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Lifespace tracking and activity monitoring on mobile phones

University of British Columbia

Daily patterns of behaviour are a rich source of information and play an important role in establishing a person’s quality of life. Lifespace refers to measurements of the frequency, geographic extent and independence of an individual’s travels. While difficult to measure and record automatically, lifespace has been shown to correlate to important metrics relating to physical performance, nutritional risk, and community engagement. MobiSense is a mobile health research platform that aims to improve mobility analysis for both ambulating and wheelchair users. The goals of the system were to be simple for users to collect mobility data, provide accessible summaries of daily behaviours and to enable further research and development in this area. The system is capable of lifespace summaries relating to indoor and outdoor mobility as well as activity trends and behaviours. For indoor reporting, we investigated robust classification algorithms for room level indoor localization using WiFi signal strengths. We pursue topological map localization as it requires simpler map models while preserving useful semantic information associated with location. Personalized maps are easy to create by capturing training observations in areas of interest. Outdoor summaries are captured by periodically recording GPS fixes. For activity monitoring, a decision tree classifier was learned using a combination of accelerometer and GPS features. The classifier can differentiate between stationary, wheeling (in a wheelchair), walking or vehicle motion. To capture the relevant sensor data, we extended an open source logging application which records data streams locally before uploading data to a web service to process and visualize results. The custom web service processes the data and generates summary files which can then be visualized either for each individual day or over a user selected date range. We employed a heat map visualization for outdoor lifespace to understand the geographic extent of a user’s mobility. For indoor and activity summaries, we employed temporal line charts to understand trends in a user’s mobility.

Text

2014-03-25

Attribution 2.5 Canada

http://hdl.handle.net/2429/46269

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/2.5/ca/