通过渐进式学习进行肌电控制的(无)监督(Co)适应:动机、回顾和未来方向。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Evan Campbell;Fabio Egle;Marius OßWald;Ulysse Côté-Allard;Patrick M. Pilarski;Nicolò Boccardo;Roberto Meattini;Ivan Vujaklija;Levi Hargrove;Michele Canepa;Ethan Eddy;Alessandro Del Vecchio;Claudio Castellini;Erik Scheme
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引用次数: 0

摘要

本文介绍了肌电控制的增量学习方法的叙述回顾,概述了自适应假肢系统的历史轨迹和潜力。传统的肌电控制已经从直接控制技术发展到先进的模式识别,但持续存在的挑战,如信号非平稳性,因此,需要频繁的重新校准。增量学习可以通过基于实时、用户在环数据的不断更新控制模型来实现范式转换,从而解决用户特定的变化、环境变化以及基于屏幕指导的训练校准的挑战。该论文的一个核心贡献是其增量学习策略的分类,该分类将该领域分为四类:专用按需重新校准、无监督增量学习、依赖预测器的增量学习和依赖环境的增量学习。讨论了每个类别的方法、优势和局限性,为评估当前的研究和指导未来的创新提供了一个清晰的框架。此外,本工作建立了三种增量学习设置:领域增量、任务增量和类增量持续学习。此外,本文还重点介绍了迁移学习、领域适应和自监督回归等新兴趋势。它还强调了生理启发算法的潜力,提高假肢性能的新型末端执行器设计,以及人与设备的共同适应。最后,本文讨论了增量学习的开放挑战,如信号变化归因于噪声与行为,模型复杂性与数据需求,以及用户与模型适应。总的来说,这些见解为下一代肌电系统铺平了道路,这些系统更强大、更直观,并能适应用户的动态需求和行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
(Un)supervised (Co)adaptation via Incremental Learning for Myoelectric Control: Motivation, Review, and Future Directions
This paper presents a narrative review of incremental learning methods for myoelectric control, outlining both the historical trajectory and potential of adaptive prosthetic systems. Traditional myoelectric control has evolved from direct control techniques to advanced pattern recognition, yet persistent challenges such as signal non-stationarities and, consequently, the need for frequent recalibration remain. Incremental learning may enable a paradigm shift by continuously updating control models based on real-time, user-in-the-loop data, thereby addressing user-specific variations, environmental changes, and challenges from screen-guided-training based calibration. A central contribution of the paper is its taxonomy of incremental learning strategies, which divides the field into four categories: dedicated on-demand recalibration, unsupervised incremental learning, predictor-dependent incremental learning, and environment-dependent incremental learning. The methodology, strengths, and limitations of each category are discussed, providing a clear framework for evaluating current research and guiding future innovations. Further, this work establishes three settings for incremental learning: domain-incremental, task-incremental, and class-incremental continual learning. In addition, the paper highlights emerging trends such as transfer learning, domain adaptation, and self-supervised regression. It also emphasizes the potential of physiologically-inspired algorithms, novel end-effector designs to enhance prosthetic performance, and human-device co-adaptation. Finally, this paper discusses open challenges for incremental learning like attribution of signal changes to noise vs. behaviours, model complexity vs. data requirements, and user vs. model adaptation. Collectively, these insights pave the way for next-generation myoelectric systems that are more robust, intuitive, and adaptable to the dynamic needs and behaviours of users.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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