A Review: Sensory System, Data Processing, Actuator Type on a Hand Exoskeleton Design

Article Preview

Abstract:

A rehabilitation device for a post-stroke is essential because stroke attacks can cause disable to part or half of the human body. An exoskeleton could be a vital device for rehabilitation for a post-stroke patient. Several studies have proposed the exoskeleton design for rehabilitation purposes to a human limb disorder. This study aims to review the state-of-the-art of hand exoskeleton devices based on myoelectric or any other sensors. This paper is expected to contribute to design a hand exoskeleton device using both myoelectric and force sensors. This was achieved by reviewing several articles related to the development of the exoskeleton, especially in the sensor system, data processing, and actuator system. The results show that the use of Ag electrode disposable Ag (AgCl) is still commonly found to detect the movement of the fingers on the hand because this sensor can reduce the artifact noise. The use of myo-armband is also found in several studies because it has wireless properties so that it is easy to use. In terms of processors, Arduino microcontrollers are more widely used than others. In order to activate the hand exoskeleton, servo motors are more widely used to actuate the finger joints, which is more precise than other actuators. In a further development, integration between exoskeleton systems and information systems will be an expected challenge. Furthermore, hopefully, the development of this exoskeleton can be applied as a rehabilitation device for patients with malfunction or hand paralysis.

You might also be interested in these eBooks

Info:

Pages:

39-49

Citation:

Online since:

April 2021

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2021 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Y. K. Lee, Design of exoskeleton robotic hand/arm system for upper limbs rehabilitation considering mobility and portability,, in 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014, 2014, vol. 11, no. Urai, p.540–544.

DOI: 10.1109/urai.2014.7057385

Google Scholar

[2] Balitbangkes, Health Basic Research, Ministry of Health Republic of Indonesia, p.1–100,, Jakarta, