Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Biomechanical gait pattern changes associated with functional fitness levels and falls in the elderly

Doutoramento em Motricidade Humana na especialidade de BiomecânicaThis thesis aimed to provide a better understanding on the determinant factors for falling in Portuguese older adults, with a special emphasis on the biomechanical changes in gait patterns associated with the functional fitness decline in this population. Our methodological approach to this problem encompassed two different levels of analysis: in the first part two epidemiological studies were conducted in order to establish the determinant factors for falling within the Portuguese older adults; in the second part three laboratory-based studies were performed in order to determine the influence of functional fitness levels on elderly gait patterns. Falls were shown to result from the interaction of many risk factors. Within these, gender, functional fitness level and health parameters were found to be the strongest fall determinants. Interestingly, age was not a determinant factor for falling, even within very old individuals (≥75 years or ≥80 years). Therefore, in the subsequent studies, the gait patterns of a subgroup of older adults, who had participated in the epidemiological studies, were characterized according with their functional fitness levels. The results showed that older subjects with a lower functional fitness level score, consistently re-distribute lower limb joint moments while performing different locomotor tasks (walking, stair ascent and stair descent). Because the success of physical activity interventions aiming at falls and disability prevention is dependent on subgroup characterization, these biomechanical gait pattern changes may yield important information for the health and exercise professionals working with the elderly.RESUMO: A presente dissertação objetiva o aprofundamento do conhecimento sobre os determinantes das quedas na população idosa portuguesa, com especial enfoque nas alterações biomecânicas nos padrões de marcha associadas ao declínio funcional característico desta população. A abordagem metodológica preconizada para a análise do problema compreende duas fases complementares: uma primeira fase, que englobou dois estudos epidemiológicos com o objetivo de estabelecer os fatores determinantes de quedas na população idosa portuguesa; uma segunda fase, onde foram considerados três estudos experimentais (laboratoriais), com o propósito de determinar a influência de diferentes níveis de aptidão funcional nos padrões de marcha desta população. Os resultados demonstraram que as quedas resultam da interação de diversos fatores de risco, destacando-se os seguintes: género, parâmetros de aptidão funcional e de saúde. De relevar que o fenómeno de queda se revelou independente da idade, mesmo quando analisada a sua associação com os fatores determinantes em grupos etários mais avançados (≥75 e ≥80 anos). Neste sentido, nos estudos subsequentes, foram analisados os padrões de marcha de subgrupos de idosos recrutados do grupo de participantes dos estudos anteriores e estratificados em função do seu nível de aptidão funcional. Observou-se então que os idosos com baixos níveis de aptidão funcional adotavam estratégias consistentes de redistribuição dos momentos de força articulares dos membros inferiores, aquando da execução de diferentes tarefas locomotoras (marcha, subir e descer escadas). Considerando o sucesso demonstrado das intervenções sustentadas em programas de atividade física para a prevenção de quedas e incapacidade, as alterações biomecânicas dos padrões de marcha observadas poderão constituir um importante suporte informacional para os profissionais de saúde e exercício que trabalham com a população idosa.FCT - Fundação para a Ciência e a Tecnologi

Feasibility and efficacy of incorporating an exoskeleton in gait training during subacute stroke rehabilitation

