A Test Bench For Evaluating Exoskeletons For Upper Limb Rehabilitation

Rehabilitation exoskeletons have proven to be useful in assisting clinicians in therapy and assisting users in daily tasks. While the potential of wearable robotics technology is undeniable, quantifying its value is difficult. As a result, performance in wearable robotics is becoming a pressing concern, and the scientific community requires reliable and repeatable testing methodologies to evaluate and compare the available exoskeletal systems. Various types of exoskeleton robots have already been developed and tested for upper limb rehabilitation. The problem is that evaluations are not standardized, particularly in pediatric rehabilitation. This paper aimed to propose a methodology for the quantitative evaluation of upper limb exoskeletons that, like a test bench, would serve for replicable testing. This was accomplished by determining the range of motion (ROM) and joint torques using both kinematic modeling and experimental measurements (using sensors integrated into Dynamixel actuators, where ROM and joint torques were estimated from actuator feedback, respectively, in position and in load through the IDE Arduino). The proposed test bench can provide an accurate range of motion (ROM) and joint torques during the pronation–supination task. The range of motion obtained with the 3D or physical prototype was approximately 156.26 ± 4.71° during the pronation–supination task, while it was approximately 146.84 ± 14.32° for the multibody model. The results show that the average range of experimental torques (0.28 ± 0.06 N.m) was overestimated by 40% and just 3.4%, respectively, when compared to the average range of simulated torques (0.2 ± 0.05 N.m) and to the highest range of simulated torques (0.29 N.m). For the experimental measurements, test–retest reliability was excellent (α = 0.96-0.98) within sessions and excellent or good (α = 0.93 and α = 0.81-0.86) between sessions. Finally, the suggested approach provides a range of motion close to the normal range of motion necessary during PS tasks. These results are important because they validate the measurements' accuracy and underline the proposed methodology's relevance. This study also confirms fluctuations in torque in human joints during motion and emphasizes the importance of considering these variations for precise quantification of joint torques by using the maximum value of estimated torques (rather than the average value). To conclude, the proposed assessment procedure could become a reference standard for evaluating exoskeletons for the upper limb. This study also addresses a methodological aspect on the accurate assessment of joint torques that can serve in applications such as the sizing of actuators in exoskeletons or the non-invasive evaluation of muscle forces in the human body. In perspective, the concept will be expanded to additional joints, such as the elbow and wrist, to have a more complex assessment tool. Furthermore, future research will address the user's safety by quantifying the kinematic coupling between the user and the device.

[1]  V. Dietz,et al.  Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective , 2018, Journal of NeuroEngineering and Rehabilitation.

[2]  J. Wojtusch,et al.  Estimation of the Body Segment Inertial Parameters for the Rigid Body Biomechanical Models Used in Motion Analysis , 2017 .

[3]  P. Rumrill,et al.  Population aging and disability: Implications for vocational rehabilitation practice , 2017 .

[4]  Federico Vicentini,et al.  COVR – Towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains based on robot safety skills , 2018 .

[5]  Production at the leading edge of technology , 2021, Lecture Notes in Production Engineering.

[6]  Christopher M Frumento,et al.  History and Future of Rehabilitation Robotics , 2010 .

[7]  Jérémie Guiochet,et al.  Applying Existing Standards to a Medical Rehabilitation Robot: Limits and Challenges , 2012, IROS 2012.

[8]  Kevin Desbrosses,et al.  Physiological consequences of using an upper limb exoskeleton during manual handling tasks. , 2018, Applied ergonomics.

[9]  W. Z. Wan Hasan,et al.  A Review on Upper Limb Rehabilitation Robots , 2020, Applied Sciences.

[10]  Jason M Wilken,et al.  Range of Motion Requirements for Upper-Limb Activities of Daily Living. , 2015, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[11]  Jean-Claude Samin,et al.  Assessment of Antagonistic Muscle Forces During Forearm Flexion/Extension , 2011 .

[13]  A. Sofiane,et al.  Impact of the choice of upper limb prosthesis mechanism on kinematics and dynamic quality. , 2021, Medical engineering & physics.

[14]  Giovanni Cannaviello,et al.  Exoskeleton and End‐Effector Robots for Upper and Lower Limbs Rehabilitation: Narrative Review , 2018, PM & R : the journal of injury, function, and rehabilitation.

[15]  R. Calabró,et al.  Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis , 2017, Neurological Sciences.

[16]  David Putrino,et al.  Robotic Rehabilitation and Spinal Cord Injury: a Narrative Review , 2018, Neurotherapeutics.

[17]  E. A. Susanto,et al.  Efficacy of robot-assisted fingers training in chronic stroke survivors: a pilot randomized-controlled trial , 2015, Journal of NeuroEngineering and Rehabilitation.

[18]  Varnita Verma,et al.  Developments and clinical evaluations of robotic exoskeleton technology for human upper-limb rehabilitation , 2020, Adv. Robotics.

[19]  Robert Riener,et al.  Rehabilitation Robotics , 2013, Found. Trends Robotics.

[20]  Michael Hillman,et al.  Rehabilitation robotics from past to present - a historical perspective , 2003 .

