Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024)

Information Fusion, Год журнала: 2025, Номер 118, С. 102982 - 102982

Опубликована: Янв. 30, 2025

Язык: Английский

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Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review

Medical & Biological Engineering & Computing, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

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 assistance according the patient's performance, aiming enhance engagement and recovery individuals with motor impairments. categorizes implemented PPA literature first time such a wide scope suggests future research directions improve adaptive personalized therapy. At first, studies are examined evaluate methods, which subsequently categorized their underlying implementation strategies: position error-based force-based electromyography (EMG), electroencephalography (EEG)-based indicator-based physiological signal-based methods. The advantages limitations of each method discussed. In addition classification current study also examines clinically tested applied rehabilitation clinical outcomes. Clinical findings from these trials demonstrate potential improving function engagement. Nevertheless, more extensive testing is necessary establish long-term benefits over conventional therapies. Ultimately, this guide developments field rehabilitation, providing researchers insights into optimizing enhanced

Язык: Английский

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Improving Access Trust in Healthcare Through Multimodal Deep Learning for Affective Computing

Human-Centric Intelligent Systems, Год журнала: 2024, Номер 4(4), С. 511 - 526

Опубликована: Авг. 29, 2024

Abstract In healthcare domain, access trust is of prime importance paramount to ensure effective delivery medical services. It also fosters positive patient-provider relationships. With the advancement technology, affective computing has emerged as a promising approach enhance trust. enables systems understand and respond human emotions. The research work investigates application multimodal deep learning techniques in improve environment. A novel algorithm, "Belief-Emo-Fusion," proposed, aiming understanding interpretation emotions healthcare. conducts comprehensive simulation analysis, comparing performance Belief-Emo-Fusion with existing algorithms using metrics: modal accuracy, ınference time, F1-score. study emphasizes emotion recognition settings. highlights role models facilitating empathetic emotionally intelligent technologies. By addressing challenges associated computing, proposed contributes development more reliable systems. findings offer valuable insights for researchers practitioners seeking leverage enhancing communication environments.

Язык: Английский

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Securing Healthcare AI: Applied Federal Learning

Studies in systems, decision and control, Год журнала: 2024, Номер unknown, С. 255 - 272

Опубликована: Янв. 1, 2024

Язык: Английский

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