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Abstract:Strength training using patients' desired force level is helpful to improve training effect and promote rehabilitation. Generally, force levels are recognized by applying EMG or biomechanical information, these methods were not suitable for patients who lost important muscle groups or have weakened muscle functions. This paper proposed a method for identifying force level based on cerebral hemoglobin information, rather than the information depending on limbs. Ten subjects performed pedaling movement in three force levels. Features were extracted in both the time-domain and frequency-domain, with deoxygenated hemoglobin (deoxy) and the difference between oxygenated hemoglobin (oxy) and deoxy as parameters. Important frequency bands (0.01-0.03Hz, 0.03-0.06Hz, 0.06-0.09Hz, 0.09-0.12Hz) were confirmed by performing power spectrum density analysis. And significant measure channels were selected by performing one-way analyses of variance on three time periods around the start of movement. Force level was recognized by applying extreme learning machine (ELM). The corresponding precision rate was up to 78.7%. The proposed identification method was not restricted to the existence of limbs or the strength of limb information. It was realized based on brain information recorded in a real movement environment; it is helpful to realize the desired force level of subjects and to provide a control command for rehabilitation training equipment. IntroductionRecently, the number of people with motor dysfunction in lower limbs has been increasing rapidly because of natural disasters, injuries, accidents, diseases, and so on. In order to improve the training effect and the efficiency of recovery, patients' resistance training and active participation is very important and necessary. Thus, there is a great need to control rehabilitation device by patients' motion states of force level. Generally, EMG and other biomechanical information were used to recognize motion states [1][2][3]. However, many patients lost important muscles or have little muscle functions, and thus EMG or biomechanical information was not enough to identify motion states for these patients. Recently, brain information obtained by using EEG and fMRI technology has been very popular around the world [4-6]. Dario Farina, et al. classified four actual cycling torque motion by EEG information, and the recognition rate was around 50.8% [7]. DongEun Kim, et al. also applied EEG to classify the three grades (25%, 50%, 75%) of hand grip MVC (Maximum Voluntary Contraction), and the result was about 52.03% for left hand and 77.7% for right hand [8]. However, the above research had strict restrictions on testing environment and subjects. By contrast, functional near infrared spectroscopy (fNIRS) is a suitable tool to record brain information. Baolei Xu, et al. classified the speed and force clench imagination by support vector machine (SVM), the accuracy results was 72% [9]. Yunfa Fu, et al. combined the features of NIRS and EEG to recognize three levels of imagin...