<ul><li>Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers. In this paper, the authors propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts. ChatEMG enables them to collect only a small dataset and expand it with synthetic samples conditioned on prompts. Experimental results show that these synthetic samples can improve intent inferral accuracy for different types of classifiers.</li><li>The authors demonstrate that their complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks.</li><li>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.</li><li>Videos, source code, and additional information can be found at https://jxu.ai/chatemg.</li></ul>

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

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