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.

使用时间序列数据从肌电图电极和运动传感器中顺序地识别患者目标，研究结果表明，该目标识别方法比现有的机器学习技术在精确度和召回率方面都明显更好，并且当错误发生时表现得更加谨慎，这有利于模拟假肢的平滑运动。

使用过程挖掘技术的跨肩腕关节假肢的数据驱动目标识别