Review of comprehensive intervention by hand rehabilitation robot after stroke_Journal of Biomedical Engineering

Authors：

WU Hongjian ^1,2 , LI Lina ^1,2 , LI Long ^1,2 , LIU Tian ^1,2 ,  WANG Jue ^1,2

1. The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R.China;
2. National Engineering Research Center of Health Care and Medical Devices, Xi’an Jiaotong University Branch, Xi’an 710049, P.R.China;

Corresponding author：

Keywords：

hand function; rehabilitation robot; comprehensive intervention

DOI：

10.7507/1001-5515.201711024

Video：

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Using intelligent rehabilitation robot to intervene hand function after stroke is an important physical treatment. With the development of biomedical engineering and the improvement of clinical demand, the comprehensive intervention of hand-function rehabilitation robot combined with new technologies is gradually emerging. This article summarizes the hand rehabilitation robots based on electromyogram (EMG), the brain-computer interface (BCI) hand rehabilitation robots, the somatosensory hand rehabilitation robots and the hand rehabilitation robots with functional electrostimulation. The advantages and disadvantages of various intervention methods are discussed, and the research trend about comprehensive intervention of hand rehabilitation robot is analyzed.

Citation： WU Hongjian, LI Lina, LI Long, LIU Tian, WANG Jue. Review of comprehensive intervention by hand rehabilitation robot after stroke. Journal of Biomedical Engineering, 2019, 36(1): 151-156. doi: 10.7507/1001-5515.201711024 Copy

1.	Mozaffarian D, Benjamin E J, Go A S, et al. Heart disease and stroke statistics–2016 update: a report from the American Heart Association. Circulation, 2016, 133(4): 447-454.
2.	Thrane G, Askim T, Stock R, et al. Efficacy of constraint-induced movement therapy in early stroke rehabilitation: a randomized controlled multisite trial. Neurorehabil Neural Repair, 2015, 29(6): 517-525.
3.	Sethy D, Bajpai P, Kujur E S, et al. Effectiveness of modified constraint induced movement therapy and bilateral arm training on upper extremity function after chronic stroke: a comparative study. Open Journal of Therapy & Rehabilitation, 2016, 4(1): 1-9.
4.	Makowski N S, Knutson J S, Chae J, et al. Control of robotic assistance using poststroke residual voluntary effort. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(2): 221-231.
5.	Adewuyi A A, Hargrove L J, Kuiken T A. An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(4): 485-494.
6.	Peternel L, Noda T, Petric T, et al. Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS One, 2016, 11(2): e0148942.
7.	Oboe R, Tonin A, Yu K, et al. Robotic finger rehabilitation system for stroke patient using surface EMG armband// 42nd Annual Conference of the IEEE-Industrial-Electronics-Society. Florence, Italy: IEEE, 2016: 785-790.
8.	Rosli N A I M, Rahman M A A, Mazlan S A, et al. Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application// IEEE Student Conference on Research and Development. Batu Ferringhi, Malaysia: IEEE, 2015: 1-5.
9.	Thielbar K O, Triandafilou K M, Fischer H C, et al. Benefits of using a voice and EMG-Driven actuated glove to support occupational therapy for stroke survivors. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(3): 297-305.
10.	Moital A R, Dogramadzi S, Ferreira H A. Development of an EMG controlled hand exoskeleton for post-stroke rehabilitation// Proceedings of the 3rd 2015 Workshop on Icts for Improving Patients Rehabilitation Research Techniques. New York, NY: ACM, 2015: 66-72.
11.	Leonardis D, Barsotti M, Loconsole C, et al. An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation. IEEE Trans Haptics, 2015, 8(2): 140-151.
12.	Trujillo P, Mastropietro A, Scano A, et al. Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1058-1067.
13.	Kai K A, Guan C, Phua K S, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng, 2014, 7(30): 30.
14.	Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol, 2013, 74(1): 100-108.
15.	Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
16.	Frolov A A, Mokienko O, Lyukmanov R A, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Front Neurosci, 2017, 11: 400.
17.	Hung Y H, Lai H Y, Shih C H. Study of multi-sensory stimulation for the design of hand rehabilitation equipment for stroke patients. Journal of Industrial & Production Engineering, 2015, 32(7): 425-431.
18.	Tsoupikova D, Stoykov N S, Corrigan M, et al. Virtual immersion for post-stroke hand rehabilitation therapy. Ann Biomed Eng, 2015, 43(2): 467-477.
19.	Adamovich S V, Merians A S, Boian R, et al. A virtual reality-based exercise system for hand rehabilitation post-stroke. Presence-Teleoperators and Virtual Environments, 2005, 14(2): 161-174.
20.	Morrow K, Docan C, Burdea G, et al. Low-cost virtual rehabilitation of the hand for patients post-stroke// 5th International Workshop on Virtual Rehabilitation. New York, NY: IEEE, 2006: 6-10.
21.	Chiri A, Vitiello N, Giovacchini F, et al. Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation. IEEE/ASME Transactions on Mechatronics, 2012, 17(5): 884-894.
22.	Pust M, Ivanova E, Schmidt H, et al. Design of a pressure sensitive matrix for analyzing direct haptic patient-therapist interaction in motor rehabilitation after stroke. Current Directions in Biomedical Engineering, 2017, 3.
23.	Rong W, Tong K Y, Hu X L, et al. Effects of electromyography-driven robot-aided hand training with neuromuscular electrical stimulation on hand control performance after chronic stroke. Disability & Rehabilitation Assistive Technology, 2015, 10(2): 149-159.
24.	Knutson J S, Gunzler D D, Wilson R D. Contralaterally controlled functional electrical stimulation improves hand dexterity in chronic hemiparesis: a randomized trial. Stroke, 2016, 47(10): 2596-2602.
25.	Mccrimmon C M, King C E, Wang P T, et al. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil, 2015, 12(1): 57.
26.	Ruppel M, Seel T, Dogramadzi S. Development of a novel functional electrical stimulation system for hand rehabilitation using feedback control// 6th IEEE International Conference on Biomedical Robotics and Biomechatronics. Singapore: IEEE, 2016: 1135-1139.
27.	Meng F, Tong K Y, Chan S T, et al. BCI-FES training system design and implementation for rehabilitation of stroke patients// International Joint Conference on Neural Networks. Hong Kong: IEEE, 2008: 4103-4106.
28.	Zhuang C, Marquez J C, Qu H E, et al. A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation// International IEEE/EMBS Conference on Neural Engineering. Montpellier, France: IEEE, 2015: 816-819.
29.	Lepley L K, Wojtys E M, Palmieri-Smith R M. Combination of eccentric exercise and neuromuscular electrical stimulation to improve quadriceps function post-ACL reconstruction. Knee, 2015, 22(3): 270-277.
30.	Huang Xianwei, Naghdy F, Naghdy G, et al. The combined effects of adaptive control and virtual reality on robot-assisted fine hand motion rehabilitation in chronic stroke patients: a case study. Journal of Stroke & Cerebrovascular Diseases, 2018, 27(1): 221-228.
31.	Kumpulainen S, Avela J, Gruber M, et al. Differential modulation of motor cortex plasticity in skill- and endurance-trained athletes. Eur J Appl Physiol, 2015, 115(5): 1107-1115.

