Walking is an essential task important to recovery after stroke. However, there is a limited understanding regarding the characteristics of walking in in-patients with stroke. The objectives of this thesis were to: 1) develop an instrument capable of acquiring temporal characteristics of everyday walking; 2) investigate the quantity and control of everyday walking; and 3) profile the task-specific link between walking and cardiorespiratory response. In study 1 we developed and validated a wireless monitoring system (ABLE system). Study 2 revealed low quantities of everyday walking (4816 steps; SD 3247) characterized by short bout durations (59.8s; SD 23.4) and asymmetric walking. In study 3 we observed a modest task-related response in HR(19.4% HRR); however, the intensity and duration of everyday walking did not approach the guidelines for aerobic benefit. Monitoring in-patient walking can help guide clinical decision making in developing methods to maximize recovery after stroke.

Walking is an essential task important to recovery after stroke. However, there is a limited understanding regarding the characteristics of walking in in-patients with stroke. The objectives of this thesis were to: 1) develop an instrument capable of acquiring temporal characteristics of everyday walking; 2) investigate the quantity and control of everyday walking; and 3) profile the task-specific link between walking and cardiorespiratory response. In study 1 we developed and validated a wireless monitoring system (ABLE system). Study 2 revealed low quantities of everyday walking (4816 steps; SD 3247) characterized by short bout durations (59.8s; SD 23.4) and asymmetric walking. In study 3 we observed a modest task-related response in HR(19.4% HRR); however, the intensity and duration of everyday walking did not approach the guidelines for aerobic benefit. Monitoring in-patient walking can help guide clinical decision making in developing methods to maximize recovery after stroke.

The portability, ease of use and improved accuracy of miniature inertial sensors brought by current microelectromechanical system (MEMS) technology has inspired researchers to develop human movement monitoring system with body-fixed sensors. Although a large number of studies have attempted to explore the use of miniature inertial sensors in estimating walking speed for the past two decades, there still remain some questions regarding applying inertial sensors in estimating walking speed under different walking conditions and for different subject populations. In this thesis, I focus on evaluating and improving the performance of a shank-mounted mounted inertial measurement unit (IMU) based walking speed estimation method. My research can be divided into four parts. The first part was a systematic review regarding the state of the art of current development of the inertial sensor based walking speed estimation method. A total of 16 articles were fully reviewed in terms of sensor specification, sensor attachment location, experimental design and spatial parameter estimation algorithm. In the second part, a comprehensive performance evaluation was conducted, which included the treadmill and overground walking experiments with constraint on the walking speed, stride length and stride frequency. A systematic error was observed in the error analysis of this study, which was adjusted by subtracting the bias by linear regression. In the third part, a post-stroke subject overground walking experiment was carried out with an improved walking speed estimation method that reduced the systematic error caused by previous false initial speed assumption. In addition to walking speed estimation, the gait asymmetry for post-stroke hemiparetic gait was also evaluated with the proposed method. The last part was the sensor error model analysis. We elaborately analyzed and discussed the estimation errors involved in this method in order to completely understand the sensor error compensation in walking speed estimation algorithm design. Two existing sensor error models and one newly developed sensor error model were compared with the treadmill walking experiment, which demonstrated the effect of each sensor error component on the estimation result and the importance of the sensor error model selection. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-08-23 19:38:16.965

La marche occupe un rôle important dans la vie quotidienne. Ce processus apparaît comme facile et naturel pour des gens en bonne santé. Cependant, différentes sortes de maladies (troubles neurologiques, musculaires, orthopédiques...) peuvent perturber le cycle de la marche à tel point que marcher devient fastidieux voire même impossible. Ce projet utilise l'application de Poincaré pour évaluer l'asymétrie de la marche d'un patient à partir d'une carte de profondeur acquise avec un senseur Kinect. Pour valider l'approche, 17 sujets sains ont marché sur un tapis roulant dans des conditions différentes : marche normale et semelle de 5 cm d'épaisseur placée sous l'un des pieds. Les descripteurs de Poincaré sont appliqués de façon à évaluer la variabilité entre un pas et le cycle complet de la marche. Les résultats montrent que la variabilité ainsi obtenue permet de discriminer significativement une marche normale d'une marche avec semelle. Cette méthode, à la fois simple à mettre en oeuvre et suffisamment précise pour détecter une asymétrie de la marche, semble prometteuse pour aider dans le diagnostic clinique. / Gait plays an important part in daily life. This process appears to be very easy and natural for healthy people. However, different kinds of diseases (neurological, muscular, orthopedic...) can impede the gait cycle to such an extent that gait becomes tedious or even infeasible. This project applied Poincare plot analysis to assess the gait asymmetry of a patient from a depth map acquired with a Kinect sensor. To validate the approach, 17 healthy subjects had to walk on a treadmill under different conditions : normal walk and with a 5 cm thick sole under one foot. Poincare descriptors were applied in such a way that they assess the variability between a step and the corresponding complete gait cycle. Results showed that variability significantly discriminates between a normal walk and a walk with a sole. This method seems promising for a clinical use as it is simple to implement and precise enough to assess gait asymmetry.

Le mouvement de la marche est un processus essentiel de l'activité humaine et aussi le résultat de nombreuses interactions collaboratives entre les systèmes neurologiques, articulaires et musculo-squelettiques fonctionnant ensemble efficacement. Ceci explique pourquoi une analyse de la marche est aujourd'hui de plus en plus utilisée pour le diagnostic (et aussi la prévention) de différents types de maladies (neurologiques, musculaires, orthopédique, etc.). Ce rapport présente une nouvelle méthode pour visualiser rapidement les différentes parties du corps humain liées à une possible asymétrie (temporellement invariante par translation) existant dans la démarche d'un patient pour une possible utilisation clinique quotidienne. L'objectif est de fournir une méthode à la fois facile et peu dispendieuse permettant la mesure et l'affichage visuel, d'une manière intuitive et perceptive, des différentes parties asymétriques d'une démarche. La méthode proposée repose sur l'utilisation d'un capteur de profondeur peu dispendieux (la Kinect) qui est très bien adaptée pour un diagnostique rapide effectué dans de petites salles médicales car ce capteur est d'une part facile à installer et ne nécessitant aucun marqueur. L'algorithme que nous allons présenter est basé sur le fait que la marche saine possède des propriétés de symétrie (relativement à une invariance temporelle) dans le plan coronal. / The gait movement is an essential process of the human activity and also the result of coordinated effort between the neurological, articular and musculoskeletal systems. This motivates why gait analysis is important and also increasingly used nowadays for the (possible early) diagnosis of many different types (neurological, muscular, orthopedic, etc.) of diseases. This paper introduces a novel method to quickly visualize the different parts of the body related to an asymmetric movement in the human gait of a patient for daily clinical. The goal is to provide a cheap and easy-to-use method to measure the gait asymmetry and display results in a perceptually relevant manner. This method relies on an affordable consumer depth sensor, the Kinect. The Kinect was chosen because this device is amenable for use in small, confined area, like a living room. Also, since it is marker-less, it provides a fast non-invasive diagnostic. The algorithm we are going to introduce relies on the fact that a healthy walk has (temporally shift-invariant) symmetry properties in the coronal plane.