Gait episode identification based on wavelet feature clustering of spectrogram images

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Conference Proceeding
Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2949 - 2952
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Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We describe a new way to detect walking, and measure gait cadence, by using time-frequency signal processing together with spectrogram analysis of signals from a chest-worn inertial measurement unit (IMU). A pilot study of 11 participants suggests that this method is able to distinguish between walk and non-walk activities with up to 88.70% sensitivity and 97.70% specificity. Limitations of the method include instability associated with manual fine-tuning of local and global threshold levels.
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