Hostname: page-component-5cf477f64f-tx7qf Total loading time: 0 Render date: 2025-04-07T18:43:06.608Z Has data issue: false hasContentIssue false
  • English
  • Français

A methodology to quantify human-robot interaction forces: a case study of a 4-DOFs upper extremity rehabilitation robot

Published online by Cambridge University Press:  02 April 2025

Qiang Cao ,
Lei Li Open the ORCID record for Lei Li [Opens in a new window] ,
Jianfeng Li ,
Rui Li  and
Xun Wang
Qiang Cao
Affiliation:
Kaiserslautern Academy of Smart Manufacturing, Shanghai DianJi University, Shanghai, 201306, China
Lei Li*
Affiliation:
The College of Machine, Shanghai DianJi University, Shanghai, 201306, China
Jianfeng Li
Affiliation:
The College of Mechanical Engineering and Applied Electronic Technology, Beijing University of Technology, Beijing, 100124, China.
Rui Li
Affiliation:
Kaiserslautern Academy of Smart Manufacturing, Shanghai DianJi University, Shanghai, 201306, China
Xun Wang
Affiliation:
Kaiserslautern Academy of Smart Manufacturing, Shanghai DianJi University, Shanghai, 201306, China
*
Corresponding author: Lei Li; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Upper extremity rehabilitation robots have become crucial in stroke rehabilitation due to their high durability, repeatability, and task-specific capabilities. A significant challenge in assessing the comfort performance of these robots is accurately calculating the human-robot interaction forces. In this study, a four-degree-of-freedom (4-DOF) upper extremity rehabilitation robot mechanism, kinematically compatible with the human upper limb, is proposed. Based on this mechanism, an algorithm for estimating human-robot interaction forces is developed using Newton-Euler dynamics. A prototype of the proposed robot is constructed, and a series of comparative experiments are carried out to validate the feasibility of the proposed force estimation approach. The results indicate that the proposed method reliably predicts interaction forces with minimal deviation from experimental data, demonstrating its potential for application in upper limb rehabilitation robots. This work provides a foundation for future studies focused on comfort evaluation and optimization of rehabilitation robots, with significant practical implications for improving patient rehabilitation outcomes.

