Assessing the relevance of biosignal-controlled robotic rehabilitation technologies: a systematic review.

IF 7.7 Q1 ENGINEERING, BIOMEDICAL
Vittorio Santoriello, Giuseppe Cesarelli, Fanny Ficuciello, Carmine Giugliano, Francesca Angelone, Michela Russo, Alfonso Maria Ponsiglione, Maria Romano
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Abstract

Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.

评估生物信号控制机器人康复技术的相关性:系统综述。
通过提高可及性和个性化,技术正在改变康复医学。机器人辅助康复使用机器人系统从身体和神经损伤中恢复,实现高强度、重复性训练和实时反馈。这些系统旨在恢复运动功能和活动能力,并提高日常生活中的独立性,鉴于人口老龄化,这些需求正在增长。这篇综述的目的不是回顾控制算法或机械设计,而是研究生物信号和机器人康复系统的结合使用,并讨论它们在特定疾病临床框架之外的康复环境中的潜力。我们回顾了从2020年到2025年初发表的研究,以捕捉这一快速发展领域的最新进展。我们的目的是评估这些技术与康复结果的相关性,如运动功能或其他临床指标的改善。我们考虑了由生物信号驱动的机器人辅助系统,如肌电图(EMG)、脑电图或心电图,并且只纳入了报告可测量的康复结果的研究。按照PRISMA指南,我们检索了Scopus、PubMed和WoS数据库。在筛选的207条记录中,有15条符合纳入标准。总体而言,纳入的研究表明,当代生物信号控制的机器人康复,特别是肌电图驱动的方法,可能比单独的传统护理支持更密集、更个性化和更有吸引力的治疗,尽管目前的证据仍处于初步阶段。初步研究结果表明,通过闭合患者、设备和治疗师之间的循环,这些系统可以减少肌肉活动并支持运动表现的改善;然而,证据基础仍然是不一致的,而且普遍不够有力。为了支持未来的临床应用,该领域需要适当规模的正面试验,采用标准化的结果测量方法,更长的随访时间,透明地报告依从性、不良事件、用户体验和实施障碍,包括培训和成本。这些步骤对于进一步评估疗效和评估现实环境中的可用性非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
9.40
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