{"title":"Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review.","authors":"Erkan Ödemiş, Cabbar Veysel Baysal, Mustafa İnci","doi":"10.1007/s11517-025-03315-z","DOIUrl":null,"url":null,"abstract":"<p><p>This paper aims to comprehensively review patient performance assessment (PPA) methods used in assist-as-needed (AAN) robotic therapy for upper extremity rehabilitation. AAN strategies adjust robotic assistance according to the patient's performance, aiming to enhance engagement and recovery in individuals with motor impairments. This review categorizes the implemented PPA methods in the literature for the first time in such a wide scope and suggests future research directions to improve adaptive and personalized therapy. At first, the studies are examined to evaluate PPA methods, which are subsequently categorized according to their underlying implementation strategies: position error-based methods, force-based methods, electromyography (EMG), electroencephalography (EEG)-based methods, performance indicator-based methods, and physiological signal-based methods. The advantages and limitations of each method are discussed. In addition to the classification of PPA methods, the current study also examines clinically tested AAN strategies applied in upper extremity rehabilitation and their clinical outcomes. Clinical findings from these trials demonstrate the potential of AAN strategies in improving motor function and patient engagement. Nevertheless, more extensive clinical testing is necessary to establish the long-term benefits of these strategies over conventional therapies. Ultimately, this review aims to guide future developments in the field of robotic rehabilitation, providing researchers with insights into optimizing AAN strategies for enhanced patient outcomes.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical & Biological Engineering & Computing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11517-025-03315-z","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper aims to comprehensively review patient performance assessment (PPA) methods used in assist-as-needed (AAN) robotic therapy for upper extremity rehabilitation. AAN strategies adjust robotic assistance according to the patient's performance, aiming to enhance engagement and recovery in individuals with motor impairments. This review categorizes the implemented PPA methods in the literature for the first time in such a wide scope and suggests future research directions to improve adaptive and personalized therapy. At first, the studies are examined to evaluate PPA methods, which are subsequently categorized according to their underlying implementation strategies: position error-based methods, force-based methods, electromyography (EMG), electroencephalography (EEG)-based methods, performance indicator-based methods, and physiological signal-based methods. The advantages and limitations of each method are discussed. In addition to the classification of PPA methods, the current study also examines clinically tested AAN strategies applied in upper extremity rehabilitation and their clinical outcomes. Clinical findings from these trials demonstrate the potential of AAN strategies in improving motor function and patient engagement. Nevertheless, more extensive clinical testing is necessary to establish the long-term benefits of these strategies over conventional therapies. Ultimately, this review aims to guide future developments in the field of robotic rehabilitation, providing researchers with insights into optimizing AAN strategies for enhanced patient outcomes.
期刊介绍:
Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging.
MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field.
MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).