  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. This paper presents a
segmentation based gait phase detection with only a single six-axis IMU sensor,
which can easily adapt to both walking and running at various speeds. The
proposed segmentation uses CNN with gait phase aware receptive field setting
and IMU oriented processing order, which can fit to high sampling rate of IMU
up to 1000Hz for high accuracy and low sampling rate down to 20Hz for real time
calculation. The proposed model on the 20Hz sampling rate data can achieve
average error of 8.86 ms in swing time, 9.12 ms in stance time and 96.44\%
accuracy of gait phase detection and 99.97\% accuracy of stride detection. Its
real-time implementation on mobile phone only takes 36 ms for 1 second length
of sensor data.
