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

TL;DR

This paper presents a novel approach using process mining techniques to recognize goals in transhumeral prostheses from time series sensor data, improving accuracy and confidence over existing methods.

Contribution

It introduces a process mining-based goal recognition method that effectively utilizes sequential sensor data, outperforming traditional machine learning approaches in prosthetic control.

Findings

Achieves higher precision and recall than state-of-the-art methods.

Less confident when incorrect, leading to smoother prosthetic movements.

Validated with data from ten subjects in a virtual reality setting.

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.