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

arXiv: https://arxiv.org/abs/2309.08106 · HTML: https://mlsanity.com/paper/2309.08106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving](https://mlsanity.com/paper/2103.06113/) (2021)
- [Designing Environments Conducive to Interpretable Robot Behavior](https://mlsanity.com/paper/2007.00820/) (2020)

## Built on by (most cited first, 1 of 1)

- [Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses](https://mlsanity.com/paper/2607.07775/) (2026)

## Abstract (arXiv)

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.