Introduction: Hemiparesis is the most common acute manifestation of stroke and often has a strong negative impact on walking ability leaving one third of patients dependent in walking activities outside one’s home. Improved methods for training of gait during stroke rehabilitation could tackle the challenge of achieving independent walking and promote better outcomes. Several studies have explored the value of introducing electromechanical gait machines in stroke rehabilitation to enhance gait training. One example is the exoskeleton Hybrid Assistive Limb (HAL). The HAL system has been found feasible to use during rehabilitation in the chronic stage after stroke, however knowledge of the feasibility in the subacute stage after stroke and its efficacy compared to evidence-based conventional gait training is still limited. Aim: The overall aim of this thesis was to evaluate the safety and feasibility of HAL for gait training in the subacute stage after stroke and the effect of HAL training on functioning, disability and health compared to conventional gait training, as part of an inpatient rehabilitation program in patients with severe limitations in walking in the subacute stage after stroke. Methods: This thesis contains two studies where one is a safety and feasibility study (Study I) and one is a prospective, randomized, open labeled, blinded evaluation study (Study II). In Study I, eight patients performed HAL training 5 days/week. The number of training sessions were adjusted individually and varied from 6 to 31 (median 16). Safety and feasibility aspects of the training were evaluated as well as clinical outcomes on functioning and disability (e.g. independence in walking, walking speed, balance, movement functions and activities of daily living), assessed before and after the intervention period. In Study II, 32 patients were randomized to either conventional training only or HAL training in addition to the conventional training, 4 days per week for 4 weeks. Within and between- group differences in independence in walking, walking speed/endurance, balance, movement functions and activities of daily living were investigated before and after the intervention period, as well as 6 months post stroke. In addition, gait pattern functions were evaluated after the intervention in a three-dimensional gait laboratory. At 6 months post stroke self- perceived aspects on functioning disability and health were assessed and subsequently correlated to the clinical assessments. Results: In Study I HAL was found to be safe and feasible for gait training after stroke in patients with hemiparesis, unable to walk independently, undergoing an inpatient rehabilitation program. All patients improved in walking independence and speed, movement function, and activities of daily living during the intervention period. In addition, it was found that patients walked long distances during the HAL sessions, suggesting that HAL training may be an effective method to enhance gait training during rehabilitation of patients in the subacute stage after stroke. In Study II substantial but equal improvements in the clinically evaluated outcomes in the two intervention groups were found. At six months post stroke, two thirds of patients were independent in walking, and a younger age but not intervention group served as the best predictor. Gait patterns were similarly impaired in both groups and in line with previous reports on gait patterns post stroke. Further, self-perceived ratings on functioning, disability and health were explained by the ability to perform self-care activities and not by intervention group. Conclusion: To incorporate gait training with HAL is safe and feasible during inpatient rehabilitation in the subacute stage after stroke and may be a way to increase the dose (i.e. number of steps) in gait training in the subacute stage after stroke. Among these included younger patients with hemiparesis and severe limitations in walking in the subacute stage after stroke, substantial improvements in body function and activity as well as equally impaired gait patterns were observed both after incorporated HAL training and after conventional gait training only, but without between-group differences. In future studies, potential beneficial effects on cardiovascular, respiratory, and metabolic functions should be addressed. Further, as the stroke population is heterogeneous, potential subgroups of patients who may benefit the most from electromechanically-assisted gait training should be identified

Physical Activity Classification Meeting Daily Life Conditions for Older Subjects

Physical inactivity can lead to several age-related issues such as falls, movement disorders and loss of independence in older adults. Therefore, promoting physical activity in daily life and tracking daily life activities are essential components for healthy aging and wellbeing. Recent advances in the MEMS devices make it happen to wirelessly integrate miniature motion capturing devices and use them in personal health care and physical activity monitoring systems in daily life conditions. Consequently, various systems have been developed to classify the activities of daily living. However, the scope and implementation of such systems are limited to laboratory-based investigations and they are mainly developed utilizing the sample population of younger adults. Therefore, this dissertation aims to develop innovative solutions for physical activity classification, with a specific focus on the elderly population in free-living conditions

Development and validation of a newtest for assessment of plantar-flexor muscle strength in older adults

