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Conference Paper
Speed Estimation From a Tri-axial Accelerometer Using Neural Networks
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- Authors
- Yoon Seon Song, Seung Chul Shin, Seung Hwan Kim, Do Heon Lee, Kwang H. Lee
- Issue Date
- 2007-08
- Citation
- International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) 2007, pp.3224-3227
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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. © 2007 IEEE.
- KSP Keywords
- Acceleration Signals, Correlation Coefficient, Estimation method, Human body, Linear correlation, Running speed, Speed estimation, Stride length, Triaxial accelerometer, neural network