AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems

Abstract

eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multimodal machine learning kernels to analyze data from different sensor modalities and automate decision-making. Noisy inputs and motion artifacts during sensory data acquisition affect the i) prediction accuracy and resilience of eHealth services and ii) energy efficiency in processing garbage data. Monitoring raw sensory inputs to identify and drop data and features from noisy modalities can improve prediction accuracy and energy efficiency. We propose a closed-loop monitoring and control framework for multi-modal eHealth applications, AMSER, that can mitigate garbage-in garbage-out by i) monitoring input modalities, ii) analyzing raw input to selectively drop noisy data and features, and iii) choosing appropriate machine learning models that fit the configured data and feature vector -to improve prediction accuracy and energy efficiency. We evaluate our AMSER approach using multi-modal eHealth applications of pain assessment and stress monitoring over different levels and types of noisy components incurred via different sensor modalities. Our approach achieves up to 22% improvement in prediction accuracy and 5.6× energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application.

Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices

“…Despite this, in stress detection, classical machine learning models have been more widely adopted compared to deep learning models due to the classical models' lower complexity levels, important for wearable on-device deployment [16]. However, both of these types of learning-based methods lack robustness when using single sensor modalities, since the coverage area of each sensing modality is limited by the domain in which the sensors operate [17].…”