Online pose classification and walking speed estimation using handheld devices
Name
Teller_Online pose.pdf
Size
1.79 MB
Format
Adobe PDF
Checksum (MD5)
d037bb7649b8502aeda39968a5516679
Author(s)
Park, Jun-geun
Patel, Ami
Curtis, Dorothy
Teller, Seth
Ledlie, Jonathan
Date Issued
September 2012
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2370216.2370235
