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

Anderson Ian, Maitland Julie, Sherwood Scott, Barkhuus Louise, Chalmers Matthew, Hall Malcolm, Brown Barry, Muller Henk

Research output: Contribution to journalArticle (Academic Journal)peer-review

154 Citations (Scopus)

Abstract

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 in order to carry out a longer-term clinical trial.
Translated title of the contributionShakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones
Original languageEnglish
Article number185-199
JournalMobile Networks and Applications
Volume12(2-3)
Publication statusPublished - 2007

Bibliographical note

Other identifier: 2000754

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