# ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke

arXiv: https://arxiv.org/abs/2406.12123 · HTML: https://mlsanity.com/paper/2406.12123/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [EMG Pattern Classification to Control a Hand Orthosis for Functional Grasp Assistance after Stroke](https://mlsanity.com/paper/1802.00373/) (2018)
- [Multimodal Sensing and Interaction for a Robotic Hand Orthosis](https://mlsanity.com/paper/1808.00092/) (2018)
- [Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for Stroke](https://mlsanity.com/paper/2011.00034/) (2020)
- [Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke Hemiparesis](https://mlsanity.com/paper/2110.02258/) (2021)
- [Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke](https://mlsanity.com/paper/2403.13147/) (2024)

## Built on by (most cited first, 4 of 4)

- [EMGFlow: Robust and Efficient Surface Electromyography Synthesis via Flow Matching](https://mlsanity.com/paper/2604.13685/) (2026)
- [Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke](https://mlsanity.com/paper/2412.07956/) (2024)
- [SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition](https://mlsanity.com/paper/2511.08344/) (2025)
- [ReGlove: A Soft Pneumatic Glove for Activities of Daily Living Assistance via Wrist-Mounted Vision](https://mlsanity.com/paper/2512.11824/) (2025)

## Abstract (arXiv)

Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming. In this paper, we propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts (i.e., a given sequence of EMG signals). ChatEMG enables us to collect only a small dataset from the new condition, session, or subject and expand it with synthetic samples conditioned on prompts from this new context. ChatEMG leverages a vast repository of previous data via generative training while still remaining context-specific via prompting. Our experiments show that these synthetic samples are classifier-agnostic and can improve intent inferral accuracy for different types of classifiers. We demonstrate that our complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks. To the best of our knowledge, this is the first time an intent classifier trained partially on synthetic data has been deployed for functional control of an orthosis by a stroke survivor. Videos, source code, and additional information can be found at https://jxu.ai/chatemg.