BIOMECHANICAL ANALYSIS OF UPPER LIMB REHABILITATION DEVICE FOR GUIDING MOVEMENTS SELECTION

Authors

  • HaoYu Han College of Home and Art Design, Northeast Forestry University, Harbin 150000, Heilongjiang, China.
  • Li Feng (Corresponding Author) College of Home and Art Design, Northeast Forestry University, Harbin 150000, Heilongjiang, China.

Keywords:

Upper limb rehabilitation, Biomechanics, OpenSim, Muscle force analysis

Abstract

Purpose: The selection of appropriate rehabilitation movements is critical for the effective design of upper limb rehabilitation devices. Conventional approaches primarily rely on mechanical degrees of freedom, often neglecting quantitative biomechanical evaluation, which may lead to mismatches between device motion and human physiological characteristics. This study proposes a biomechanics-driven framework to guide rehabilitation movement selection. Methods: Four representative upper limb rehabilitation movements were selected based on the Brunnstrom recovery stages. Joint range of motion (ROM) data were collected from ten healthy subjects using clinical measurement methods. These kinematic data were imported into the OpenSim musculoskeletal model (DAS3) to simulate muscle forces of the deltoid, biceps brachii, and triceps brachii. Root mean square (RMS) values of muscle forces were calculated and statistically analyzed using one-way ANOVA and Tukey’s HSD post-hoc tests. Results: Significant differences in muscle activation were observed among the four movements. Horizontal adduction/abduction and flexion/extension exhibited significantly higher RMS muscle forces, particularly in the deltoid and triceps (p < 0.05). These movements demonstrated both higher peak forces and sustained muscle activation levels. Conclusion: The findings indicate that rehabilitation movements with higher biomechanical activation potential can be effectively identified through musculoskeletal simulation. Horizontal adduction/abduction and flexion/extension are recommended as optimal guiding movements for upper limb rehabilitation device design. This study provides a quantitative and physiologically grounded approach to improve the compatibility between rehabilitation devices and human motion.

References

[1] Wang Z, Hu S, Sang S, et al. Age-period-cohort analysis of stroke mortality in China. Stroke, 2017, 48(2): 271-275.

[2] Wang Y, Peng Q, Guo J, et al. Age-period-cohort analysis of type-specific stroke morbidity and mortality in China. Circulation Journal, 2020, 84: 662-669. DOI: 10.1253/circj.CJ-19-0903.

[3] Persson H C, Parziali M, Danielsson A, et al. Outcome and upper extremity function within 72 hours after first occasion of stroke in an unselected population at a stroke unit: a part of the SALGOT study. BMC Neurology, 2012, 12: 162. DOI: 10.1186/1471-2377-12-162.

[4] Yang S, Boudier-Revéret M, Kwon S, et al. Effect of diabetes on post-stroke recovery: a systematic narrative review. Frontiers in Neurology, 2021, 12: 747878. DOI: 10.3389/fneur.2021.747878.

[5] Sun J, Kramer E H, Rosen J. A safety-focused admittance control approach for physical human-robot interaction with rigid multi-arm serial link exoskeletons. IEEE/ASME Transactions on Mechatronics, 2025, 30: 414-425. DOI: 10.1109/TMECH.2024.3345678.

[6] Li X, Yang L, Wang X, et al. Research on upper limb rehabilitation product design based on NFBMS innovative design synthesis model. Journal of Graphics, 2025, 46(1): 211-220.

[7] Maas B, Van Der Sluis C K, Bongers R M, et al. Corrigendum: assessing effectiveness of serious game training designed to assist in upper limb prosthesis rehabilitation. Frontiers in Rehabilitation Sciences, 2024, 5: 1532227. DOI: 10.3389/fresc.2024.1532227.

[8] Flynn N, Froude E, Cooke D, et al. The sustainability of upper limb robotic therapy for stroke survivors in an inpatient rehabilitation setting. Disability and Rehabilitation, 2022, 44: 7522-7527. DOI: 10.1080/09638288.2021.1955307.

[9] Hynčík L, Čechová H, Bońkowski T, et al. Personalization of a human body model using subject-specific dimensions for designing clothing patterns. Applied Sciences, 2021, 11: 10138. DOI: 10.3390/app112110138.

[10] Langerak A J, D'Olivo P, Thijm O S A, et al. Stroke patients' motivation for home-based upper extremity rehabilitation with eHealth tools. Disability and Rehabilitation, 2024, 46: 5323-5333. DOI: 10.1080/09638288.2023.2245963.

[11] Ahmed T, Islam M R, Brahmi B, et al. Robustness and tracking performance evaluation of PID motion control of 7 DoF anthropomorphic exoskeleton robot assisted upper limb rehabilitation. Sensors, 2022, 22(10). DOI: 10.3390/s22103747.

[12] Sun J, Foroutani Y, Rosen J. Virtually constrained admittance control using feedback linearization for physical human-robot interaction with rehabilitation exoskeletons. IEEE/ASME Transactions on Mechatronics, 2025, 30: 898-909. DOI: 10.1109/TMECH.2024.3351234.

[13] Nikooyan A A, Veeger H E J, Westerhoff P, et al. Validation of the shoulder network model of the Delft Shoulder and Elbow Model (DSEM). Medical and Biological Engineering and Computing, 2011, 49(12): 1425-1435.

[14] Nikooyan A A, Veeger H E J, Van Der Helm F C T, et al. A musculoskeletal model of the upper extremity for simulating muscle forces and joint contact forces. Biological Cybernetics, 2012, 106(3): 189-204.

Downloads

Published

2026-04-16

How to Cite

HaoYu Han, Li Feng. Biomechanical Analysis Of Upper Limb Rehabilitation Device For Guiding Movements Selection. Eurasia Journal of Science and Technology. 2026, 8(2): 21-26. DOI: https://doi.org/10.61784/ejst3142.