Keywords

upper extremityrehabilitation robotkinematic compatibilityNewton-Euler dynamicinteraction forces
Type
Research Article
Information
Robotica , First View , pp. 1 - 22
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Jiang, J., Li, W. and Lee, K. M., “A novel pantographic exoskeleton based collocated joint design with application for early stroke rehabilitation,” IEEE/ASME Trans. Mechatron. 25(4), 1922–1932 (2020).CrossRefGoogle Scholar
Pang, Z., Wang, T., Wang, Z., et al., “Design and analysis of a wearable upper limb rehabilitation robot with characteristics of tension mechanism,” Appl. Sci. 10(6), 2101 (2020).CrossRefGoogle Scholar
Nicholson-Smith, C., Mehrabi, V., Atashzar, S. F., et al., “A multi-functional lower- and upper-limb stroke rehabilitation robot,” IEEE Trans. Med. Robot. Bionics 2(4), 549–552 (2020).CrossRefGoogle Scholar
Thompson, S., Ranta, A., Porter, K., et al., “How much rehabilitation are our patients with stroke receiving?,” NZ Med J 132(1499), 49–55 (2019).Google ScholarPubMed
Jo, J. K., et al., “Effect of starting penile rehabilitation with sildenafil immediately after robot-assisted laparoscopic radical prostatectomy on erectile function recovery: a prospective randomized trial,” J Urol 199(6), 1601–1607 (2018).Google ScholarPubMed
Zhang, W., Lu, S., Wu, L., et al., “Master-slave upper-limb exoskeleton rehabilitation robot training control method based on fuzzy compensation,” Robot 41(1), 104–111 (2019).Google Scholar
Brahmi, B., Saad, M., Luna, C. O., et al., “Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties,” Robotica 36(11), 1757–1779 (2018).CrossRefGoogle Scholar
Sarac, M., Solazzi, M. and Frisoli, A., “Design requirements of generic hand exoskeletons and survey of hand exoskeletons for rehabilitation, assistive or haptic use,” IEEE Trans. Haptics 12(4), 400–413 (2019).CrossRefGoogle ScholarPubMed
Song, A., Pan, L., Xu, G. and Li, H., “Adaptive motion control of arm rehabilitation robot based on impedance identification,” Robotica 33(9), 1795–1812 (2015).CrossRefGoogle Scholar
Epple, C., Maurer-Burkhard, B., Lichti, M. C., et al., “Vojta therapy improves postural control in very early stroke rehabilitation: a randomised controlled pilot trial,” Neurolog. Res. Pract. 2(1), 23 (2020).CrossRefGoogle ScholarPubMed
Gudrun, D., Maria, S., Andrea, G., et al., “Task-oriented circuit training for mobility in outpatient stroke rehabilitation in Germany and Austria: a contextual transferability analysis,” Phys. Ther. 100(8), 1307–1322.Google Scholar
Liu, Z. H., Research on the Human Factors Engineering of Motor Function Rehabilitation System for Upper Limb Exoskeleton (DongHua University, 2017).Google Scholar
Eom, R. I. and Lee, Y., “Comfort evaluation by wearing a gait-assistive rehabilitation robot,” J. Korean Soc. Cloth. Text. 44 (6), 1107–1119.Google Scholar
Freeman, C. T., Rogers, E., Hughes, A. M., et al., “Iterative learning control in health care: electrical stimulation and robotic-assisted upper-limb stroke rehabilitation,” IEEE Control Syst. Mag. 32(1), 18–43 (2012).Google Scholar
Blank, A. A., French, J. A., Pehlivan, A. U., et al., “Current trends in robot-assisted upper-limb stroke rehabilitation: promoting patient engagement in therapy,” Curr. Phys. Med. Rehabil. Rep. 2(3), 184–195 (2014).Google ScholarPubMed
Islam, M. R., Rahmani, M. and Rahman, M. H., “A novel exoskeleton with fractional sliding mode control for upper limb rehabilitation,” Robotica 38(11), 2099–2120 (2020).CrossRefGoogle Scholar
Moshaii, A. A., Moghaddam, M. M. and Niestanak, V. D., “Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger,” Ind. Robot, ahead-of-print (2019).Google Scholar
Ji, J., Shuai, G., Tao, S, et al., “Design and analysis of a novel fall prevention device for lower limbs rehabilitation robot,” J. Back Musculoskel. Rehabil. 31(19), 1–8 (2017).Google Scholar
Amertet, S., “Design and analysis of fuzzy based proportional-integral-derivative controller for elbow-forearm rehabilitation robot,” Int. J. Image Graph. Sig. Process. 12(4), 18 (2020).Google Scholar
Rahman, M. H., Rahman, M. J., Cristobal, O. L., et al., “Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements,” Robotica 33(1), 19–39 (2015).CrossRefGoogle Scholar
Wu, M., Hornby, T. G., Landry, J. M., et al., “A cable-driven locomotor training system for restoration of gait in human SCI,” Gait Posture 33(2), 256–260 (2011).CrossRefGoogle ScholarPubMed
Stegall, P., Winfree, K., Zanotto, D., et al., “Rehabilitation exoskeleton design: exploring the effect of the anterior lunge degree of freedom,” IEEE Trans. Robot. 29(4), 825–837 (2013).CrossRefGoogle Scholar
