Variable Admittance Control of a Hand Exoskeleton for Virtual Reality-Based Rehabilitation Tasks

Alberto Topini1, William Sansom1, Nicola Secciani1, Lorenzo Bartalucci1, Alessandro Ridolfi1, Benedetto Allotta1
1Department of Industrial Engineering, University of Florence, Florence, Italy

Tóm tắt

Robot-based rehabilitation is consolidated as a viable and efficient practice to speed up and improve the recovery of lost functions. Several studies highlight that patients are encouraged to undergo their therapies and feel more involved in the process when collaborating with a user-friendly robotic environment. Object manipulation is a crucial element of hand rehabilitation treatments; however, as a standalone process may result in being repetitive and unstimulating in the long run. In this view, robotic devices, like hand exoskeletons, do arise as an excellent tool to boost both therapy's outcome and patient participation, especially when paired with the advantages offered by interacting with virtual reality (VR). Indeed, virtual environments can simulate real-life manipulation tasks and real-time assign a score to the patient's performance, thus providing challenging exercises while promoting training with a reward-based system. Besides, they can be easily reconfigured to match the patient's needs by manipulating exercise intensity, e.g., Assistance-As-Needed (AAN) and the required tasks. Modern VR can also render interaction forces when paired to wearable devices to give the user some sort of proprioceptive force or tactile feedback. Motivated by these considerations, a Hand Exoskeleton System (HES) has been designed to be interfaced with a variable admittance control to achieve VR-based rehabilitation tasks. The exoskeleton assists the patient's movements according to force feedback and following a reference value calculated inside the VR. Whenever the patient grasps a virtual object, the HES provides the user with a force feedback sensation. In this paper, the virtual environment, developed within the Webots framework and rendering a HES digital-twin mapping and mimicking the actual HES motion, will be described in detail. Furthermore, the admittance control strategy, which continuously varies the control parameters to best render the force sensation and adapt to the user's motion intentions, will be investigated. The proposed approach has been tested on a single subject in the framework of a pilot study.

Từ khóa


Tài liệu tham khảo

Abu-Dakka, 2020, Variable impedance control and learning-a review, arXiv preprint, 10.3389/frobt.2020.590681

Anam, 2012, Active exoskeleton control systems: State of the art, Procedia Eng, 41, 988, 10.1016/j.proeng.2012.07.273

Bartalucci, 2020, “Rehabilitative hand exoskeleton system: a new modular mechanical design for a remote actuated device,”, The International Conference of IFToMM ITALY, 128

Bielsa, 2021, Virtual reality simulation in plastic surgery training. literature review, J. Plastic Reconstr. Aesthet. Surg, 74, 2372, 10.1016/j.bjps.2021.03.066

Colgate, 1988, Robust control of dynamically interacting systems, Int. J. Control, 48, 65, 10.1080/00207178808906161

Conti, 2017, Kinematic synthesis and testing of a new portable hand exoskeleton, Meccanica, 52, 2873, 10.1007/s11012-016-0602-0

de Araújo, 2019, Efficacy of virtual reality rehabilitation after spinal cord injury: a systematic review, Biomed. Res. Int, 2019, 7106951, 10.1155/2019/7106951

du Plessis, 2021, A review of active hand exoskeletons for rehabilitation and assistance, Robotics, 10, 40, 10.3390/robotics10010040

Duchaine, 2007, “General model of human-robot cooperation using a novel velocity based variable impedance control,”, Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC'07), 446, 10.1109/WHC.2007.59

Gomi, 1993, Neural network control for a closed-loop system using feedback-error-learning, Neural Netw, 6, 933, 10.1016/S0893-6080(09)80004-X

Hogan, 1984, Adaptive control of mechanical impedance by coactivation of antagonist muscles, IEEE Trans. Automat. Contr, 29, 681, 10.1109/TAC.1984.1103644

Hogan, 2018, “Impedance and interaction control,”, Robotics and Automation Handbook, 375

Hua, 2021, Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning, Sensors, 21, 1278, 10.3390/s21041278

Huang, 2018, Master-slave control of an intention-actuated exoskeletal robot for locomotion and lower extremity rehabilitation, Int. J. Precision Eng. Manufact, 19, 983, 10.1007/s12541-018-0116-x

Huang, 2019, Learning physical human-robot interaction with coupled cooperative primitives for a lower exoskeleton, IEEE Trans. Autom. Sci. Eng, 16, 1566, 10.1109/TASE.2018.2886376

Ikeura, 1994, “Cooperative motion control of a robot and a human,”, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication, 112, 10.1109/ROMAN.1994.365946

Ikeura, 2002, “Optimal variable impedance control for a robot and its application to lifting an object with a human,”, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication, 500, 10.1109/ROMAN.2002.1045671

