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

Clinical Snapshot

Key Findings

• P Value: Not reported at the review level; individual study-level statistics not summarised in the abstract

• Effect Size: Not quantified; no pooled effect size reported. Narrative synthesis suggests directionally positive effects for EMG-driven systems on motor performance outcomes

• Primary Outcome: Improvements in motor function and reduction in muscle activity (EMG-measured) in patients undergoing biosignal-controlled robotic rehabilitation, assessed narratively across 15 included studies

• Nnt Or Sensitivity: Not calculable from available data; no NNT, sensitivity/specificity, or hazard ratio reported. The absence of quantitative synthesis is a critical limitation for clinical decision-making

• Confidence Interval: Not reported; no meta-analytic synthesis performed

Clinical Application

Biosignal-controlled robotic rehabilitation systems (particularly EMG-driven exoskeletons and end-effector devices) are technically feasible in well-resourced rehabilitation centres. However, barriers to widespread clinical adoption remain substantial: high equipment costs, requirement for specialist technical training, lack of standardised protocols, and absence of robust safety and usability data. The technology readiness level for routine clinical use appears low based on the current evidence base. In Australia, robotic rehabilitation technologies are not currently listed on the Pharmaceutical Benefits Scheme (PBS), as they are devices rather than pharmaceuticals. Funding pathways may exist through the National Disability Insurance Scheme (NDIS) for eligible participants, or through private health insurance extras cover, though coverage is inconsistent. The Therapeutic Goods Administration (TGA) regulates medical devices including robotic rehabilitation systems under the Australian Register of Therapeutic Goods (ARTG); any commercially deployed system would require appropriate classification and registration. The RACGP and the Australasian Faculty of Rehabilitation Medicine (AFRM) have not issued specific guidelines on biosignal-controlled robotic rehabilitation, reflecting the early-stage evidence base. Australian rehabilitation services, particularly in rural and remote settings, face additional access barriers that make the scalability of these technologies uncertain. The growing burden of stroke and neurological disability in an ageing Australian population (consistent with the global trends cited by the authors) provides a compelling rationale for continued investment in this research area, but current evidence does not support routine clinical adoption. Adults with physical or neurological impairments requiring motor rehabilitation — including but not limited to stroke survivors, spinal cord injury patients, and those with neurodegenerative conditions affecting motor function. The disease-agnostic framing means no single patient group can be identified as the primary beneficiary from current evidence.

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.

References

1. 1.Santoriello, V., Cesarelli, G., Ficuciello, F., Giugliano, C., Angelone, F., Russo, M., Ponsiglione, A. M., & Romano, M. (2026). Assessing the relevance of biosignal-controlled robotic rehabilitation technologies: a systematic review. Progress in Biomedical Engineering. https://doi.org/10.1088/2516-1091/ae7862