Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones
Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones
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DOI：
10.1007/s11036-007-0011-7
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发表时间：
2007-06-01
影响因子：
3.8
通讯作者：
Muller, Henk
中科院分区：
文献类型：
--
作者：
Anderson, Ian;Maitland, Julie;Muller, Henk
本文探讨了使用一种未增强的商品技术--移动的手机作为健康促进工具的潜力。我们描述了一个原型应用程序，跟踪人们的日常锻炼活动，使用人工神经网络（ANN）来分析GSM蜂窝信号强度和可见性，以估计用户的运动。在一个短期的研究中，我们发现，在朋友之间分享活动信息的原型，意识鼓励反思，并增加了日常活动的动力。这项研究引起了人们对“真实的世界”中人工神经网络促进的活动检测的可靠性的关注。我们描述了一些试点研究的细节，并介绍了一种有前途的新方法，已开发的活动检测中提出的一些问题的试点研究，涉及隐马尔可夫模型（HMM），任务建模和无监督校准。最后，我们提出了进一步开发该系统的计划，以便进行更长期的临床试验。
This paper explores the potential for use of an unaugmented commodity technology-the mobile phoneas 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.