Jung, 1998, Neural network impedance force control of robot manipulator, IEEE Trans. Ind. Electr, 45, 451, 10.1109/41.679003

Jung, 2004, Force tracking impedance control of robot manipulators under unknown environment, IEEE Trans. Control Syst. Technol, 12, 474, 10.1109/TCST.2004.824320

Kavanagh, 2017, A systematic review of virtual reality in education, Themes Sci. Technol. Educ, 10, 85

Keemink, 2018, Admittance control for physical human-robot interaction, Int. J. Rob. Res, 37, 1421, 10.1177/0278364918768950

Kim, 2018, “Weighted hybrid admittance-impedance control with human intention based stiffness estimation for human-robot interaction,”, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1

Lecours, 2012, “Variable admittance control of a four-degree-of-freedom intelligent assist device,”, 2012 IEEE International Conference on Robotics and Automation, 3903, 10.1109/ICRA.2012.6224586

Lee, 2008, Force tracking impedance control with variable target stiffness, IFAC Proc, 41, 6751, 10.3182/20080706-5-KR-1001.01144

Lei, 2019, Effects of virtual reality rehabilitation training on gait and balance in patients with parkinson's disease: A systematic review, PLoS ONE, 14, e0224819, 10.1371/journal.pone.0224819

Li, 2017, Adaptive impedance control of human-robot cooperation using reinforcement learning, IEEE Trans. Ind. Electron, 64, 8013, 10.1109/TIE.2017.2694391

Losey, 2018, A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction, Appl. Mech. Rev, 70, 010804, 10.1115/1.4039145

Lu, 1995, Robust impedance control and force regulation: Theory and experiments, Int. J. Rob. Res, 14, 225, 10.1177/027836499501400303

Lum, 2002, Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke, Arch. Phys. Med. Rehabil, 83, 952, 10.1053/apmr.2001.33101

Molteni, 2018, Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: narrative review, PM R, 10, S174, 10.1016/j.pmrj.2018.06.005

Petrenko, 2019, “Exoskeleton for operator's motion capture with master-slave control,”, 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019), 152

Pfandler, 2017, Virtual reality-based simulators for spine surgery: a systematic review, Spine J, 17, 1352, 10.1016/j.spinee.2017.05.016

Qian, 2021, Quantitative assessment of motor function by an end-effector upper limb rehabilitation robot based on admittance control, Appl. Sci, 11, 6854, 10.3390/app11156854

Radianti, 2020, A systematic review of immersive virtual reality applications for higher education: design elements, lessons learned, and research agenda, Comput. Educ, 147, 103778, 10.1016/j.compedu.2019.103778

Rose, 2018, Immersion of virtual reality for rehabilitation-review, Appl. Ergon, 69, 153, 10.1016/j.apergo.2018.01.009

Roveda, 2015, Optimal impedance force-tracking control design with impact formulation for interaction tasks, IEEE Rob. Autom. Lett, 1, 130, 10.1109/LRA.2015.2508061

Sado, 2014, “Adaptive hybrid impedance control for a 3dof upper limb rehabilitation robot using hybrid automata,”, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), 596, 10.1109/IECBES.2014.7047573

Sandison, 2020, “HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation,”, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), 4867

Schumacher, 2019, An introductory review of active compliant control, Rob. Auton. Syst, 119, 185, 10.1016/j.robot.2019.06.009

Seraji, 1997, Force tracking in impedance control, Int. J. Rob. Res, 16, 97, 10.1177/027836499701600107

Shi, 2019, A review on lower limb rehabilitation exoskeleton robots, Chin. J. Mech. Eng, 32, 1, 10.1186/s10033-019-0389-8

Song, 2019, A tutorial survey and comparison of impedance control on robotic manipulation, Robotica, 37, 801, 10.1017/S0263574718001339

Souzanchi-K., 2017, Robust impedance control of uncertain mobile manipulators using time-delay compensation, IEEE Trans. Control Syst. Technol, 26, 1942, 10.1109/TCST.2017.2739109

Staubli, 2009, Effects of intensive arm training with the rehabilitation robot armin ii in chronic stroke patients: four single-cases, J. Neuroeng. Rehabil, 6, 1, 10.1186/1743-0003-6-46

Tsumugiwa, 2002, “Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task,”, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), Vol. 1, 644, 10.1109/ROBOT.2002.1013431

Wang, 2018, A critical review of the use of virtual reality in construction engineering education and training, Int. J. Environ. Res. Public Health, 15, 1204, 10.3390/ijerph15061204

Wolfartsberger, 2019, Analyzing the potential of virtual reality for engineering design review, Autom. Construct, 104, 27, 10.1016/j.autcon.2019.03.018

Yung, 2019, New realities: a systematic literature review on virtual reality and augmented reality in tourism research, Curr. Issues Tourism, 22, 2056, 10.1080/13683500.2017.1417359