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eHealth systems deliver critical digital healthcare and wellness services forusers by continuously monitoring physiological and contextual data. eHealthapplications use multi-modal machine learning kernels to analyze data fromdifferent sensor modalities and automate decision-making. Noisy inputs andmotion artifacts during sensory data acquisition affect the i) predictionaccuracy and resilience of eHealth services and ii) energy efficiency inprocessing garbage data. Monitoring raw sensory inputs to identify and dropdata and features from noisy modalities can improve prediction accuracy andenergy efficiency. We propose a closed-loop monitoring and control frameworkfor multi-modal eHealth applications, AMSER, that can mitigate garbage-ingarbage-out by i) monitoring input modalities, ii) analyzing raw input toselectively drop noisy data and features, and iii) choosing appropriate machinelearning models that fit the configured data and feature vector - to improveprediction accuracy and energy efficiency. We evaluate our AMSER approach usingmulti-modal eHealth applications of pain assessment and stress monitoring overdifferent levels and types of noisy components incurred via different sensormodalities. Our approach achieves up to 22\% improvement in prediction accuracyand 5.6$\times$ energy consumption reduction in the sensing phase against thestate-of-the-art multi-modal monitoring application.

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