Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Background: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods: The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results: From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions: We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders

Validation, optimization and exploitation of orientation measurements issued from inertial systems for clinical biomechanics

Les centrales inertielles (triade de capteurs inertiels dont la fusion des données permet l’estimation de l’orientation d’un corps rigide) sont de plus en plus populaires en biomécanique. Toutefois, les qualités métrologiques des centrales inertielles (CI) sont peu documentées et leur capacité à identifier des incapacités liées à la mobilité, sous-évaluée. Objectifs : (i) Caractériser la validité de la mesure d’orientation issue de CI ; (ii) Optimiser la justesse et la fidélité de ces mesures; et (iii) Proposer des métriques de mobilité basées sur les mesures d’orientation issues de CI. Méthodologie et résultats : La validité de la mesure d’orientation de différents types de CI a d’abord été évaluée en conditions contrôlées, à l’aide d’une table motorisée et d’une mesure étalon. Il a ainsi été démontré que les mesures d’orientation issues de CI ont une justesse acceptable lors de mouvements lents (justesse moyenne ≤ 3.1º), mais que cette justesse se dégrade avec l’augmentation de la vitesse de rotation. Afin d’évaluer l’impact de ces constatations en contexte clinique d’évaluation de la mobilité, 20 participants ont porté un vêtement incorporant 17 CI lors de la réalisation de diverses tâches de mobilité (transferts assis-debout, marche, retournements). La comparaison des mesures des CI avec celles d’un système étalon a permis de dresser un portrait descriptif des variations de justesse selon la tâche exécutée et le segment/l’articulation mesuré. À partir de ces constats, l’optimisation de la mesure d’orientation issue de CI est abordée d’un point de vue utilisateur, démontrant le potentiel d’un réseau de neurones artificiel comme outil de rétroaction autonome de la qualité de la mesure d’orientation (sensibilité et spécificité ≥ 83%). Afin d’améliorer la robustesse des mesures de cinématique articulaire aux variations environnementales, l’ajout d’une photo et d’un algorithme d’estimation de pose tridimensionnelle est proposé. Lors d’essais de marche (n=60), la justesse moyenne de l’orientation à la cheville a ainsi été améliorée de 6.7° à 2.8º. Finalement, la caractérisation de la signature de la cinématique tête-tronc pendant une tâche de retournement (variables : angle maximal tête-tronc, amplitude des commandes neuromusculaires) a démontré un bon pouvoir discriminant auprès de participants âgés sains (n=15) et de patients atteints de Parkinson (PD, n=15). Ces métriques ont également démontré une bonne sensibilité au changement, permettant l’identification des différents états de médication des participants PD. Conclusion : Les mesures d’orientation issues de CI ont leur place pour l’évaluation de la mobilité. Toutefois, la portée clinique réelle de ce type de système ne sera atteinte que lorsqu’il sera intégré et validé à même un outil de mesure clinique.Abstract : Inertial measurement of motion is emerging as an alternative to 3D motion capture systems in biomechanics. Inertial measurement units (IMUs) are composed of accelerometers, gyroscopes and magnetometers which data are fed into a fusion algorithm to determine the orientation of a rigid body in a global reference frame. Although IMUs offer advantages over traditional methods of motion capture, the value of their orientation measurement for biomechanics is not well documented. Objectives: (i) To characterize the validity of the orientation measurement issued from IMUs; (ii) To optimize the validity and the reliability of these measurements; and (iii) To propose mobility metrics based on the orientation measurement obtained from IMUs. Methods and results: The criterion of validity of multiple types of IMUs was characterized using a controlled bench test and a gold standard. Accuracy of orientation measurement was shown to be acceptable under slow conditions of motion (mean accuracy ≤ 3.1º), but it was also demonstrated that an increase in velocity worsens accuracy. The impact of those findings on clinical mobility evaluation was then assessed in the lab, with 20 participants wearing an inertial suit while performing typical mobility tasks (standing-up, walking, turning). Comparison of the assessed IMUs orientation measurements with those from an optical gold standard allowed to capture a portrait of the variation in accuracy across tasks, segments and joints. The optimization process was then approached from a user perspective, first demonstrating the capability of an artificial neural network to autonomously assess the quality of orientation data sequences (sensitivity and specificity ≥ 83%). The issue of joint orientation accuracy in magnetically perturbed environment was also specifically addressed, demonstrating the ability of a 2D photograph coupled with a 3D pose estimation algorithm to improve mean ankle orientation accuracy from 6.7° to 2.8º when walking (n=60 trials). Finally, characterization of the turn cranio-caudal kinematics signature (variables: maximum head to trunk angle and neuromuscular commands amplitude) has demonstrated a good ability to discriminate between healthy older adults (n=15) and early stages of Parkinson’s disease patients (PD, n=15). Metrics have also shown a good sensitivity to change, enabling to detect changes in PD medication states. Conclusion: IMUs offer a complementary solution for mobility assessment in clinical biomechanics. However, the full potential of this technology will only be reached when IMUs will be integrated and validated within a clinical tool

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

PD_manager: an mHealth platform for Parkinson's disease Management

Parkinson’s disease (PD) current clinical management is mostly based on patient’s subjective report about the effects of treatments and on medical examinations that unfortunately represent only a snapshot of a highly fluctuating clinical condition. This traditional approach requires time, it is biased by patient’s judgment and is often not completely reliable, especially in moderate advanced stages. The main purpose of the EU funded project PD_manager (Horizon 2020, Grant Agreement n° 643706) is to build and evaluate an innovative, mHealth, patient-centric system for PD remote monitoring. After a first phase of research and development, a set of wearable devices has been selected and tested on 20 patients. The raw data recorded have been used to feed algorithms necessary to recognize motor symptoms. In parallel, other applications have been developed to test also the main non-motor symptoms. On a second phase, a case- control randomized multicentric study has been designed and performed to assess the acceptability and utility of the PD_manager system at patients’ home, compared to the current gold standard for home monitoring, represented by symptoms diaries. 136 couples of patients and caregivers have been recruited, and at the end of the trial the system was found to be very well tolerated and easy to use, compared to diaries. The developed System is able to recognize motor and non-motor symptoms, helping healthcare professionals in taking decisions on therapeutic strategies. Moreover, PD_manager could represent a useful tool for patient's self-monitoring and self-care promotion

