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

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. Copyright 2008 ACM.