An Exoskeleton Robotic Arm System Based on Motion Pattern Recognition and Control Using Multi-Channel EMG Signals

In last few decades, exoskeleton robotics has become a popular research topic and used for enhancing human abilities, assisting disabled people, and physical therapy applications. This type of researches focuses on enhancing or recovering user’s abilities and enabling the user to intuitively control an exoskeleton robot using the user’s biomedical signals. In this study, a novel feature extraction method, using the short-time Fourier transform ranking (STFT-ranking) feature, was proposed to represent the motion pattern information in multichannel EMG signals. Moreover, an exoskeleton-type robot arm system is implemented to enhance human abilities using multi-channel electromyography (EMG) signals as the main control signals. Otherwise, a control strategy based on admittance control method and a selection method of how to define an appropriate range for the adjustable admittance control parameters are proposed.
The STFT-ranking feature method was used to extract information from the user’s muscles; then in order to build a motion pattern recognition model, principal component analysis (PCA) and support vector machine (SVM) were applied. With the motion pattern recognition model, the exoskeleton robot system can recognize the user’s motion patterns then be controlled by the user intuitively. In addition, the estimated relationship between the EMG signals and the force produced was employed to control the robot arm through the admittance control method. Furthermore, a selection method of how to define an appropriate range for the adjustable admittance control parameters was proposed. The corresponding assistant performance quantized by an energy saving index (ESI) was compared and analyzed.
Considering two different muscle contractions (dynamic and isometric contractions), the study also compared and analyzed the performances of applying STFT-ranking features and conventional EMG features including time-domain and frequency-domain features. Among the features tested, the STFT-ranking feature yielded an accuracy rate exceeding 90% when the EMG signals the same type of muscle contraction were used in the training and validation feature data sets . On average, the robot arm system saved user energy by over 40% (43.7% and 59.3% for shoulder and elbow joints, respectively). The proposed methods and exoskeleton robotic arm system can be applied in the future to assist people such as farmers and laborers in the case of handling heavy objects to reduce their workloads.