A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

翻译：经肱骨假体可重建包括手部在内的肩部以下缺失解剖结构。主动式假体利用实值连续传感器数据识别患者目标姿态（即目标），并主动驱动人工肢体运动。既往研究考察了在未考虑时间步长的静止姿态下采集的数据对目标区分的有效性。在本案例研究论文中，我们聚焦于利用表面肌电电极和运动学传感器的时间序列数据来顺序识别患者目标。我们的方法涉及将数据转化为离散事件，并训练现有的基于过程挖掘的目标识别系统。在虚拟现实环境中采集的十名受试者数据表明，我们提出的目标识别方法具有有效性，其精确率和召回率显著优于当前最先进的机器学习技术，且在预测错误时置信度更低——这有助于实现更平滑的假体运动逼近。