Strazzulla Ilaria, Nowak Markus, Controzzi Marco, Cipriani Christian, Castellini Claudio
IEEE Trans Neural Syst Rehabil Eng. 2017 Mar;25(3):227-234. doi: 10.1109/TNSRE.2016.2554884. Epub 2016 Apr 27.
The paradigm of simultaneous and proportional myocontrol of hand prostheses is gaining momentum in the rehabilitation robotics community. As opposed to the traditional surface electromyography classification schema, in simultaneous and proportional control the desired force/torque at each degree of freedom of the hand/wrist is predicted in real-time, giving to the individual a more natural experience, reducing the cognitive effort and improving his dexterity in daily-life activities. In this study we apply such an approach in a realistic manipulation scenario, using 10 non-linear incremental regression machines to predict the desired torques for each motor of two robotic hands. The prediction is enforced using two sets of surface electromyography electrodes and an incremental, non-linear machine learning technique called Incremental Ridge Regression with Random Fourier Features. Nine able-bodied subjects were engaged in a functional test with the aim to evaluate the performance of the system. The robotic hands were mounted on two hand/wrist orthopedic splints worn by healthy subjects and controlled online. An average completion rate of more than 95% was achieved in single-handed tasks and 84% in bimanual tasks. On average, 5 min of retraining were necessary on a total session duration of about 1 h and 40 min. This work sets a beginning in the study of bimanual manipulation with prostheses and will be carried on through experiments in unilateral and bilateral upper limb amputees thus increasing its scientific value.
手部假肢同步和比例肌电控制模式在康复机器人领域正日益受到关注。与传统的表面肌电分类模式不同，在同步和比例控制中，手部/腕部每个自由度上所需的力/扭矩能够实时预测，从而为使用者带来更自然的体验，减少认知负担，并提高其在日常生活活动中的灵活性。在本研究中，我们将这种方法应用于一个实际操作场景，使用10台非线性增量回归机器来预测两个机器人手部每个电机所需的扭矩。预测通过两组表面肌电电极以及一种名为具有随机傅里叶特征的增量岭回归的增量非线性机器学习技术来实现。九名身体健全的受试者参与了一项功能测试，旨在评估该系统的性能。机器人手安装在健康受试者佩戴的两个手部/腕部矫形夹板上并进行在线控制。单手任务的平均完成率超过95%，双手任务的平均完成率为84%。在大约1小时40分钟的总训练时间内，平均需要5分钟的再训练。这项工作开启了假肢双手操作研究的先河，并将通过对单侧和双侧上肢截肢者进行实验继续开展，从而提高其科学价值。
