Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being
| Autor: | Gary M. Weiss, Isaac H. Ronan, Jessica L. Timko, Tony T. Pulickal, Paul T. McHugh, Jeffrey W. Lockhart |
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| Rok vydání: | 2016 |
| Předmět: |
Fitness Trackers
Service (systems architecture) Computer science 02 engineering and technology Data modeling Activity recognition World Wide Web Public use 020204 information systems Server 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing User interface Set (psychology) |
| Zdroj: | DSAA |
| DOI: | 10.1109/dsaa.2016.89 |
| Popis: | Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user's activities. It initially employs a "universal" model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share. |
| Databáze: | OpenAIRE |
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