Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.
Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.
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DOI：
10.3389/fneur.2017.00007
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发表时间：
2017
影响因子：
3.4
通讯作者：
Santello M
中科院分区：
文献类型：
--
作者：
Gailey A;Artemiadis P;Santello M
目前，上肢丧失的患者可以选择的选择范围从可以执行许多动作、但需要更多认知努力来控制的假手，到功能有限的更简单的终端设备。我们试图通过设计一种肌电控制系统来调节假手的姿势和指力分布来解决这个问题。我们记录了8名健康受试者前臂五块肌肉的表面肌电(EMG)信号，同时它们调制了手部姿势和单个手指屈曲力的分布。我们使用支持向量机(SVM)和随机森林回归(RFR)分别将肌电信号的特征映射到手势和个体指力。训练结束后，受试者执行抓握任务和手势，同时计算机程序计算并显示所有指力的在线反馈，其中指头被弯曲，以及接触力的大小。我们还使用了商业上可用的假手I-Limb(触摸仿生学)来提供建议方法控制手部姿势和指力的能力的实际演示。在在线测试中，受试者可以控制手部姿势和手指间的力分布。2个手指抓取和静止状态的解码成功率分别为60%(食指指向)和83~99%。受试者还可以调节手指的力量分布。该工作为支持向量机和RFR分别用于手势和指力分布的在线控制提供了概念证明。我们的方法在使用假手进行手部操作方面具有潜在的应用价值。
Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution. We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach’s ability to control hand posture and finger forces. Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83–99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution. This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.