Soma Hirokazu, Horiuchi Yuse, Gonzalez Jose, Yu Wenwei
Medical System Engineering, Chiba University, Chiba, Japan.
IEEE Int Conf Rehabil Robot. 2011;2011:5975368. doi: 10.1109/ICORR.2011.5975368.
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.
最近，用于假肢控制目的的上肢运动意图检测吸引了越来越多的研究关注。在大多数研究中，前臂肌肉活动记录被用作信号源，利用模式识别技术从中检测手腕和手部运动的意图。然而，大多数日常生活中的上肢活动需要肩-臂-手复合体的协调。仅依靠局部信息来识别全身协调运动的缺点是容易发生误识别，从而无法实现稳定可靠的连续运动。此外，使用前臂肌肉活动会限制该系统在高位截肢患者中的应用。因此，在本研究中，我们旨在探索使用在线分类算法实时测试意图检测的可行性。进行实验以使用肌电图（EMG）和加速度传感器记录肩部周围肌肉活动。然后，使用这些数据训练神经网络，最后在一组测试中进行在线测试。结果表明，从5通道肌电图（EMG）和4通道加速度计中，可以区分3种不同的抓握方式和5种手臂伸展方向。
