George Jacob A, Brinton Mark R, Duncan Christopher C, Hutchinson Douglas T, Clark Gregory A
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:3782-3787. doi: 10.1109/EMBC.2018.8513342.
Working towards improved neuromyoelectric control of dexterous prosthetic hands, we explored how differences in training paradigms affect the subsequent online performance of two different motor-decode algorithms. Participants included two intact subjects and one participant who had undergone a recent transradial amputation after complex regional pain syndrome (CRPS) and multi-year disuse of the affected hand. During algorithm training sessions, participants actively mimicked hand movements appearing on a computer monitor. We varied both the duration of the hold-time (0.1 s or 5 s) at the end-point of each of six different digit and wrist movements, and the order in which the training movements were presented (random or sequential). We quantified the impact of these variations on two different motordecode algorithms, both having proportional, six-degree-offreedom (DOF) control: a modified Kalman filter (MKF) previously reported by this group, and a new approach - a convolutional neural network (CNN). Results showed that increasing the hold-time in the training set improved run-time performance. By contrast, presenting training movements in either random or sequential order had a variable and relatively modest effect on performance. The relative performance of the two decode algorithms varied according to the performance metric. This work represents the first-ever amputee use of a CNN for real-time, proportional six-DOF control of a prosthetic hand. Also novel was the testing of implanted high-channelcount devices for neuromyoelectric control shortly after amputation, following CRPS and long-term hand disuse. This work identifies key factors in the training of decode algorithms that improve their subsequent run-time performance.
为了改进灵巧假手的神经肌电控制，我们探讨了训练范式的差异如何影响两种不同运动解码算法的后续在线性能。参与者包括两名肢体健全的受试者和一名最近因复杂性区域疼痛综合征（CRPS）以及受影响手多年未使用而接受经桡骨截肢的受试者。在算法训练过程中，参与者积极模仿出现在电脑显示器上的手部动作。我们改变了六个不同手指和手腕动作中每个动作终点处的保持时间（0.1秒或5秒），以及训练动作呈现的顺序（随机或顺序）。我们量化了这些变化对两种不同的运动解码算法的影响，这两种算法都具有比例式六自由度（DOF）控制：该团队之前报道的一种改进卡尔曼滤波器（MKF），以及一种新方法——卷积神经网络（CNN）。结果表明，增加训练集中的保持时间可提高运行时性能。相比之下，以随机或顺序方式呈现训练动作对性能的影响可变且相对较小。两种解码算法的相对性能根据性能指标而有所不同。这项工作代表了首次有截肢者使用CNN对假手进行实时、比例式六自由度控制。同样新颖的是，在截肢后不久，针对因CRPS和长期手部未使用而植入的高通道数神经肌电控制设备进行测试。这项工作确定了训练解码算法时可提高其后续运行时性能的关键因素。
