MultiGRehab: Developing a Multimodal Biosignals Acquisition and Analysis Framework for Personalizing Stroke and Cardiac Rehabilitation based on Adaptive Serious Games

Abstract

Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.