Using wearable sensors and real time inference to understand human recall of routine activities

  • Authors:

  • Predrag Klasnja;Beverly L. Harrison;Louis LeGrand;Anthony LaMarca;Jon Froehlich;Scott E. Hudson

  • Affiliations:

  • Intel Research Seattle, Seattle, WA;Intel Research Seattle, Seattle, WA;Intel Research Seattle, Seattle, WA;Intel Research Seattle, Seattle, WA;University of Washington, Seattle, WA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
  • Year:
  • 2008

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Abstract

Users' ability to accurately recall frequent, habitual activities is fundamental to a number of disciplines, from health sciences to machine learning. However, few, if any, studies exist that have assessed optimal sampling strategies for in situ self-reports. In addition, few technologies exist that facilitate benchmarking self-report accuracy for routine activities. We report on a study investigating the effect of sampling frequency of self-reports of two routine activities (sitting and walking) on recall accuracy and annoyance. We used a novel wearable sensor platform that runs a real time activity inference engine to collect in situ ground truth. Our results suggest that a sampling frequency of five to eight times per day may yield an optimal balance of recall and annoyance. Additionally, requesting self-reports at regular, predetermined times increases accuracy while minimizing perceived annoyance since it allows participants to anticipate these requests. We discuss our results and their implications for future studies.