Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques

• URL: http://arxiv.org/abs/2309.08106v1
• Date: Fri, 15 Sep 2023 02:03:59 GMT
• Title: Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques
• Authors: Zihang Su, Tianshi Yu, Nir Lipovetzky, Alireza Mohammadi, Denny Oetomo, Artem Polyvyanyy, Sebastian Sardina, Ying Tan, Nick van Beest
• Abstract summary: 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. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system.
• Score: 7.95507524742396
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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