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Ödemiş E, Baysal CV. Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises. Med Biol Eng Comput 2024; 62:1441-1457. [PMID: 38231343 PMCID: PMC11021326 DOI: 10.1007/s11517-023-03014-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/29/2023] [Indexed: 01/18/2024]
Abstract
In conventional and robotic rehabilitation, the patient's active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients' therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients' progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients' improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients' participation during the rehabilitation exercises.
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Affiliation(s)
- Erkan Ödemiş
- Department of Biomedical Engineering, Çukurova University, 01330, Saricam, Adana, Turkey.
| | - Cabbar Veysel Baysal
- Department of Biomedical Engineering, Çukurova University, 01330, Saricam, Adana, Turkey
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Dalla Gasperina S, Roveda L, Pedrocchi A, Braghin F, Gandolla M. Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons. Front Robot AI 2021; 8:745018. [PMID: 34950707 PMCID: PMC8688994 DOI: 10.3389/frobt.2021.745018] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/25/2021] [Indexed: 01/09/2023] Open
Abstract
Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.
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Affiliation(s)
- Stefano Dalla Gasperina
- NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,WE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy
| | - Loris Roveda
- Istituto Dalle Molle di studi sull'Intelligenza Artificiale (IDSIA), USI-SUPSI, Lugano, Switzerland
| | - Alessandra Pedrocchi
- NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,WE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy
| | - Francesco Braghin
- WE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy.,Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
| | - Marta Gandolla
- WE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy.,Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
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