Asynchronous Averaging of Gait Cycles for Classification of Gait and Device Modes
This work addresses gait and device mode classification for applications in wearable technology, but it is incremental as it builds on existing segmentation and feature extraction techniques.
The paper tackled the problem of classifying human gait and device modes using IMU data by proposing a method to compute a unique gait signature through segmentation, resampling, and Fourier expansion, achieving a high classification rate for each step cycle.
An approach for computing unique gait signature using measurements collected from body-worn inertial measurement units (IMUs) is proposed. The gait signature represents one full cycle of the human gait, and is suitable for off-line or on-line classification of the gait mode. The signature can also be used to jointly classify the gait mode and the device mode. The device mode identifies how the IMU-equipped device is being carried by the user. The method is based on precise segmentation and resampling of the measured IMU signal, as an initial step, further tuned by minimizing the variability of the obtained signature within each gait cycle. Finally, a Fourier series expansion of the gait signature is introduced which provides a low-dimensional feature vector well suited for classification purposes. The proposed method is evaluated on a large dataset involving several subjects, each one containing two different gait modes and four different device modes. The gait signatures enable a high classification rate for each step cycle.