Jianfeng, L., Qiang, C., Chunzhao, Z., et al., “Position solution of a novel four-DOFs self-aligning exoskeleton mechanism for upper limb rehabilitation,” Mech. Mach. Theory 141, 14–39 (2019).Google Scholar
Jianfeng, L., Qiang, C., Mingjie, D., et al., “Compatibility evaluation of a 4-DOF ergonomic exoskeleton for upper limb rehabilitation,” Mech. Mach. Theory 156, 104146 (2020).Google Scholar
Jarrasse, N. and Morel, G., “Connecting a human limb to an exoskeleton,” IEEE Trans. Robot. 28(3), 697–709 (2012).CrossRefGoogle Scholar
Jianfeng, L., Ziqiang, Z., Chunjing, T., et al., “A number synthesis method of the self-adapting upper-limb rehabilitation exoskeletons,” Int. J. Adv. Robot. Syst. 14(3), 1–14 (2017).Google Scholar
Liang, X., Wang, W., Hou, Z., et al., “Adaptive Estimation of Human-Robot Interaction Force for Lower Limb Rehabilitation,” In International Conference on Neural Information Processing (Springer International Publishing, Cham, 2019) pp. 540–547.CrossRefGoogle Scholar
Jianfeng, L., Shicai, L., Leiyu, Z., et al., “Position solution and kinematic interference analysis of a novel parallel hip-assistive mechanism,” Mech. Mach. Theory 120, 265–287 (2018).Google Scholar
Niyetkaliyev, A. S., Hussain, S., Jamwal, P. K., et al., “Modelling of the human shoulder girdle as a 6-4 parallel mechanism with a moving scapulothoracic joint,” Mech. Mach. Theory 118, 219–230 (2017).CrossRefGoogle Scholar
Chunzhao, Z., Mingjie, D. and Jianfeng, L., et al., “A modified kinematic model of shoulder complex based on Vicon motion capturing system: generalized GH joint with floating centre,” Sensors, 20(13), 3713 (2020).Google Scholar
Jianfeng, L., Chunzhao, Z., Mingjie, D., et al., “A kinematic model of the shoulder complex obtained from a wearable detection system,” Appl. Sci. Basel 10(11), 3696 (2020).Google Scholar
Jianfeng, L., “Kinematics and dexterity analysis of the human-machine compatible exoskeleton mechanism for shoulder joint rehabilitation,” J. Mech. Eng. 54(3), 46 (2018).Google Scholar
Hamming, D., Braman, J., Phadke, V., et al., “The accuracy of measuring glenohumeral motion with a surface humeral cuff,” J. Biomech. 45(7), 1161–1168 (2012).Google ScholarPubMed
Klopcr, N., Tomsic, M. and Lenarcic, J., “A kinematic model of the shoulder complex to evaluate the arm-reachable workspace,” J. Biomech. 40(1), 86–91 (2007).CrossRefGoogle Scholar
Newkirk, J. T., Tomšič, M., Crowell, C. R., et al., “Measurement and quantification of gross human shoulder motion,” Appl. Bionics Biomech. 10(4), 159–173 (2013).Google Scholar
Bottlang, M., Madey, S. M., Steyers, C. M., et al., “Assessment of elbow joint kinematics in passive motion by electromagnetic motion tracking,” J. Orthopaed. Res. 18(2), 195–202 (2000).CrossRefGoogle ScholarPubMed
Sheikhzadeh, A., Yoon, J., Pinto, V. J., et al., “Three-dimensional motion of the scapula and shoulder during activities of daily living,” J. Shoulder Elbow Surg. 17(6), 936–942 (2008).CrossRefGoogle ScholarPubMed
Schiele, A. and Helm, F., “Kinematic design to improve ergonomics in human machine interaction,” IEEE Trans. Neural Syst. Rehabilit. Eng. Publ. IEEE Eng. Med. Biol. Soc. 14(4), 456 (2006).CrossRefGoogle ScholarPubMed
Wang, B., Zhang, T., Chen, J., et al., “A modular cable-driven humanoid arm with anti-parallelogram mechanisms and Bowden cables,” Front. Mech. Eng. 18(1), 197–215 (2023).CrossRefGoogle Scholar
Klinkwan, P., Kongmaroeng, C., Muengtaweepongsa, S., et al., Prototype Development of Bilateral Arm Mirror-Like-Robotic Rehabilitation Device for Acute Stroke Patients (IOP Publishing Ltd. for Biomedical Physics & Engineering Express, 2023).CrossRefGoogle ScholarPubMed
Velden, L. L. V. D., Onneweer, B., Haarman, C. J. W., et al., “Development of a single device to quantify motor impairments of the elbow: proof of concept,”  J. Neuroeng. Rehabil. 19(1), 77 (2022).Google ScholarPubMed
Cao, Q., Li, J. and Dong, M., “Comparative analysis of three categories of four-DOFs exoskeleton mechanism based on relative movement offsets,” Ind. Robot. 49(4), 672–687 (2021).CrossRefGoogle Scholar
Zhong, J., Dongkai, H. E., Zhao, C., et al., “A rehabilitation robot driven by pneumatic artificial muscles,” J. Mech. Med. Biol. 20(9), 2040008 (2020).CrossRefGoogle Scholar
Wang, Y. L., “Mechanical characteristics analysis of a bionic muscle cable-driven lower limb rehabilitation robot,” J. Mech. Med. Biol. 20(10), 2040037 (2020).CrossRefGoogle Scholar