[21]  Antonio Frisoli,et al.  Exoskeletons for upper limb rehabilitation , 2018 .

[22]  Noriyuki Tejima,et al.  Rehabilitation robotics: a review , 2001, Adv. Robotics.

[23]  C. Burgar,et al.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.

[24]  Julius Griškevičius,et al.  Effects of robot-assisted training on upper limb functional recovery during the rehabilitation of poststroke patients. , 2018, Technology and health care : official journal of the European Society for Engineering and Medicine.

[25]  Paul Fisette,et al.  ROBOTRAN: a powerful symbolic gnerator of multibody models , 2013 .

[26]  Shaoping Bai,et al.  A Review on Design of Upper Limb Exoskeletons , 2020, Robotics.

[27]  Nicola Vitiello,et al.  Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Alberto Jardón,et al.  Benchmarking Usability of Assistive Robotic Systems : Methodology and Application , 2010 .

[29]  A. Gorgey Robotic exoskeletons: The current pros and cons , 2018, World journal of orthopedics.

[30]  S. H. Mahdioun,et al.  A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke , 2015, Disability and rehabilitation. Assistive technology.

[31]  Sheng Quan Xie,et al.  Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. , 2012, Medical engineering & physics.

[32]  E. Cheung,et al.  Robot-Assisted Training for People With Spinal Cord Injury: A Meta-Analysis. , 2017, Archives of physical medicine and rehabilitation.

[33]  B. Brewer,et al.  Poststroke Upper Extremity Rehabilitation: A Review of Robotic Systems and Clinical Results , 2007, Topics in stroke rehabilitation.

[34]  P. Feys,et al.  Robot-assisted rehabilitation in multiple sclerosis: Overview of approaches, clinical outcomes, and perspectives , 2018 .

[35]  Delphine Périé,et al.  Refinement of the upper limb joint kinematics and dynamics using a subject-specific closed-loop forearm model , 2015 .

[36]  Eduard Fosch Villaronga,et al.  A Taxonomy of Ethical, Legal and Social Implications of Wearable Robots: An Expert Perspective , 2020, Science and Engineering Ethics.

[37]  S. Leonhardt,et al.  A survey on robotic devices for upper limb rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[38]  François Michaud,et al.  Exoskeletons' design and usefulness evidence according to a systematic review of lower limb exoskeletons used for functional mobility by people with spinal cord injury , 2016, Disability and rehabilitation. Assistive technology.

[39]  A. Quanbury,et al.  Normal functional range of motion of upper limb joints during performance of three feeding activities. , 1990, Archives of physical medicine and rehabilitation.

[40]  Hermano Igo Krebs,et al.  Improved motor performance in chronic spinal cord injury following upper-limb robotic training. , 2013, NeuroRehabilitation.

[41]  Robin Pierce,et al.  Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access , 2020, Paladyn J. Behav. Robotics.

[42]  David J. Guggenmos,et al.  Physiological basis of neuromotor recovery , 2018 .

[43]  Andrea Turolla An overall framework for neurorehabilitation robotics: Implications for recovery , 2018 .

[44]  Simona Crea,et al.  Benchmarking Wearable Robots: Challenges and Recommendations From Functional, User Experience, and Methodological Perspectives , 2020, Frontiers in Robotics and AI.

[45]  Pablo Aqueveque,et al.  After Stroke Movement Impairments: A Review of Current Technologies for Rehabilitation , 2017 .

[46]  J. Zimbelman,et al.  Physical Therapy Workforce in the United States: Forecasting Nationwide Shortages , 2010, PM & R : the journal of injury, function, and rehabilitation.

[47]  David J. Reinkensmeyer,et al.  Rehabilitation and Health Care Robotics , 2016, Springer Handbook of Robotics, 2nd Ed..

[48]  Hui Liang,et al.  Upper limb rehabilitation using robotic exoskeleton systems: a systematic review , 2018, International Journal of Intelligent Robotics and Applications.

[49]  J. Kleim,et al.  Neural Plasticity: The Biological Substrate For Neurorehabilitation , 2010, PM & R : the journal of injury, function, and rehabilitation.

[50]  Nuray Yozbatiran,et al.  Robot-assisted Therapy for the Upper Limb after Cervical Spinal Cord Injury. , 2019, Physical medicine and rehabilitation clinics of North America.

[51]  Reza Langari,et al.  Challenges and Opportunities in Exoskeleton-based Rehabilitation , 2017, ArXiv.

[52]  B Samadi,et al.  Custom sizing of lower limb exoskeleton actuators using gait dynamic modelling of children with cerebral palsy , 2016, Computer methods in biomechanics and biomedical engineering.

[53]  W. Rymer,et al.  Current Evidence for Use of Robotic Exoskeletons in Rehabilitation , 2020 .

[54]  Rose-Marie Johansson-Pajala,et al.  Significant challenges when introducing care robots in Swedish elder care , 2020, Disability and rehabilitation. Assistive technology.

[55]  Susan Jaglal,et al.  Robot-assisted upper extremity rehabilitation for cervical spinal cord injuries: a systematic scoping review , 2018, Disability and rehabilitation. Assistive technology.