Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Lengghalde 5, 8008, Zurich, Switzerland.
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Lengghalde 5, 8008, Zurich, Switzerland.
J Neuroeng Rehabil. 2023 Aug 3;20(1):101. doi: 10.1186/s12984-023-01222-8.
Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated.
In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance.
We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance.
The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.
辅助机器人手矫形器可以在许多日常生活活动中支持感觉运动手损伤的人，从而帮助他们重新获得独立性。然而，为了让用户充分利用这些设备的功能，安全可靠地检测他们的运动意图以进行设备控制是至关重要的。基于力肌电图测量肌肉收缩时体积变化的手势识别已被证明是控制手部假肢的一种可行且易于实施的策略。这种方法是否可以有效地应用于直观地控制辅助机器人手矫形器仍有待研究。
在这项工作中，我们评估了使用从前臂测量的力肌电图来控制佩戴在手测量部位同侧的机器人手矫形器的可行性。在十个神经健全的参与者佩戴机器人手矫形器的情况下，我们收集了针对在九个手臂配置中训练的四个手势的数据，即七个静态位置和两个动态运动，对应于典型的日常生活活动条件。在离线分析中，我们确定了两个二进制分类器（一个用于打开，一个用于关闭）的分类准确率，并进一步评估了个体训练手臂配置对整体性能的影响。
我们实现了 92.9%的总体分类准确率（平均两个二进制分类器，个体准确率分别为 95.5%和 90.3%），但发现参与者之间的表现差异很大，范围从 75.4%到 100%。每个样本的平均推断时间低于 0.15 毫秒。此外，我们发现，在不明显降低分类性能的情况下，训练手臂配置的数量可以从九个减少到六个。
这项工作的结果支持使用力肌电图作为机器人手矫形器的直观意图检测策略的一般可行性。此外，这些发现还为在日常生活活动中为感觉运动手损伤的人提供辅助时应用此类技术所面临的挑战和潜在解决方案提供了有价值的见解。
