2 research outputs found
ViT-MDHGR: Cross-day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding
Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) biosignals
have been extensively investigated for myoelectric control of prosthetic
devices, neurorobotics, and more recently human-computer interfaces because of
their capability for hand gesture recognition/prediction in a wearable and
non-invasive manner. High intraday (same-day) performance has been reported.
However, the interday performance (separating training and testing days) is
substantially degraded due to the poor generalizability of conventional
approaches over time, hindering the application of such techniques in real-life
practices. There are limited recent studies on the feasibility of multi-day
hand gesture recognition. The existing studies face a major challenge: the need
for long sEMG epochs makes the corresponding neural interfaces impractical due
to the induced delay in myoelectric control. This paper proposes a compact
ViT-based network for multi-day dynamic hand gesture prediction. We tackle the
main challenge as the proposed model only relies on very short HD-sEMG signal
windows (i.e., 50 ms, accounting for only one-sixth of the convention for
real-time myoelectric implementation), boosting agility and responsiveness. Our
proposed model can predict 11 dynamic gestures for 20 subjects with an average
accuracy of over 71% on the testing day, 3-25 days after training. Moreover,
when calibrated on just a small portion of data from the testing day, the
proposed model can achieve over 92% accuracy by retraining less than 10% of the
parameters for computational efficiency
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
Hand gesture recognition (HGR) has gained significant attention due to the
increasing use of AI-powered human-computer interfaces that can interpret the
deep spatiotemporal dynamics of biosignals from the peripheral nervous system,
such as surface electromyography (sEMG). These interfaces have a range of
applications, including the control of extended reality, agile prosthetics, and
exoskeletons. However, the natural variability of sEMG among individuals has
led researchers to focus on subject-specific solutions. Deep learning methods,
which often have complex structures, are particularly data-hungry and can be
time-consuming to train, making them less practical for subject-specific
applications. In this paper, we propose and develop a generalizable, sequential
decoder of transient high-density sEMG (HD-sEMG) that achieves 73% average
accuracy on 65 gestures for partially-observed subjects through
subject-embedded transfer learning, leveraging pre-knowledge of HGR acquired
during pre-training. The use of transient HD-sEMG before gesture stabilization
allows us to predict gestures with the ultimate goal of counterbalancing system
control delays. The results show that the proposed generalized models
significantly outperform subject-specific approaches, especially when the
training data is limited, and there is a significant number of gesture classes.
By building on pre-knowledge and incorporating a multiplicative
subject-embedded structure, our method comparatively achieves more than 13%
average accuracy across partially observed subjects with minimal data
availability. This work highlights the potential of HD-sEMG and demonstrates
the benefits of modeling common patterns across users to reduce the need for
large amounts of data for new users, enhancing practicality