Wi-KF: A Rehabilitation Motion Recognition in Commercial Wireless Devices

Wireless sensing is increasingly used in the field of medical rehabilitation because of its advantages of low cost, non-contact and wide coverage. In the rehabilitation of patients, the recovery after upper limb injury is greatly significant. Nonstandard rehabilitation motions will cause secondary injury to the body. Therefore, how to achieve standardized rehabilitation motions at a low cost in the home environment has become an urgent problem to be solved. In order to settle it, a rehabilitation motion recognition method Wi-KF based on Wi-Fi signal is designed. First, we propose a data segmentation and counting Peak method, which can accurately segment a continuous motion into multiple single motions and lays a foundation for a feature extraction algorithm. The motion segmented by the Peak method is converted into a Doppler feature image. Then Bag of Convolutional Feature (BoCF) algorithm is used to extract features and overcomes the difference in image size. Finally, the extracted features are input into Extreme Learning Machine (ELM) algorithm for classification. The Wi-KF method has been extensively and fully verified in two real environments. The experimental results show that the average motion recognition rate of the Wi-KF method is about 94.9%. Hence the method has strong robustness. In sum, the method proposed in the paper provides a low-cost solution for standardizing the rehabilitation motions of patients.