Kinematic assessment for stroke patients in a stroke game and a daily activity recognition and assessment system

Stroke is the leading cause of serious, long-term disabilities among which deficits in motor abilities in arms or legs are most common. Those who suffer a stroke can recover through effective rehabilitation which is delicately personalized. To achieve the best personalization, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently. Traditionally, rehabilitation involves patients performing exercises in clinics where clinicians oversee the procedure and evaluate patients' recovery progress. Following the in-clinic visits, additional home practices are tailored and assigned to patients. The in-clinic visits are important to evaluate recovery progress. The information collected can then help clinicians customize home practices for stroke patients. However, as the number of in-clinic sessions is limited by insurance policies, the recovery information collected in-clinic is often insufficient. Meanwhile, the home practice programs report low adherence rates based on historic data. Given that clinicians rely on patients to self-report adherence, the actual adherence rate could be even lower. Despite the limited feedback clinicians could receive, the measurement method is subjective as well. In practice, classic clinical scales are mostly used for assessing the qualities of movements and the recovery status of patients. However, these clinical scales are evaluated subjectively with only moderate inter-rater and intra-rater reliabilities. Taken together, clinicians lack a method to get sufficient and accurate feedback from patients, which limits the extent to which clinicians can personalize treatment plans. This work aims to solve this problem. To help clinicians obtain abundant health information regarding patients' recovery in an objective approach, I've developed a novel kinematic assessment toolchain that consists of two parts. The first part is a tool to evaluate stroke patients' motions collected in a rehabilitation game setting. This kinematic assessment tool utilizes body-tracking in a rehabilitation game. Specifically, a set of upper body assessment measures were proposed and calculated for assessing the movements using skeletal joint data. Statistical analysis was applied to evaluate the quality of upper body motions using the assessment outcomes. Second, to classify and quantify home activities for stroke patients objectively and accurately, I've developed DARAS, a daily activity recognition and assessment system that evaluates daily motions in a home setting. DARAS consists of three main components: daily action logger, action recognition part, and assessment part. The logger is implemented with a Foresite system to record daily activities using depth and skeletal joint data. Daily activity data in a realistic environment were collected from sixteen post-stroke participants. The collection period for each participant lasts three months. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network fuses the prediction outputs from customized 3D Convolutional-De-Convolutional, customized Region Convolutional 3D network and a proposed Region Hierarchical Co-occurrence network which learns rich spatial-temporal features from either depth data or joint data. The per-frame precision and the per-action precision were 0.819 and 0.838, respectively, on the validation set. For the recognized actions, the kinematic assessments were performed using the skeletal joint data, as well as the longitudinal assessments. The results showed that, compared with non-stroke participants, stroke participants had slower hand movements, were less active, and tended to perform fewer hand manipulation actions. The assessment outcomes from the proposed toolchain help clinicians to provide more personalized rehabilitation plans that benefit patients.Includes bibliographical references

Sistema para análise automatizada de movimento durante a marcha usando uma câmara RGB-D

Nowadays it is still common in clinical practice to assess the gait (or way of walking) of a given subject through the visual observation and use of a rating scale, which is a subjective approach. However, sensors including RGB-D cameras, such as the Microsoft Kinect, can be used to obtain quantitative information that allows performing gait analysis in a more objective way. The quantitative gait analysis results can be very useful for example to support the clinical assessment of patients with diseases that can affect their gait, such as Parkinson’s disease. The main motivation of this thesis was thus to provide support to gait assessment, by allowing to carry out quantitative gait analysis in an automated way. This objective was achieved by using 3-D data, provided by a single RGB-D camera, to automatically select the data corresponding to walking and then detect the gait cycles performed by the subject while walking. For each detected gait cycle, we obtain several gait parameters, which are used together with anthropometric measures to automatically identify the subject being assessed. The automated gait data selection relies on machine learning techniques to recognize three different activities (walking, standing, and marching), as well as two different positions of the subject in relation to the camera (facing the camera and facing away from it). For gait cycle detection, we developed an algorithm that estimates the instants corresponding to given gait events. The subject identification based on gait is enabled by a solution that was also developed by relying on machine learning. The developed solutions were integrated into a system for automated gait analysis, which we found to be a viable alternative to gold standard systems for obtaining several spatiotemporal and some kinematic gait parameters. Furthermore, the system is suitable for use in clinical environments, as well as ambulatory scenarios, since it relies on a single markerless RGB-D camera that is less expensive, more portable, less intrusive and easier to set up, when compared with the gold standard systems (multiple cameras and several markers attached to the subject’s body).Atualmente ainda é comum na prática clínica avaliar a marcha (ou o modo de andar) de uma certa pessoa através da observação visual e utilização de uma escala de classificação, o que é uma abordagem subjetiva. No entanto, existem sensores incluindo câmaras RGB-D, como a Microsoft Kinect, que podem ser usados para obter informação quantitativa que permite realizar a análise da marcha de um modo mais objetivo. Os resultados quantitativos da análise da marcha podem ser muito úteis, por exemplo, para apoiar a avaliação clínica de pessoas com doenças que podem afetar a sua marcha, como a doença de Parkinson. Assim, a principal motivação desta tese foi fornecer apoio à avaliação da marcha, permitindo realizar a análise quantitativa da marcha de forma automatizada. Este objetivo foi atingido usando dados em 3-D, fornecidos por uma única câmara RGB-D, para automaticamente selecionar os dados correspondentes a andar e, em seguida, detetar os ciclos de marcha executados pelo sujeito durante a marcha. Para cada ciclo de marcha identificado, obtemos vários parâmetros de marcha, que são usados em conjunto com medidas antropométricas para identificar automaticamente o sujeito que está a ser avaliado. A seleção automatizada de dados de marcha usa técnicas de aprendizagem máquina para reconhecer três atividades diferentes (andar, estar parado em pé e marchar), bem como duas posições diferentes do sujeito em relação à câmara (de frente para a câmara e de costas para ela). Para a deteção dos ciclos da marcha, desenvolvemos um algoritmo que estima os instantes correspondentes a determinados eventos da marcha. A identificação do sujeito com base na sua marcha é realizada usando uma solução que também foi desenvolvida com base em aprendizagem máquina. As soluções desenvolvidas foram integradas num sistema de análise automatizada de marcha, que demonstrámos ser uma alternativa viável a sistemas padrão de referência para obter vários parâmetros de marcha espácio-temporais e alguns parâmetros angulares. Além disso, o sistema é adequado para uso em ambientes clínicos, bem como em cenários ambulatórios, pois depende de apenas de uma câmara RGB-D que não usa marcadores e é menos dispendiosa, mais portátil, menos intrusiva e mais fácil de configurar, quando comparada com os sistemas padrão de referência (múltiplas câmaras e vários marcadores colocados no corpo do sujeito).Programa Doutoral em Informátic