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Real-Time Wearable Gait Phase Segmentation For Running And Walking
Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios.
IMU Based Deep Stride Length Estimation With Self-Supervised Learning
The proposed model can achieve better average percent error, 4. 78\%, on running and walking stride length regression and 99. 83\% accuracy on running and walking classification, when compared to the previous approach, 7. 44\% on the stride length estimation.
Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model
Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing.
