2 citations · 3 across the 6 of their papers we have counts for
6 papers
ReactEMG Stroke: Healthy-to-Stroke Few-shot Adaptation for sEMG-Based Intent Detection
Runsheng Wang, Katelyn Lee, Xinyue Zhu +4
Surface electromyography (sEMG) is a promising control signal for assist-as-needed hand rehabilitation after stroke, but detecting intent from paretic muscles often requires length…
ReactEMG: Stable, Low-Latency Intent Detection from sEMG via Masked Modeling
Runsheng Wang, Xinyue Zhu, Ava Chen +5
Surface electromyography (sEMG) signals show promise for effective human-machine interfaces, particularly in rehabilitation and prosthetics. However, challenges remain in developin…
Reciprocal Learning of Intent Inferral with Augmented Visual Feedback for Stroke
Jingxi Xu, Ava Chen, Lauren Winterbottom +5
Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent…
Fabric Sensing of Intrinsic Hand Muscle Activity
Katelyn Lee, Runsheng Wang, Ava Chen +9
Wearable robotics have the capacity to assist stroke survivors in assisting and rehabilitating hand function. Many devices that use surface electromyographic (sEMG) for control rel…
ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke
Jingxi Xu, Runsheng Wang, Siqi Shang +10
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 diff…
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
Pedro Leandro La Rotta, Jingxi Xu, Ava Chen +5
We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and r…