Smart Shoes With Adaptive Sampling for Outpatient Daily Health Monitoring

This paper proposes an adaptive sampling algorithm for a pair of smart shoes for patients to use as a daily health monitoring device. The main hardware of the smart shoes features four pneumatic pressure sensors that measure ground contact forces (GCFs) and a global positioning system (GPS) to track the location of the user. The sampling rate of the pressure sensors and the GPS are changed based on the activity, either walking or sitting, detected from the user’s GCFs. An outdoor test was conducted to validate the adaptive sampling algorithm. The result was a 95% reduction in data size compared to sampling with the highest settings from all components. Collected GPS information from a subject’s morning activities was displayed onto a map to demonstrate how it could be used as contextual data for daily monitoring.