用视觉识别手势和意图，让假手更自然地抓取物体。
A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp
- 通过手部几何特征建模手势，实现视觉驱动的抓握控制。
- 抓取成功率95.43%，动作相似度达0.911，耗时约3.07秒。
- 适合想提升假手自然性和智能化的康复与机器人研究者。
仿生抓握过程显著提升假手使用者的体验与抓取效率。当前基于脑机接口（BCI）和肌电（EMG）信号控制的假手难以精准识别截肢者的抓握手势，也难以实现仿生抓握。尽管配备视觉系统的假手可识别物体特征，但缺乏对人类抓握意图的感知。为此，本文提出基于空间几何的手势映射（SG-GM）方法，利用手部抓握过程的几何特征构建手势函数，并在假手上实现；同时提出基于运动轨迹回归的抓握意图估计（MTR-GIE）算法，通过回归预测结合先验空间分割，推断抓握目标。实验针对8种日常物品（如杯子、叉子）进行抓取测试，结果表明抓握过程相似系数 $R^{2}$ 达0.911，均方根误差（RMSE）为2.47°，抓取成功率95.43%，平均抓取时长为3.07±0.41秒。多物体环境下的意图估计平均准确率达94.35%。本方法为提升假手功能提供了新范式。
原文摘要 · Abstract (English)
The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects' feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It's subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand's position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient $R^{2}$ of grasping process of 0.911, a Root Mean Squared Error ($RMSE$) of 2.47\degree, a success rate of grasping of 95.43$\%$, and an average duration of grasping process of 3.07$\pm$0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35$\%$. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand's functionality and provides valuable insights for future research.
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