Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Abstract

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

“…(b) Duty cycling. It puts the processor into sleep state to reduce power consumption, even though it may affect activity recognition [95]. (c) Low-power communications.…”

An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition

“…The drawback of the approach is that it cannot handle quick changes in context state and is prone to delays due to the cycling mechanism. Starliper et al [ 23 ] presented an approach where power consuming physiological response sensors are activated or deactivated depending on the activity recognized.…”

ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy

“…For example, the harvested energy changes significantly if the user moves indoors after being in the sun. User and context-aware approaches aim to address this issue by leveraging knowledge about the user's activities to reduce the power consumption of the device [25], [26]. These approaches focus only on power management and do not account for energy harvesting.…”