Advancing Precision Rehabilitation Through a Sensor-Based 6-DoF Robotic Exoskeleton: Clinical Validation and Ergonomic Assessment

Effective upper-extremity rehabilitation requires intensive and precise movement training, yet conventional therapies lack accurate motion tracking. Robotic exoskeletons address this limitation but are often hindered by ergonomic misalignment and limited adaptability. The AssistOn-Arm, a novel self-aligning exoskeleton, integrates ergonomic design and back-drivable actuation to enhance comfort and facilitate natural user interaction. This study aimed to assess the usability and ergonomics of the device in healthy participants and to conduct a pilot clinical evaluation in individuals with upper-extremity impairments. Thirty healthy participants and twelve patients with shoulder impairments performed predefined tasks under participant-active and device-active conditions. Kinematic data captured concurrently with AssistOn-Arm and Xsens MVN demonstrated strong agreement between conditions. Quantitative analysis revealed no significant differences (<i>p</i> > 0.05) in flexion, elevation, abduction-adduction, and external rotation, indicating reliable alignment with natural joint axes. Significant differences (<i>p</i> < 0.05) were observed only in sagittal hyperextension and internal rotation, reflecting device mechanical constraints. The study confirms the clinical feasibility of AssistOn-Arm as a sensor-driven, self-aligning exoskeleton that bridges engineering innovation and precision rehabilitation, paving the way for its integration into clinical practice.

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Publication Details

Journal
Sensors
Published
2025-12-23
DOI
https://doi.org/10.3390/s26010088
Citations
1
Primary Topic
Prosthetics and Rehabilitation Robotics
Type
article
Field-Weighted Citation Impact
0.67

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article

Advancing Precision Rehabilitation Through a Sensor-Based 6-DoF Robotic Exoskeleton: Clinical Validation and Ergonomic Assessment

Mehmet Alper Ergin, Volkan Patoğlu, Hande Argunsah, Gökay Çoruhlu et al.
1 citations
Sensors
Prosthetics and Rehabilitation Robotics
0.67
article

Advancing Precision Rehabilitation Through a Sensor-Based 6-DoF Robotic Exoskeleton: Clinical Validation and Ergonomic Assessment

Mehmet Alper Ergin, Volkan Patoğlu, Hande Argunsah, Gökay Çoruhlu, Zeynep Güven, Begüm Beril YALÇIN, M. Namık Yalçin
article en
1 citations

Abstract

Effective upper-extremity rehabilitation requires intensive and precise movement training, yet conventional therapies lack accurate motion tracking. Robotic exoskeletons address this limitation but are often hindered by ergonomic misalignment and limited adaptability. The AssistOn-Arm, a novel self-aligning exoskeleton, integrates ergonomic design and back-drivable actuation to enhance comfort and facilitate natural user interaction. This study aimed to assess the usability and ergonomics of the device in healthy participants and to conduct a pilot clinical evaluation in individuals with upper-extremity impairments. Thirty healthy participants and twelve patients with shoulder impairments performed predefined tasks under participant-active and device-active conditions. Kinematic data captured concurrently with AssistOn-Arm and Xsens MVN demonstrated strong agreement between conditions. Quantitative analysis revealed no significant differences (<i>p</i> > 0.05) in flexion, elevation, abduction-adduction, and external rotation, indicating reliable alignment with natural joint axes. Significant differences (<i>p</i> < 0.05) were observed only in sagittal hyperextension and internal rotation, reflecting device mechanical constraints. The study confirms the clinical feasibility of AssistOn-Arm as a sensor-driven, self-aligning exoskeleton that bridges engineering innovation and precision rehabilitation, paving the way for its integration into clinical practice.

SensorsVol. 26(1)
Kent Hastanesi (TR), Acıbadem Adana Hospital (TR), Istanbul University (TR)
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Prosthetics and Rehabilitation Robotics
0.67
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