A Pilot Study Based on Cerebral Hemoglobin Information to Classify the Desired Walking Speed

To achieve more intelligent performance for walking-assistive devices, spontaneous motion intention of walking speed should be identified for providing a control command. In this letter, cerebral hemoglobin information was analyzed to recognize three levels of walking speed: low, medium, and high speed. Thirty subjects performed walking tasks in three levels of speed, with cerebral hemoglobin information being recorded and oxygenated hemoglobin (oxyHb) mainly analyzed. A novel method of time–frequency–space analysis is proposed to extract features through two steps. First, a wavelet packet method is used to analyze the oxyHb signals in four frequency bands [(I) 0–0.03 Hz, (II) 0.03–0.06 Hz, (III) 0.06–0.09 Hz, and (IV) 0.09–0.12 Hz]. Second, spatial features were summarized for each frequency band. The support vector machine method is used to identify speed levels. The dataset of 15 people is used to train the model, and the dataset of the other 15 people is used for validation. The final recognition rates of low, medium, and high speed are 93.33%, 66.67%, and 80%, respectively. The average recognition is up to 80%. The results indicate that the proposed method of time–frequency–space analysis is feasible for recognizing expected walking speed, and cerebral hemoglobin information could reflect humans’ spontaneous motion intention. Moreover, it may provide a more intelligent control method for walking-assistive devices and promote its development in the future.