1 Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA.
2 TIRR Memorial Hermann Research Center, 1333B Moursund St., Houston, TX, USA.
Int J Neural Syst. 2017 Aug;27(5):1750009. doi: 10.1142/S0129065717500095. Epub 2016 Oct 6.
Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.
机器人辅助训练为神经损伤康复提供了一种有效的方法。为了应对神经损伤后手康复的挑战，本研究提出了一种先进的肌电模式识别方案，用于对手外骨骼进行实时意图驱动控制。所开发的方案使用从前臂和手部肌肉采集的四个通道的表面肌电图（EMG）信号来检测和识别用户进行六种不同手部运动的意图，然后驱动外骨骼辅助用户完成预期的运动。该系统在 8 名神经正常的受试者和 2 名脊髓损伤（SCI）受试者中进行了测试。神经正常的受试者的整体控制精度为[Formula: see text]，而 SCI 受试者的控制精度为[Formula: see text]。系统的总滞后时间约为 250[Formula: see text]ms，包括数据采集、传输和处理。一位 SCI 受试者还在他的第二次和第三次就诊中参加了训练课程。控制精度和效率都有提高的趋势。这些结果表明，可将先进的肌电模式识别控制应用于可穿戴机器人手系统，以改善神经损伤后的手部功能，具有很大的潜力。