Background: The Calf-raise (CR) test is often used as a screening tool to assess anklemuscle functioning in clinical practice. Most studies restrict the administration of thistest to the young adult population and, of our knowledge, no study has evaluated thevalidityand reliability of this test with elderly people. Purpose: This study aimed to develop a new field test protocol with a standardizedmeasurement of strength and power in plantar flexor muscles targeted to functionallyindependent older adults, the calf-raise senior (CRS) test, and also evaluate its reliabilityand validity. Patients and methods: Forty-one subjects aged 65 years and older of bothsexesparticipated in five different cross-sectional studies: 1) pilot (n=12); 2) inter and intra-rater agreement (n=12); 3) construct (n=41); 4) criterion validity (n=33); and 5) test–retest reliability (n=41). Different motion parameters were compared in order to defineaspecifically designed protocol for seniors. Two raters evaluated each participant twice, and the results of the same individual were compared between raters and participantstoassess the interrater and intra-rater agreement. The validity and reliability studies involvedthree testing sessions that lasted 2 weeks, including a battery of functional fitness tests, CRS test in two occasions, accelerometry, and strength assessments in an sokineticdynamometer. Results: The CRS test presented an excellent test–retest reliability (intra-class correlationcoefficient [ICC] =0.90, standard error of measurement =2.0) and interrater reliability(ICC=0.93–0.96), as well as a good intra-rater agreement (ICC =0.79–0.84). Participantswithbetter results in the CRS test were younger and presented higher levels of physical activity and functional fitness. A significant association between test results andall strength parameters (isometric, r=0.87, r=0.75; isokinetic, r=0.86, r=0.74; and rateof orcedevelopment, r=0.77, r=0.59) was shown. Conclusion: This study was successful in demonstrating that the CRS test can meet thescientific criteria of validity and reliability. The test can be a good indicator of anklestrength in older adults and proved to discriminate significantly between individualswithimproved functionality and levels of physical activity.Background: The assessment of the plantar-flexors muscle strength in the elderly peopleis of the utmost importance since they are strongly associated o the performanceof fundamental tasks of daily life. Purpose: Our study aims at strengthen the validity of the Calf-Raise Senior (CRS) test byassessing the biomechanical movement pattern of calf muscles in elderly participantswithdifferent functional fitness profiles. Patients and methods: Twenty-six older adults with different levels of functional fitness(FF) and physical activity (PA) participated in this study. CRS test was administered together with a FF battery, accelerometry, strength tests, kinematics and electromyography (EMG). Older adults with the best and worst CRS scores were compared and the associationbetween the scores and EMG pattern of ankle muscles was determined. Results: Older participants with the best CRS scores presented higher levels of FF, PA, strength, power, speed and range of movement, and also a more efficient movement pattern during the test. Subjects who scored more at the CR test demonstratedthepossibly to use a stretch-shortening cycle type of action in the PF muscles to increasepower during the movements. Conclusion: Older adults with different levels of functional fitness can be tratifiedbythemuscular activation pattern of the calf muscles and the scores in CRS test. . This studyreinforced the validity of CRS for evaluating ankle strength and power in elderlyBackground: Mobility significantly depends on the ankle muscles’ strength, whichisparticularly relevant for the performance of daily activities. There are few tools available, with all of the measurement properties tested, to assess ankle strength. Purpose: The purpose of this study was to test the responsiveness of Calf-RaiseSenior Test (CRS) in a sample of elderly participants undergoing a 24 weeks communityexercise program.. Patients and methods: 82 older adults participated in an exercise programandwereassessed with CRS Test and 30-seconds chair stand test (CS) at baseline and at follow- up. Effect size (ES), standardized response mean (SRM) and minimal detectablechange(MDC) measures were calculated for the CRS and CS tests scores. ROC curves analysiswas used to define a cut-off representing the minimally important difference of Calf-RaiseSenior test. Results: Results revealed a small (ES = 0.42) to moderate (SRM = 0.51) responsivenessin plantar-flexion strength and power across time, which was lower than that of CStest (ES = 0.64, SRM = 0.67). The responsiveness of CRS test was more evident in groupsof subjects with lower initial scores. A minimal important difference (MID) of 3.5 repetitionsand a minimal detectable change (MDC) of 4.6 was found for the CRS. Conclusion: Calf-Raise Senior Test is a useful field test to assess elderly ankle function, with moderate responsiveness properties. The cutoff scores of MDC and MIDpresentedin this study can be useful in determining the success of interventions aiming at improvingmobility in senior participants

Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults

Wearable technologies allow the measurement of unhindered activities of daily living (ADL) among patients who had a stroke in their natural settings. However, methods to extract meaningful information from large multi-day datasets are limited. This study investigated new visualization-driven time-series extraction methods for distinguishing activities from stroke and healthy adults. Fourteen stroke and fourteen healthy adults wore a wearable sensor at the L5/S1 position for three consecutive days and collected accelerometer data passively in the participant’s naturalistic environment. Data from visualization facilitated selecting information-rich time series, which resulted in classification accuracy of 97.3% using recurrent neural networks (RNNs). Individuals with stroke showed a negative correlation between their body mass index (BMI) and higher-acceleration fraction produced during ADL. We also found individuals with stroke made lower activity amplitudes than healthy counterparts in all three activity bands (low, medium, and high). Our findings show that visualization-driven time series can accurately classify movements among stroke and healthy groups using a deep recurrent neural network. This novel visualization-based time-series extraction from naturalistic data provides a physical basis for analyzing passive ADL monitoring data from real-world environments. This time-series extraction method using unit sphere projections of acceleration can be used by a slew of analysis algorithms to remotely track progress among stroke survivors in their rehabilitation program and their ADL abilities

Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment System-Home Care (RAI-HC)

