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L'analyse de la démarche clinique est généralement subjective, étant effectuée par des cliniciens observant la démarche des patients. Des alternatives à une telle analyse sont les systèmes basés sur les marqueurs et les systèmes basés sur les plates-formes au sol. Cependant, cette analyse standard de la marche nécessite des laboratoires spécialisés, des équipements coûteux et de longs délais d'installation et de post-traitement. Les chercheurs ont fait de nombreuses tentatives pour proposer une alternative basée sur la vision par ordinateur pour l'analyse de la demarche. Avec l'apparition de caméras 3D bon marche, le problème de l'évaluation qualitative de la démarche a été re-examiné. Les chercheurs ont réalisé le potentiel des dispositifs de cameras 3D pour les applications d'analyse de mouvement. Cependant, malgré des progrès très encourageants dans les technologies de détection 3D, leur utilisation réelle dans l'application clinique reste rare.Cette thèse propose des modèles et des techniques pour l'évaluation du mouvement à l'aide d'un capteur Microsoft Kinect. En particulier, nous étudions la possibilité d'utiliser différentes données fournies par une caméra RGBD pour l'analyse du mouvement et de la posture. Les principales contributions sont les suivantes. Nous avons réalisé une étude de l'etait de l'art pour estimer les paramètres importants de la démarche, la faisabilité de différentes solutions techniques et les méthodes d'évaluation de la démarche existantes. Ensuite, nous proposons un descripteur de posture basé sur un nuage de points 3D. Le descripteur conçu peut classer les postures humaines statiques a partir des données 3D. Nous construisons un système d'acquisition à utiliser pour l'analyse de la marche basée sur les donnees acquises par un capteur Kinect v2. Enfin, nous proposons une approche de détection de démarche anormale basée sur les données du squelette. Nous démontrons que notre outil d'analyse de la marche fonctionne bien sur une collection de données personnalisées et de repères existants. Notre méthode d'évaluation de la démarche affirme des avances significatives dans le domain, nécessite un équipement limité et est prêt à être utilisé pour l'évaluation de la démarche. / Clinical Gait analysis is traditionally subjective, being performed by clinicians observing patients gait. A common alternative to such analysis is markers-based systems and ground-force platforms based systems. However, this standard gait analysis requires specialized locomotion laboratories, expensive equipment, and lengthy setup and post-processing times. Researchers made numerous attempts to propose a computer vision based alternative for clinical gait analysis. With the appearance of commercial 3D cameras, the problem of qualitative gait assessment was reviewed. Researchers realized the potential of depth-sensing devices for motion analysis applications. However, despite much encouraging progress in 3D sensing technologies, their real use in clinical application remains scarce.In this dissertation, we develop models and techniques for movement assessment using a Microsoft Kinect sensor. In particular, we study the possibility to use different data provided by an RGBD camera for motion and posture analysis. The main contributions of this dissertation are the following. First, we executed a literature study to estimate the important gait parameters, the feasibility of different possible technical solutions and existing gait assessment methods. Second, we propose a 3D point cloud based posture descriptor. The designed descriptor can classify static human postures based on 3D data without the use of skeletonization algorithms. Third, we build an acquisition system to be used for gait analysis based on the Kinect v2 sensor. Fourth, we propose an abnormal gait detection approach based on the skeleton data. We demonstrate that our gait analysis tool works well on a collection of custom data and existing benchmarks. Weshow that our gait assessment approach advances the progress in the field, is ready to be used for gait assessment scenario and requires a minimum of the equipment.

Background: Total knee arthroplasty (TKA) is the treatment of choice for end-stage knee osteoarthritis. Growth in the number of procedures performed annually in the United States is expected to increase steadily. Post-operative rehabilitation settings vary and include both institutional and community based physical therapy (PT) services. Despite access to PT, deficits in gait often persist for months and even years after surgery. Slow gait speed, asymmetrical walking patterns, and prolonged time in double-limb support following the TKA often lead to the need for an assistive device for walking and prolong the rehabilitation phase. Purpose: The purpose of this study is to analyze early gait during inpatient rehabilitation to quantify both the improvements made and deficits that remain in important gait variables. This study identifies predictor variables that contribute to the variance in discharge ambulation device use and IRF length of stay. Subjects: A convenience sample of 230 patients discharged to an IRF following a TKA (160 following a single TKA and 70 following a bilateral procedure) was used for this analysis. Method: Paired t-tests were used to compare temporal and spatial gait variables from the initial gait assessment compared to the discharge gait assessment in patients following single TKA to determine remaining deficits. Right vs left comparisons were made for patients following a bilateral procedure. A binary logistic regression was used to identify predictors associated with the need for a two-handed ambulation device at discharge. A multiple linear regression developed a model to assess predictors of the inpatient rehabilitation length of stay. Finally, a self-assessment to evaluate patient confidence with walking (mGES scale) was correlated to actual gait speed performed on the gait analysis in a sample of patients from our study population. Findings: Deficits in step length, step time and percent of single limb support remained in the involved limb compared to uninvolved limb at discharge from inpatient rehabilitation following single TKA; no limb differences between the right and left side were noted in patients after bilateral TKA. The discharge gait speed of 54.6 cm/sec for single TKA patients and discharge speed of 61.5 cm/sec for bilateral TKA patients is within the classification of limited community ambulators and making them appropriate for a home discharge. But despite improvement from admission to discharge, the gait speed for both groups in our study remain below the gait speed identified by prior studies 3-months following TKA surgery where speed reached 135 cm/sec. The need for a two-handed ambulation device, such as bilateral canes or a walker, was associated with slow walking speed and prior use of a device before surgery. A longer rehabilitation length of stay was associated with slower initial gait speed, lower motor FIM scores and reduced knee extension at admission. The mGES patient self-report conducted at the time of the discharge gait assessment showed a moderate correlation to the discharge gait speed; however, the pairing of the admission mGES with the admission gait speed was not significantly correlated.

