Cipriani Christian, Sassu Rossella, Controzzi Marco, Carrozza Maria Chiara
BioRobotics Institute of the Scuola Superiore Sant'Anna, 56025 Pontedera, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1620-3. doi: 10.1109/IEMBS.2011.6090468.
In transradial amputees, the muscles in the residual forearm naturally employed by the unimpaired for flexing/extending the hand fingers, are the most appropriate targets, for multi-fingered prostheses control. However, once the prosthetic socket is manufactured and fitted on the residual forearm, the recorded EMG might not be originated only by the intention of performing finger movements, but also by the muscular activity needed to sustain the prosthesis itself. In this work, we preliminary show--on healthy subjects wearing a prosthetic socket emulator--that (i) variations in the weight of the prosthesis, and (ii) upper arm movements significantly influence the robustness of a traditional classifier based on k-nn algorithm. We show in simulated conditions that traditional pattern recognition systems do not allow the separation of the effects of the weight of the prosthesis because a surface recorded EMG pattern caused by the simple lifting or moving of the prosthesis is misclassified into a hand control movement. This suggests that a robust classifier should add to myoelectric signals, inertial transducers like multi-axes position, acceleration sensors or sensors able to monitor the interaction forces between the socket and the end-effector.
在经桡骨截肢者中，未受损时用于弯曲/伸展手指的残留前臂肌肉，是多指假肢控制最合适的目标。然而，一旦制作好假肢接受腔并安装在残留前臂上，记录的肌电图可能不仅源于手指运动的意图，还源于维持假肢本身所需的肌肉活动。在这项工作中，我们在佩戴假肢接受腔模拟器的健康受试者身上初步表明：（i）假肢重量的变化，以及（ii）上臂运动，会显著影响基于k近邻算法的传统分类器的稳健性。我们在模拟条件下表明，传统模式识别系统无法区分假肢重量的影响，因为由简单抬起或移动假肢引起的表面记录肌电图模式会被误分类为手部控制运动。这表明，一个稳健的分类器应该在肌电信号之外，增加惯性传感器，如多轴位置、加速度传感器或能够监测接受腔与末端执行器之间相互作用力的传感器。
