PBVI for Optimal Photoplethysmography Noise Filter Selection Using Human Activity Recognition Observations for Improved Heart Rate Estimation on Multi-Sensor Systems

Abstract

This work details the partially observable markov decision process (POMDP) and the point-based value iteration (PBVI) algorithms for use in multisensor systems, specifically, a sensor system capable of heart rate (HR) estimation through wearable photoplethysmography (PPG) and accelerometer signals. PPG sensors are highly susceptible to motion artifact (MA); however, current methods focus more on overall MA filters, rather than action specific filtering. An end-to-end embedded human activity recognition (HAR) System is developed to represent the observation uncertainty, and two action specific PPG MA reducing filters are proposed as actions. PBVI allows optimal action decision-making based on an uncertain observation, effectively balancing correct action choice and sensor system cost. Two central systems are proposed to accompany these algorithms, one for unlimited observation access and one for limited observation access. Through simulation, it can be shown that the limited observation system performs optimally when sensor cost is negligible, while limited observation access performs optimally when a negative reward for sensor use is considered. The final general framework for POMDP and PBVI was applied to a specific HR estimation example. This work can be expanded on and used as a basis for future work on similar multisensor system.