Toward Seamless Wearable Sensing: Automatic On-Body Sensor Localization for Physical Activity Monitoring
Abstract- Mobile wearable sensors have demonstrated great potential in a broad range of applications in healthcare and wellbeing. These technologies are known for their potential to revolutionize the way next generation medical services are supplied and consumed by providing more effective interventions, improving health outcomes, and substantially reducing healthcare costs. Despite these potentials, utilization of these sensors is currently limited to lab settings and in highly controlled clinical trials. A major obstacle in widespread utilization of these systems is that the sensors need to be used in predefined locations on the body in order to provide accurate outcomes such as type of physical activity performed by the user. This has reduced patients' willingness to utilize such technologies. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms for accurate and automatic localization of wearable sensors. Our results based on real data collected using wearable motion sensors demonstrate that the proposed approach can perform sensor localization with 98.4% accuracy which is 30.7% more accurate than an approach without a feature selection mechanism. Furthermore, our algorithm obtains 2.4% accuracy improvement over compressive sensing techniques while it requires one order of magnitude less number of features for node classification.