Speed Estimation From a Tri-axial Accelerometer Using Neural Networks
- Resource Type
- Conference
- Authors
- Song, Yoonseon; Shin, Seungchul; Kim, Seunghwan; Lee, Doheon; Lee, Kwang H.
- Source
- 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. :3224-3227 Aug, 2007
- Subject
- Bioengineering
Accelerometers
Neural networks
Legged locomotion
Velocity measurement
Length measurement
Acceleration
Magnetic field measurement
Belts
Magnetic sensors
Navigation
- Language
- ISSN
- 1094-687X
1558-4615
We propose a speed estimation method with human body accelerations measured on the chest by a tri-axial accelerometer. To estimate the speed we segmented the acceleration signal into strides measuring stride time, and applied two neural networks into the patterns parameterized from each stride calculating stride length. The first neural network determines whether the subject walks or runs, and the second neural network with different node interactions according to the subject's status estimates stride length. Walking or running speed is calculated with the estimated stride length divided by the measured stride time. The neural networks were trained by patterns obtained from 15 subjects and then validated by 2 untrained subjects' patterns. The result shows good agreement between actual and estimated speeds presenting the linear correlation coefficient r=0.9874. We also applied the method to the real field and track data.