The thesis deals with the evaluation of gait and balance tests in patients after vestibular schwannoma surgery. The experimental part focuses on the evaluation of dynamics of walking tests during hospitalization of patients diagnosed with vestibular schwannoma. The aim of the experimental part is also to analyze statistically significant correlations between measurable parameters of vestibulo-ocular reflex and walking tests and to evaluate the correlation between the subjective scale of fall fear and the objective assessment of walking and dynamic postural stability. A total of 28 patients aged 33 to 68 (14 men and 14 women) were incuded in the research with diagnosed vestibular schwannoma. Measurements were performed three times in patients (before surgery, after surgery and before the end of hospitalization). After the surgery, in addition to standard rehabilitation, training with visual biofeedback was included using the interactive Homebalance system. Gait and balance assessments were performed throug the tests Timed Up and Go, Four Step Square Test and Functional Gait Assessment. Examinations were complemented by a questionnaire of subjective assessment of fear of falling Falls Efficacy Scale. Statistical analysis showed a significant correlation coefficient in the correlation of Four Step...

Global rise in life expectancy has resulted in an increased demand for affordable healthcare and monitoring services. The advent of miniature and low–power sensor technologies coupled with the emergence of the Internet–of–Things has paved the way towards affordable health monitoring tools in wearable platforms. However, ensuring power–efficient operation, data accuracy and user comfort are critical for such wearable systems. This thesis focuses on the development of accurate and computationally efficient algorithms and low–cost, unobtrusive devices with potential predictive capability for monitoring mobility and cardiac health in a wearable platform. A three–stage complementary filter–based approach is developed to realize a computationally efficient method to estimate sensor orientation in real–time. A gradient descent–based approach is used to estimate the gyroscope integration drift, which is subsequently subtracted from the integrated gyroscope data to get the sensor orientation. This predominantly gyroscope–based orientation estimation approach is least affected by external acceleration and magnetic disturbances. A two–stage complementary filter–based efficient sensor fusion algorithm is developed for real–time monitoring of lower–limb joints that estimates the IMU inclinations in the first stage and uses a gradient descent–based approach in the second stage to estimate the joint angles. The proposed method estimates joint angles primarily from the gyroscope measurements without incorporating the magnetic field measurement, rendering the estimated angles least affected by any external acceleration and insensitive to magnetic disturbances. An IMU–based simple, low–cost and computationally efficient gait–analyzer is developed to track the course of an individual's gait health in a continuous fashion. Continuous monitoring of gait patterns can potentially enable detecting musculoskeletal or neurodegenerative diseases at the early onset. The proposed gait analyzer identifies an anomalous gait with moderate to high accuracy by evaluating the gait features with respect to the baseline clusters corresponding to an individual’s healthy peer group. The adoption of a computationally efficient signal analysis technique renders the analyzer suitable for systems with limited processing capabilities. A flexible dry capacitive electrode and a wireless ECG monitoring system with automatic anomaly detection capability are developed. The flexible capacitive electrode reduces motion artifacts and enables sensing bio–potential over a dielectric material such as cotton cloth. The virtual ground of the electrode allows for obtaining single–lead ECG using two electrodes only. ECG measurements obtained over different types of textile materials and in presence of body movements show comparable performance to other reported ECG monitoring systems. An algorithm is developed separately as a potential extension of the software to realize automatic identification of Atrial Fibrillation from short single–lead ECGs. The association between human gait and cardiac activities is studied. The gait is measured using wearable IMUs and the cardiac activity is measured with a single–lead handheld ECG monitor. Some key cardiac parameters, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation. These associations between gait and heart can be useful in realizing low–cost in–home personal monitoring tool for early detecting CVD–related changes in gait features before the CVD symptoms are manifested. / Thesis / Doctor of Philosophy (PhD) / Wearable health monitoring systems can be a viable solution to meet the increased demand for affordable healthcare and monitoring services. However, such systems need to be energy–efficient, accurate and ergonomic to enable long–term monitoring of health reliably while preserving user comfort. In this thesis, we develop efficient algorithms to obtain real–time estimates of on–body sensors' orientation, gait parameters such as stride length, and gait velocity and lower–limb joint angles. Furthermore, we develop a simple, low–cost and computationally efficient gait–analyzer using miniature and low–power inertial motion units to track the health of human gait in a continuous fashion. In addition, we design flexible, dry capacitive electrodes and use them to develop a portable single–lead electrocardiogram (ECG) device. The flexible design ensures better conformity of the electrode to the skin, resulting in better signal quality. The capacitive nature allows for obtaining ECG signals over insulating materials such as cloth, thereby potentially enabling a comfortable means of long–term cardiac health monitoring at home. Besides, we implement an automatic anomaly detection algorithm that detects Atrial Fibrillation with good accuracy from short single–lead ECGs. Finally, we investigate the association between gait and cardiac activities. We observe that some important cardiac signs, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation.