A Novel Robot-Aided Upper Limb Rehabilitation Training System Based on Multimodal Feedback.

Lizheng Pan, Lu Zhao, Aiguo Song, Zeming Yin, Shigang She
Author Information
  1. Lizheng Pan: School of Mechanical Engineering, Changzhou University, Changzhou, China.
  2. Lu Zhao: School of Mechanical Engineering, Changzhou University, Changzhou, China.
  3. Aiguo Song: Remote Measurement and Control Key Lab of Jiangsu Province, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
  4. Zeming Yin: School of Mechanical Engineering, Changzhou University, Changzhou, China.
  5. Shigang She: School of Mechanical Engineering, Changzhou University, Changzhou, China.

Abstract

During robot-aided rehabilitation exercises, monotonous, and repetitive actions can, to the subject, feel tedious and tiring, so improving the subject's motivation and active participation in the training is very important. A novel robot-aided upper limb rehabilitation training system, based on multimodal feedback, is proposed in this investigation. To increase the subject's interest and participation, a friendly graphical user interface and diversiform game-based rehabilitation training tasks incorporating multimodal feedback are designed, to provide the subject with colorful and engaging motor training. During this training, appropriate visual, auditory, and tactile feedback is employed to improve the subject's motivation via multi-sensory incentives relevant to the training performance. This approach is similar to methods applied by physiotherapists to keep the subject focused on motor training tasks. The experimental results verify the effectiveness of the designed multimodal feedback strategy in promoting the subject's participation and motivation.

Keywords

References

  1. J Neuroeng Rehabil. 2004 Dec 20;1(1):12 [PMID: 15679949]
  2. J Stroke Cerebrovasc Dis. 2006 Jul-Aug;15(4):151-7 [PMID: 17904068]
  3. Biomed Res Int. 2017;2017:4185939 [PMID: 28194413]
  4. J Neurol Phys Ther. 2012 Jun;36(2):79-86 [PMID: 22592063]
  5. J Neuroeng Rehabil. 2014 Mar 06;11:32 [PMID: 24597650]
  6. Acta Psychol (Amst). 2010 Feb;133(2):180-90 [PMID: 20021998]
  7. Front Neurosci. 2018 Jul 05;12:453 [PMID: 30026685]
  8. Adv Neurol. 2003;92:429-33 [PMID: 12760210]
  9. Neurology. 2000 May 23;54(10):1938-44 [PMID: 10822433]
  10. Lancet Neurol. 2016 Sep;15(10):1019-27 [PMID: 27365261]
  11. Arch Phys Med Rehabil. 2004 Sep;85(9):1417-23 [PMID: 15375810]
  12. Neurorehabil Neural Repair. 2008 Mar-Apr;22(2):111-21 [PMID: 17876068]
  13. Int J Rehabil Res. 2014 Dec;37(4):334-42 [PMID: 25221845]
  14. Conf Proc IEEE Eng Med Biol Soc. 2004;2004:4825-8 [PMID: 17271391]
  15. J Neuroeng Rehabil. 2017 Mar 23;14(1):23 [PMID: 28330504]
  16. J Med Syst. 2018 Oct 30;42(12):246 [PMID: 30374695]
  17. J Biomed Inform. 2019 Jan;89:81-100 [PMID: 30521854]
  18. Neurorehabil Neural Repair. 2007 Mar-Apr;21(2):180-9 [PMID: 17312093]
  19. Biomed Res Int. 2019 Feb 20;2019:2742595 [PMID: 30915351]
  20. Gait Posture. 2019 Jun;71:157-162 [PMID: 31071538]

Word Cloud

Created with Highcharts 10.0.0trainingfeedbackrehabilitationsubject'smultimodalsubjectmotivationparticipationrobot-aidedupperlimbtasksdesignedmotorexercisesmonotonousrepetitiveactionscanfeeltedioustiringimprovingactiveimportantnovelsystembasedproposedinvestigationincreaseinterestfriendlygraphicaluserinterfacediversiformgame-basedincorporatingprovidecolorfulengagingappropriatevisualauditorytactileemployedimproveviamulti-sensoryincentivesrelevantperformanceapproachsimilarmethodsappliedphysiotherapistskeepfocusedexperimentalresultsverifyeffectivenessstrategypromotingNovelRobot-AidedUpperLimbRehabilitationTrainingSystemBasedMultimodalFeedbackmotionrobotstroke

Similar Articles

Cited By (3)