Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques
This work addresses goal recognition for prosthetic limb control, which is incremental as it applies an existing process mining method to new sensor data.
The paper tackled the problem of recognizing patient target poses for transhumeral prostheses using time series data, achieving significantly better precision and recall than state-of-the-art machine learning techniques in a virtual reality setting with ten subjects.
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.