AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth Systems
AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth Systems
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AMSER：用于节能和弹性电子医疗系统的自适应多模态传感
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
N. Dutt
中科院分区：
文献类型：
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作者：
Emad Kasaeyan Naeini;Sina Shahhosseini;A. Kanduri;P. Liljeberg;A. Rahmani;N. Dutt
EHealth系统通过持续监测生理和背景数据，为用户提供关键的数字保健和健康服务。EHealth应用程序使用多模式机器学习内核来分析来自不同传感器模式的数据，并自动化决策。感知数据采集过程中的噪声输入和运动伪影影响了i)eHealth服务的预测准确性和弹性以及ii)处理垃圾数据时的能源效率。监控原始感觉输入以识别和丢弃噪声模式中的数据和特征可以提高预测精度和能源效率。我们提出了一个用于多模式eHealth应用的闭环监控框架AMSER，该框架可以通过i)监控输入通道，ii)分析原始输入以选择性地丢弃噪声数据和特征，以及iii)选择合适的机器学习模型来匹配配置的数据和特征向量-来减少垃圾输入和垃圾输出，以提高预测精度和能量效率。我们使用疼痛评估和压力监测的多模式eHealth应用程序对我们的AMSER方法进行评估，这些应用程序通过不同的传感器模式产生不同级别和类型的噪音组件。与最先进的多模式监测应用相比，我们的方法在预测精度上提高了22%，在传感阶段将能耗降低了5.6倍。
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× energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application.
在日常环境中使用可穿戴传感器进行个性化压力监测
DOI：
10.1109/embc46164.2021.9630224
发表时间：
2021
期刊：
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
影响因子：
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作者：
Tazarv, Ali;Labbaf, Sina;Reich, Stephanie M.;Dutt, Nikil;Rahmani, Amir M.;Levorato, Marco
通讯作者：
Levorato, Marco