Preliminary results of online classification of upper limb motions from around-shoulder muscle activities

Recently, detecting upper-limb motion intention for prosthetic control purpose attracted growing research attention. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex. The disadvantage of relying only on the local information to recognize a whole body coordinated motion is that misrecognition could easily happen, so that steady and reliable continuous motions could not be realized. Moreover, using forearm muscle activities would limit the use of the system for higher level amputation patients. Therefore, in this study we aimed to explore the feasibility of using an online classification algorithm to test the intention detection in real time. Experiments were conducted to record around-shoulder muscle activity using EMG and acceleration sensors. Then, a neural network was trained using these data, and finally tested online in a set of tests. Results showed that, from 5 channels of Electromyogram (EMG) and 4 channels of accelerometers, it is possible to discriminate 3 different grips and 5 reaching direction of arm.