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

被引:17
|
作者
Starliper, Nathan [1 ,3 ]
Mohammadzadeh, Farrokh [1 ,3 ]
Songkakul, Tanner [1 ,3 ]
Hernandez, Michelle [2 ]
Bozkurt, Alper [1 ,3 ]
Lobaton, Edgar [1 ,3 ]
机构
[1] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27695 USA
[2] Univ N Carolina, Dept Pediat, Sch Med, Chapel Hill, NC 27516 USA
[3] Engn Bldg 2,890 Oval Dr, Raleigh, NC 27695 USA
来源
SENSORS | 2019年 / 19卷 / 03期
关键词
wearable health; physiological prediction; activity clustering; multi-modal data; Body Sensor Networks; sensor selection; power efficient sensing; SELF-MANAGEMENT; SENSORS; ASTHMA; HEALTH; NETWORKS; PERCEPTION; GAIT;
D O I
10.3390/s19030441
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:20
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