NDLI: AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

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AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

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Demo: An unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

Content Provider	ACM Digital Library
Author	al'Absi, Mustafa Kumar, Santosh Raij, Andrew Ertin, Emre Stohs, Nathan Shah, Siddharth
Abstract	The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.
Starting Page	274
Ending Page	287
Page Count	14
File Format	PDF MP4
ISBN	9781450307185
DOI	10.1145/2070942.2070970
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-11-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Wearable physiological sensors Deployment experiences Mobile health Psychological stress monitoring
Content Type	Audio Text
Resource Type	Article

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