Stride-Classification

The primary motivation for this work is to reduce the time burden required in the manual classification of non-trip toe-off events. The rationale behind implementing a machine learning solution is driven both by the high inter and intra-subject data variability and the relatively large number of potential features/ input sources. Lastly, the implementation of any solution can be used to classify toe-off events that an experimenter is unsure of the classification.