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

  • Authors:

  • Emre Ertin;Nathan Stohs;Santosh Kumar;Andrew Raij;Mustafa al'Absi;Siddharth Shah

  • Affiliations:

  • The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;University of Memphis, Memphis, TN;Universit of South Florida, Tampa, FL;University of Minnesota, Duluth, MN;The Ohio State University, Columbus, OH

  • Venue:
  • Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
  • Year:
  • 2011

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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.