Influence of Friction Forces and Gravity on the Quality of Movement in the MWR Rehabilitation Supporting Device
Source abstract
Abstract
Recovering from upper limb injuries is very important because having limited functionality affects everyday tasks. Rehabilitation supporting systems significantly impact the results; however, it is important to ensure that they are safe and accurate. This study addressed friction forces and gravity on the movement quality of a mechatronic rehabilitation device (MWR) for the upper limb. At first, an experimental analysis was conducted to quantify the active torque required to maintain specific angular positions. The results revealed the significant impact of the mass of mechanical components and friction between moving parts on the device’s movement quality. To address these issues, friction and gravity compensation functions were derived empirically and integrated into the control system’s algorithm. Implementing these compensation functions resulted in a substantial improvement in movement quality, as demonstrated by a reduction of over 50% in the integral of the absolute position error. These results underscore the importance of physical constraints in the control design of rehabilitation devices and provide a foundation for developing adaptive rehabilitation technologies that enhance therapeutic outcomes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Muscle activation and electromyography studies · Prosthetics and Rehabilitation Robotics · Stroke Rehabilitation and Recovery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Wroclaw University of Science and Technology, 30-370 Wroclaw, Poland
References (23)
Show source-provided references
1. Understanding the pattern of functional recovery after stroke: Facts and theories
2. The Present and Future of Robotic Technology in Rehabilitation10.1007/s40141-016-0139-0 ↗
3. Machines to support motor rehabilitation after stroke: 10 years experience in Berlin10.1682/JRRD.2005.02.0052 ↗
4. Overview of clinical trials with MIT-MANUS: A robot-aided neuro-rehabilitation facility10.3233/THC-1999-7606 ↗
5. Hesse, S., and Werner, C. (2009, January 7–12). Machines to support motor rehabilitation after stroke. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Munich, Germany.10.1007/978-3-642-03889-1_41 ↗
6. Rehabilitation robotics: Pilot trial of a spatial extension for MIT-Manus10.1186/1743-0003-1-5 ↗
7. Resistive versus active assisted robotic training for the upper limb after a stroke: A randomized controlled study10.1016/j.rehab.2023.101789 ↗
8. Three Ways to Improve Arm Function in the Chronic Phase After Stroke by Robotic Priming Combined With Mirror Therapy, Arm Training, and Movement-Oriented Therapy10.1016/j.apmr.2023.02.015 ↗
9. Sanchez, R., Wolbrecht, E., Smith, R., Liu, J., Rao, S., Cramer, S., Rahman, T., and Bobrow, J.E. (July, January 28). A pneumatic robot for re-training arm movement after stroke: Rationale and mechanical design. Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, IL, USA.
10. Nef, T., Mihelj, M., Kiefer, G., Perndl, C., Muller, R., and Riener, R. (2007, January 13–15). ARMin—Exoskeleton for Arm Therapy in Stroke Patients. Proceedings of the IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands.10.1109/ICORR.2007.4428408 ↗
11. Outcome Measures with COPM of Children using a Wilmington Robotic Exoskeleton10.15453/2168-6408.1262 ↗
12. Evaluation of fatigue and fatigability in people with Duchenne muscular dystrophy using a dynamic arm support—A pilot study10.1080/17483107.2024.2388284 ↗
13. Oblak, J., Perry, J.C., Jung, J.H., Cikajlo, I., Keller, T., and Matjačić, Z. (September, January 31). A variable structure pantograph mechanism for comprehensive upper extremity haptic movement training. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.
14. Kortier, H.G., Schepers, H.M., and Veltink, P.H. (2016). Identification of Object Dynamics Using Hand Worn Motion and Force Sensors. Sensors, 16.10.3390/s16122005 ↗
15. Lin, B.S., Lee, I., Yang, S.Y., Lo, Y.C., Lee, J., and Chen, J.L. (2018). Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation. Sensors, 18.10.3390/s18051545 ↗
16. Salchow-Hömmen, C., Callies, L., Laidig, D., Valtin, M., Schauer, T., and Seel, T. (2019). A Tangible Solution for Hand Motion Tracking. Sensors, 19.10.3390/s19010208 ↗
17. Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention10.1109/TNSRE.2022.3147260 ↗
18. Friction modeling and compensation for haptic master manipulator based on deep Gaussian process10.1016/j.mechmachtheory.2021.104480 ↗
19. Model-Based Upper-Limb Gravity Compensation Strategies for Active Dynamic Arm Supports
20. External force estimation for robot manipulator based on a LuGre-linear-hybrid friction model and an improved square root cubature Kalman filter10.1108/IR-03-2022-0057 ↗
21. Noccaro, A., Buscaglione, S., Pinardi, M., Di Pino, G., and Formica, D. (2023, January 1–5). Evaluation of a 7-DoFs Robotic Manipulator as Haptic Interface During Planar Reaching Tasks. Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.10.1109/IROS55552.2023.10342470 ↗
22. Lewandowski, B., Wudarczyk, S., Sperzyński, P., and Bałchanowski, J. (2021). Prediction of Motion Intentions as a Novel Method of Upper Limb Rehabilitation Support. Sensors, 21.10.3390/s21020410 ↗
23. A conceptual project of a device for human wrist functional rehabilitation10.1515/ijame-2016-0055 ↗