An interactive Internet-based system for tracking upper limb motion in home-based rehabilitation

Abstract

In this paper, we introduce an interactive telecommunication system that supports video/audio signal acquisition, data processing, transmission, and 3D animation for post stroke rehabilitation. It is designed for stroke patients to use in their homes. It records motion exercise data, and immediately transfers this data to hospitals via the internet. A real-time videoconferencing interface is adopted for patients to observe therapy instructions from therapists. The system uses a peer-to-peer network architecture, without the need for a server. This is a potentially effective approach to reducing costs, allowing easy setup and permitting group-rehabilitation sessions. We evaluate this system using the following steps: (1) motion detection in different movement patterns, such as reach, drink, and reach-flexion; (2) online bidirectional visual telecommunication; and (3) 3D rendering using a proposed offline animation package. This evaluation has subjectively been proved to be optimal.

Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification

“…Currently, a number of methods are used to monitor and classify daily physical activity and associated wellbeing parameters, such as self administered questionnaires [6], surveillance cameras [7], pedometers [8], heart-rate monitors [9], and various wearable sensors [10] [11]. All of these can be viewed as having inherent limitations.…”

Use of Multilayer Perceptron vs. Distance Measurement Methods for Classification of Exercises in Telerehabilitation

“…Nevertheless, for rehabilitation purposes it is common to combine them with other sensing devices, in order to overcome inertial sensors' handicaps like drift when estimating positions on 3D space [13,14,15,16,17,18]. These combinations of sensors result in systems that require complex algorithms to process motion data, being also intrusive and annoying for patients.…”

“…Common approaches to do this are the use of Hidden Markov Models [16,20], Extended Kalman and particle filters [13,14], DTW [21,25], and other machine learning methods, including mainly supervised neural networks [16,17]. In all these cases there is a restricted set of movements to be recognized.…”

Remote physiotherapy treatments using wireless body sensor networks

“…For example, in one project it is being used to link patients that reside within home environments to medical facilities using the Internet [12][13][14]. Motion data is used to monitor the patient's progress and devise new rehabilitation programs.…”