Background: Falls are a serious problem especially in the aging population. To accurately identify individuals at risk for falls and mitigate the devastating effects caused by falls has become prominent to geriatrics and public health agencies. Leveraging wearable technologies and clinical assessment information may improve fall risk classification. Objectives: The overall objectives of this thesis project are to: (1) investigate the similarities and differences in physical activity (PA), heart rate (HR) and night sleep (SP) in a sample of community-dwelling older adults with varying fall histories, using a smart wrist-worn device; and (2) examine the risk factors for falls in the target population, create fall risk classification models and evaluate classification performances based on: i) wearable data, ii) the Resident Assessment Instrument for Home Care (RAI-HC), and iii) the combination of wearable data and the RAI-HC system. Methods: Two parallel studies were conducted in this project. Study I was a community-based cross-sectional study, utilizing the RAI-HC system to examine the risk factors for falls in older people. In the primary analysis, the ordinal attribute of previous falls (0, 1, and ≥ 2) was used as the outcome variable to build the proportional odds models (POM) for ordinal logistic regression. In the secondary analysis, the binary attribute of falls (yes/no) was used to distinguish fallers and non-fallers. Study II, a prospective, observational study was conducted to investigate the similarities and differences among three independent faller groups (non-fallers, single fallers, and recurrent fallers) based on the number of previous falls in a sample of older adults living in community, with continuous measurements of PA, HR and SP using a smart wearable device. Descriptive statistics and simple statistical analyses were conducted to test the differences between groups. The wearable and RAI-HC assessment data were further analyzed and utilized to create fall risk classification models, with two supervised machine learning algorithms: logistic regression (LR) and decision tree (DT). The calculation of a set of performance metrics was performed to evaluate the classification performance of each final model. Results: Study I: Of 167,077 individuals aged ≥ 65 in the RAI-HC data set, 113,529 (68.0%) had no history of falls, 27,320 (16.4%) had one fall, and 26,226 (15.7%) experienced multiple (≥ 2) falls. Unsteady gait, Activities of Daily Living (ADL) decline, ADL self-performance on transfer dependency, short-term memory problem, primary modes of locomotion (indoors), stair climbing, bladder continence, and limit going outdoors due to fear of falling were significant predictors of fall risk in both human and computer feature selection models derived from the Minimum Data Set-Home Care (MDS-HC). The Method of Assigning Priority Levels (MAPLe) (1 vs. 5: odds ratio (OR) = 0.20; 95% confidence internal (CI), 0.18-0.22), Changes in Health, End-Stage Disease, Signs, and Symptoms (CHESS) (0 vs. 5: OR = 0.27; 95% CI, 0.21-0.36), ADL Clinical Assessment Protocol (CAP) (0 vs. 2: OR = 0.21; 95% CI, 0.20-0.22), Cognitive CAP (0 vs. 2: OR = 0.33; 95% CI, 0.31-0.35), and Urinary Incontinence CAP (3 vs. 0: OR = 1.77; 95% CI, 1.62-1.94) were strong predictors in classifying older people with past fall histories based on the CAPs and a variety of summary scales and algorithms available within the RAI-HC assessment. The POM built on all available items on the RAI-HC data set achieved the best performance in classifying the three faller groups, with overall classification accuracy of 71.5%, and accuracies of 93.3%, 5.5% and 46.0% in classifying the non-faller, single faller and recurrent faller group, respectively. Likewise, the logistic regression model built on all available RAI-HC items achieved the best performance in distinguishing fallers and non-fallers, with the highest overall classification accuracy of 75.1%, the largest area under the curve (AUC) of 0.769, and the lowest Brier score of 0.171. Study II: Of 40 participants aged 65-93, 16 (40%) had no previous falls, while 8 (20%) and 16 (40%) had experienced one and multiple (≥ 2) falls, respectively. The wearable components of PA measurements extracted from the smart wrist-worn device were significantly different among three faller groups. Daily walking HR and daily activity time were identified as the best subset of predictors of fall risk with wearable data. Classification models derived from the RAI-HC data set containing 40 participants’ latest assessments outperformed those based on wearable data only. The best classification model was a decision tree based on the combination of both data sets with 80.0% of overall classification accuracy, and accuracies of 87.5%, 50.0% and 87.5% in classifying the non-faller, single faller and recurrent faller group, respectively. Conclusions: Continuous measurements of PA, HR and SP appear to supplement the RAI-HC system in facilitating fall risk stratification. Future fall risk assessment studies should consider leveraging wearable technologies to supplement resident assessment instruments

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions

Research on mobility in older adults to improve the fall risk screening in physiotherapy