1. Mozaffarian D, Benjamin E J, Go A S, et al. Heart disease and stroke statistics–2016 update: a report from the American Heart Association. Circulation, 2016, 133(4): 447-454.
2. Thrane G, Askim T, Stock R, et al. Efficacy of constraint-induced movement therapy in early stroke rehabilitation: a randomized controlled multisite trial. Neurorehabil Neural Repair, 2015, 29(6): 517-525.
3. Sethy D, Bajpai P, Kujur E S, et al. Effectiveness of modified constraint induced movement therapy and bilateral arm training on upper extremity function after chronic stroke: a comparative study. Open Journal of Therapy & Rehabilitation, 2016, 4(1): 1-9.
4. Makowski N S, Knutson J S, Chae J, et al. Control of robotic assistance using poststroke residual voluntary effort. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(2): 221-231.
5. Adewuyi A A, Hargrove L J, Kuiken T A. An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(4): 485-494.
6. Peternel L, Noda T, Petric T, et al. Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS One, 2016, 11(2): e0148942.
7. Oboe R, Tonin A, Yu K, et al. Robotic finger rehabilitation system for stroke patient using surface EMG armband// 42nd Annual Conference of the IEEE-Industrial-Electronics-Society. Florence, Italy: IEEE, 2016: 785-790.
8. Rosli N A I M, Rahman M A A, Mazlan S A, et al. Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application// IEEE Student Conference on Research and Development. Batu Ferringhi, Malaysia: IEEE, 2015: 1-5.
9. Thielbar K O, Triandafilou K M, Fischer H C, et al. Benefits of using a voice and EMG-Driven actuated glove to support occupational therapy for stroke survivors. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(3): 297-305.
10. Moital A R, Dogramadzi S, Ferreira H A. Development of an EMG controlled hand exoskeleton for post-stroke rehabilitation// Proceedings of the 3rd 2015 Workshop on Icts for Improving Patients Rehabilitation Research Techniques. New York, NY: ACM, 2015: 66-72.
11. Leonardis D, Barsotti M, Loconsole C, et al. An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation. IEEE Trans Haptics, 2015, 8(2): 140-151.
12. Trujillo P, Mastropietro A, Scano A, et al. Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1058-1067.
13. Kai K A, Guan C, Phua K S, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng, 2014, 7(30): 30.
14. Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol, 2013, 74(1): 100-108.
15. Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
16. Frolov A A, Mokienko O, Lyukmanov R A, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Front Neurosci, 2017, 11: 400.
17. Hung Y H, Lai H Y, Shih C H. Study of multi-sensory stimulation for the design of hand rehabilitation equipment for stroke patients. Journal of Industrial & Production Engineering, 2015, 32(7): 425-431.
18. Tsoupikova D, Stoykov N S, Corrigan M, et al. Virtual immersion for post-stroke hand rehabilitation therapy. Ann Biomed Eng, 2015, 43(2): 467-477.
19. Adamovich S V, Merians A S, Boian R, et al. A virtual reality-based exercise system for hand rehabilitation post-stroke. Presence-Teleoperators and Virtual Environments, 2005, 14(2): 161-174.
20. Morrow K, Docan C, Burdea G, et al. Low-cost virtual rehabilitation of the hand for patients post-stroke// 5th International Workshop on Virtual Rehabilitation. New York, NY: IEEE, 2006: 6-10.
21. Chiri A, Vitiello N, Giovacchini F, et al. Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation. IEEE/ASME Transactions on Mechatronics, 2012, 17(5): 884-894.
22. Pust M, Ivanova E, Schmidt H, et al. Design of a pressure sensitive matrix for analyzing direct haptic patient-therapist interaction in motor rehabilitation after stroke. Current Directions in Biomedical Engineering, 2017, 3.
23. Rong W, Tong K Y, Hu X L, et al. Effects of electromyography-driven robot-aided hand training with neuromuscular electrical stimulation on hand control performance after chronic stroke. Disability & Rehabilitation Assistive Technology, 2015, 10(2): 149-159.
24. Knutson J S, Gunzler D D, Wilson R D. Contralaterally controlled functional electrical stimulation improves hand dexterity in chronic hemiparesis: a randomized trial. Stroke, 2016, 47(10): 2596-2602.
25. Mccrimmon C M, King C E, Wang P T, et al. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil, 2015, 12(1): 57.
26. Ruppel M, Seel T, Dogramadzi S. Development of a novel functional electrical stimulation system for hand rehabilitation using feedback control// 6th IEEE International Conference on Biomedical Robotics and Biomechatronics. Singapore: IEEE, 2016: 1135-1139.
27. Meng F, Tong K Y, Chan S T, et al. BCI-FES training system design and implementation for rehabilitation of stroke patients// International Joint Conference on Neural Networks. Hong Kong: IEEE, 2008: 4103-4106.
28. Zhuang C, Marquez J C, Qu H E, et al. A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation// International IEEE/EMBS Conference on Neural Engineering. Montpellier, France: IEEE, 2015: 816-819.
29. Lepley L K, Wojtys E M, Palmieri-Smith R M. Combination of eccentric exercise and neuromuscular electrical stimulation to improve quadriceps function post-ACL reconstruction. Knee, 2015, 22(3): 270-277.
30. Huang Xianwei, Naghdy F, Naghdy G, et al. The combined effects of adaptive control and virtual reality on robot-assisted fine hand motion rehabilitation in chronic stroke patients: a case study. Journal of Stroke & Cerebrovascular Diseases, 2018, 27(1): 221-228.
31. Kumpulainen S, Avela J, Gruber M, et al. Differential modulation of motor cortex plasticity in skill- and endurance-trained athletes. Eur J Appl Physiol, 2015, 115(5): 1107-1115.

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Review of comprehensive intervention by hand rehabilitation robot after stroke

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