou la somme cumulée des cinq signaux de pression. Les résultats ont montré que notre semelle instrumentée détectait le pas avec un taux de succès de 94,8 ± 9,4% à 99,5 ± 0,4% à des vitesses de marche confortable et de 97,0 ± 6,2% à 99,6 ± 0,4% à des vitesses de marche rapide à l’intérieur et à l’extérieur d’un bâtiment avec les trois méthodes. Toutefois, la méthode basée sur la somme cumulée avait les niveaux de précision plus élevés pour le comptage de pasInstrumented insoles are devices which can be used for quantifying steps and recognizing activities. Validity of many instrumented insoles varies from medium to high. This thesis has three objectives: 1) to systematically review the literature on the validity of existing instrumented insoles for posture, type of activities recognition, and step counting, 2) to develop an instrumented insole and 3) to study its criterion validity for step counting. For objective 1, five databases were used to select 33 articles on criterion validity of sixteen insole models for posture and type of activities recognition, and step detection. According to indicators used, validity values vary from 65.8% to 100% for activities and postures recognition and from 96% to 100% for detection of steps. In summary, few studies have used instrumented insoles for steps counting even though they demonstrated a very good validity. For objectives 2 and 3, we equipped a commercialized insole with five pressure sensors and tested three pressure signal processing methods for step quantification. These three methods are based on signal of each pressure sensor, average or cumulative sum of five pressure signals. Results showed that our instrumented insole detected steps with a success rate varying from 94.8 ± 9.4% to 99.5 ± 0.4% at self-selected walking speeds and from 97.0 ± 6.2% to 99.6 ± 0.4% at maximal walking speeds in indoor and outdoor settings with all three processing methods. However, cumulative sum method had the highest levels of accuracy for step counting