ViT-MDHGR: Cross-Day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-03-17 DOI:10.1109/JSTSP.2024.3402340
Qin Hu;Golara Ahmadi Azar;Alyson Fletcher;Sundeep Rangan;S. Farokh Atashzar
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Abstract

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.
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ViT-MDHGR:通过 HD-sEMG 信号解码实现动态手势预测的跨天可靠性和敏捷性
由于表面肌电图(sEMG)和高密度 sEMG(HD-sEMG)生物信号能够以可穿戴和无创的方式进行手势识别/预测,因此已被广泛研究用于假肢设备的肌电控制、神经机器人以及最近的人机界面。据报道,该技术具有很高的日内(当天)性能。然而,由于传统方法在一段时间内的通用性较差,跨日(训练日和测试日分开)性能大大降低,阻碍了此类技术在现实生活中的应用。近期关于多天手势识别可行性的研究非常有限。现有的研究面临着一个重大挑战:由于需要较长的 sEMG 时间,相应的神经接口因肌电控制的延迟而变得不切实际。本文提出了一种基于 ViT 的紧凑型网络,用于多日动态手势预测。我们所提出的模型仅依赖于非常短的 HD-sEMG 信号窗口(即 50 毫秒,仅占实时肌电实施惯例的六分之一),提高了灵活性和响应性,从而解决了这一主要挑战。我们提出的模型可以预测 20 名受试者的 11 种动态手势,在训练后 3-25 天的测试日平均准确率超过 71%。此外,当仅对测试日的一小部分数据进行校准时,为了提高计算效率,我们提出的模型只需重新训练不到 10%的参数,就能达到 92% 以上的准确率。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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