Online pose classification and walking speed estimation using handheld devices
Author(s)
Park, Jun-geun; Patel, Ami; Curtis, Dorothy; Teller, Seth; Ledlie, Jonathan
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We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.
Date issued
2012-09Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12)
Publisher
Association for Computing Machinery (ACM)
Citation
Jun-geun Park, Ami Patel, Dorothy Curtis, Seth Teller, and Jonathan Ledlie. 2012. Online pose classification and walking speed estimation using handheld devices. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). ACM, New York, NY, USA, 113-122.
Version: Author's final manuscript
ISBN
9781450312240