Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones

This paper explores the potential for use of an unaugmented commodity technology—the mobile phone— as a health promotion tool. We describe a prototype application that tracks the daily exercise activities of people, using an Artificial Neural Network (ANN) to analyse GSM cell signal strength and visibility to estimate a user’s movement. In a short-term study of the prototype that shared activity information amongst groups of friends, we found that awareness encouraged reflection on, and increased motivation for, daily activity. The study raised concerns regarding the reliability of ANN-facilitated activity detection in the ‘real world’. We describe some of the details of the pilot study and introduce a promising new approach to activity detection that has been developed in response to some of the issues raised by the pilot study, involving Hidden Markov Models (HMM), task modelling and unsupervised calibration. We conclude with our intended plans to develop the system further...

• Type: Journal
• Year: 2007
• Where: MONET
• Authors: Ian Anderson, Julie Maitland, Scott Sherwood, Louise Barkhuus, Matthew Chalmers, Malcolm Hall, Barry Brown, Henk L. Muller
• Keywords: Activity Detection, Daily Activity, MONET 2007, Pilot Study