Abstract
Most available approaches for step counting rely on accelerometer data, and thus are prone to over-counting. In addition, most existing devices calculate the traveled distance based on the counted number of steps and a preset stride length. We present a robust and autonomous method for counting steps and tracking and calculating stride length by using accelerometer, gravity sensor and camera data from smart phones. To provide higher precision, instead of using a preset step and/or stride length, the proposed method calculates the distance traveled with each step by using the camera data. If camera is tilted significantly, the angle data obtained from the gravity sensor is used to account for camera geometry and increase the precision of the calculated step length. Experiments are performed with different subjects and the proposed method is compared with accelerometer-based step counter apps. The results show that incorporating camera geometry increases the accuracy, and the proposed method provides the lowest average error rate in number of steps taken and the calculated traveled distance.
Original language | English (US) |
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Title of host publication | 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 464-468 |
Number of pages | 5 |
Volume | 2016-August |
ISBN (Electronic) | 9781467399616 |
DOIs | |
State | Published - Aug 3 2016 |
Event | 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016 |
Other
Other | 23rd IEEE International Conference on Image Processing, ICIP 2016 |
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Country/Territory | United States |
City | Phoenix |
Period | 9/25/16 → 9/28/16 |
Keywords
- Indoor navigation
- Smart phone
- Step counting
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing