Abstract 13824: Lifestyle Management for Cardiac Health With Deep Learning of Biomechanical Dynamics

Abstract

Physical activities are of great values and, with appropriate principles, are reported to be clinically effective for rehabilitation from cardiac diseases. The physical activity-based lifestyle stimulation is thus highly promising to foster cardiac health management. Target the challenge that, the understanding of how different body locations contribute to the physical activity detection accuracy is still limited, we propose to leverage deep learning to investigate comprehensively seven body locations and six signal channels for each location. They are the chest, forearm, head, shin, thigh, upper-arm, and waist locations, and for each location, there are tri-axis acceleration signals and tri-axis gyroscope signals. In total there are forty-two combinations. The proposed convolutional neural network, when applied on each of the forty-two cases, yields a comprehensive comparison of these configurations in terms of physical activity type detection accuracy. Evaluated on fifteen subjects and six different physical activities, including climbing downstairs, climbing upstairs, jumping, lying, running/jogging, and walking, the determined optimal configuration is the chest location and the accelerometer axis-Y channel, with an accuracy of 95.1%. This study has not only maximized the physical activity detection accuracy through optimal configuration determination, but also deepened our understanding of how biomechanical dynamics are generated through different body locations and sensor channels. The research findings will greatly benefit cardiac health management.