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

• URL: http://arxiv.org/abs/2112.08176v1
• Date: Mon, 13 Dec 2021 00:52:33 GMT
• Title: AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems
• Authors: Emad Kasaeyan Naeini, Sina Shahhosseini, Anil Kanduri, Pasi Liljeberg, Amir M. Rahmani, Nikil Dutt
• Abstract summary: Noisy inputs and motion artifacts during sensory data acquisition affect prediction accuracy and resilience of eHealth services. We propose a closed-loop monitoring and control framework for multi-modal eHealth applications, AMSER, that can mitigate garbage-in garbage-out. Our approach achieves up to 22% improvement in prediction accuracy and 5.6$times$ energy consumption reduction in the sensing phase.
• Score: 5.04685484754788
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal 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$\times$ energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application.

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