Hintergrund Sturzprävention ist eine gesundheitspolitische Herausforderung in einer alternden Gesellschaft. Es ist für viele Bereiche der Versorgungsforschung von hohem Interesse, Prä-diktoren für Stürze zu identifizieren, um wiederum die Einleitung geeigneter Präventionsmaß-nahmen zu ermöglichen und die Versorgungsqualität zu verbessern. Die vorliegende Arbeit soll einen Beitrag zum Sturzrisikoscreening bei älteren Menschen in der Physiotherapie leisten. Methodik Drei Publikationen aus drei wissenschaftlichen Projekten wurden in die vorliegende Dissertation einbezogen. Methodisch folgen alle drei Ansätze einem quantitativen Verfahren. Zwei Aspekte der funktionellen Mobilität - das Treppensteigen und das Gehen in der Ebene - sowie ein psychischer Aspekt, die Sturzangst, wurden im Fokus der vorliegenden Dissertation betrachtet: Ziel des ersten Projektes war es, einen Beitrag zur Analyse von Gangmustern mittels moderner Sensortechnologie zu leisten. Hierfür wurde die grundsätzliche Eignung eines intelli-genten Fußbodensensors, des SensFloor® der Firma FutureShape GmbH, für den klinischen Be-reich der Ganganalyse kritisch überprüft. Junge, gesunde Proband*innen gingen wiederholt über den SensFloor®, um ein künstliches neuronales Netzwerk mit diesen Gangdaten zu trainie-ren. Ziel der zweiten Studie war es, die Treppensteigegeschwindigkeit in einer Kohorte älterer stationärer Patient*innen sowie einer Kohorte älterer Menschen ohne funktionelle Beeinträch-tigungen zu untersuchen. Hierfür stiegen die Studienteilnehmer*innen einen Treppenabsatz von 13 Stufen hinauf und wieder hinunter. In der dritten Studie wurden für den „Survey of Acti-vities and Fear of Falling in the Elderly“-Fragebogen Grenzwerte für die Einteilung in niedrige, moderate und hohe Sturzangst ermittelt. Grundlage waren die Daten aus einer Kohorte 98 älte-rer stationärer Patient*innen. Ergebnisse Die SensFloor-Technologie ist lernfähig und geeignet, um zwischen unterschiedli-chen Gangmodi zu differenzieren. Die Test-Retest-Analyse der Treppensteigegeschwindigkeit lieferte moderate bis exzellente Ergebnisse. Die Analyse des Sturzangstscores zeigte, dass die optimalen Grenzwerte zur Klassifikation niedriger, moderater und hoher Sturzangst bei 0,6 und 1,4 liegen. Schlussfolgerungen Mit der Anwendung der Sensfloor-Technologie, der Treppensteige-schwindigkeit in Stufen pro Sekunde sowie der Klassifikation der Sturzangst bietet die vorliegen-de Arbeit drei neue Ansätze, welche beim Sturzrisikoscreening sowohl im klinischen Setting als auch in der Forschung zukünftig eine stärkere Beachtung finden sollten.Background In an aging society fall prevention is a focal point in healthcare policy. It is of high in-terest to identify predictors of falls, in order to initiate appropriate preventive measures and to im-prove the quality of care. It is the purpose of this thesis to make a contribution to fall risk screening in the elderly in physical therapy. Methods Three publications resulting from three scientific projects were included in this disserta-tion. Methodologically, all three approaches follow a quantitative method. Two aspects of func-tional mobility - stair climbing and walking on level ground - as well as a psychological aspect, fear of falling, are in the focus of the present thesis. The aim of the first publication was the examination of gait patterns using modern sensor technology. For this purpose, the eligibility of an intelligent floor, the SensFloor® by the FutureShape company, was critically reviewed for the clinical field of gait analysis. Young healthy participants walked over the SensFloor® repeatedly in order to train an artificial neural network with this gait data. The aim of the second study was to investigate stair climbing speed in a cohort of older hospitalized patients and a cohort of older adults without func-tional impairments. For this purpose, the participants climbed up and down a flight of 13 steps. In the third study classification schemes for low, moderate, and high fear of falling were calculated using the “Survey of Activities and Fear of Falling in the Elderly“ (SAFE). For this, data from a cohort of 98 older hospitalized patients was analyzed. Results The SensFloor technology is capable of learning and able to differentiate various gait modes. Test-retest analysis of stair climbing speed provided moderate to excellent results. Analysis of the fear of falling score for classifying low, moderate, and high fear of falling resulted in optimal cut-off points with .6 and 1.4. Conclusions With the application of SensFloor technology, stair climbing speed in steps per second and classification of fear of falling, the present thesis offers three new approaches that should re-ceive more attention in fall risk screening. The results obtained should be considered in both the clinical setting and clinical research

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