Functional mobility in Parkinson’s disease

Introduction: Parkinson’s disease (PD) is the second most common neurodegenerative disease, affecting 1% of the world population over the age of 60. The presence of a large and heterogeneous spectrum of motor and non-motor symptoms, some resistant to levodopa therapy, is usually a major source of disability that affects patients’ daily activities and social participation. Functional mobility (FM) is an outcome that merges the concepts of function with mobility, autonomy, and the accomplishment of daily tasks in different environments. Its use in PD studies is common. However, several aspects associated with its application in PD remain to be defined, hampering a wider use of the concept in clinical practice and the comparison of clinical study results. Aim: This thesis aimed to provide evidence on the appropriateness of the concept of FM in the PD field. A two-fold approach was used to this end: 1) To investigate the clinical and research applicability of the concept of FM in PD; 2) To identify the most suitable clinical and technological outcome measures for evaluating the response of PD patients’ FM to a therapeutic intervention. Methods: A narrative review using the framework of the International Classification of Functioning, Disability, and Health (ICF) was performed to explore the concept of FM when applied to PD. This first study aimed to provide a better understanding of the interaction between PD symptoms, FM, and patients’ daily activities and social participation. To identify and recommend the most suitable outcome measures to assess FM in PD, a systematic review was conducted using the CENTRAL, MEDLINE, Embase, and PEDro databases, from their inception to January 2019. During this review, we also explored the different definitions of FM present in the literature, proposing the one we believed should be established as the definition of FM in the PD field. We then conducted a focus group to explore PD patients' and health professionals’ perspectives on the proposed definition. Part of the scope of the focus group was also to investigate the impact of FM problems on patients’ daily living and the strategies used to deal with this. The study included four focus groups, two with patients (early and advanced disease stages), and two with health professionals (neurologists and physiotherapists). A second systematic review using the CENTRAL, MEDLINE, Embase, and PEDro databases, from their inception to September 2019, was performed to summarize and critically appraise the published evidence on PD spatiotemporal gait parameters. Finally, a pragmatic clinical study was conducted to identify the clinical and technological outcome measures that better predict changes in FM, when patients are submitted to a specialized multidisciplinary program for PD. Results: All the definitions found in an open search of the literature on the FM concept included three key aspects: gait, balance, and transfers. All participants in the focus group study were able to present a spontaneous definition of FM that matched the one used by the authors. All also agreed that FM reflects the difficulties of PD patients in daily life activities. Early-stage PD patients mentioned needing more time to complete their usual tasks, while advanced-stage PD patients considered FM limitations as the main limiting factor of daily activities, especially in medication “OFF” periods. Physiotherapists maintained that the management of PD FM limitations should be a joint work of the multidisciplinary team. For neurologists, FM may better express patients’ perception of their overall health status and may help to adopt a more patient-centered approach. Of the 95 studies included in the systematic review aiming to appraise the outcome measures that have been used to assess FM in PD patients, only one defined the concept of FM. The most frequent terms used as synonyms of FM were mobility, mobility in association with functional activities/performance, motor function, gait-related activity, or balance. In the literature, the Timed Up and Go (TUG) test was the most frequently reported tool used as a single instrument to assess FM in PD. The changes from baseline in the TUG Cognitive test, step length, and free-living step time asymmetry were identified as the best predictors of TUG changes. Conclusion: The information generated by the different studies included in this thesis revealed FM as a useful concept to be adopted in the PD field. FM was shown to be a meaningful outcome (for patients and health professionals), easy to measure, and able to provide more global and ecological information on patients’ daily living performances. Our results support the use of FM for PD assessment and free-living monitoring, as a way to better understand and address patients’ needs. The changes in the TUG Cognitive test, the supervised step length, and the free-living step time asymmetry seem the most suitable outcomes to measure an effect in FM. Future research should focus on determining the severity cut-off for FM changes, the minimal clinical important difference (MCID) for each of these outcome measures and resolve the current obstacles to the widespread use of technological assessments in PD clinical practice and research

Semi-automatic falls risk estimation of elderly adults using single wrist worn accelerometer

PhD ThesisThe population of the oldest old (aged 85 years and over) is growing. It is estimated that 30% of the adults over the age of 65 years experience falls at least once a year. This figure rises to 50% per annum for adults over 80 years living either at home or in care home. Currently older people are the fastest growing segment of the population. In the UK alone, the proportion of people aged 85 years old has increased from 2% to 4% in the past six decades. This marked increase in growth of population aged over 85 years is expected to have substantial impact on overall falls rate and pose serious issues to meet care needs for social and health care departments. In the light of such negative consequences for the faller and the associated costs to society, simple and quantitative techniques for falls risk screening can contribute significantly. This study describes a semi-automated technique to estimate falls risk of community dwelling elderly adults (aged 85 and over). This study presents the detailed analysis of tri-axial accelerometer movement data recorded from the right wrist of individuals undertaking the Timed Up and Go (TUG) test. The semi-automated assessment is evaluated here on 394 subjects’ data collected in their home environment. The study compares logistic regression models developed using accelerometer derived features against the traditional TUG measure ‘time taken to complete the test’. Gender based models were built separately across two groups of participants- with and without walking aid. The accelerometer derived feature model yielded a mean sensitivity of 63.95%, specificity of 63.51% and accuracy of 66.24% based on leave one-out cross validation compared to manually timed TUG (mean sensitivity of 52.64%, specificity of 45.41% and accuracy of 55.22%). Results show that accelerometer derived models offer improvement over traditional falls assessment. This automated method enables identification of older people at risk of falls residing both at home and in care homes and to monitor intervention effectiveness of falls management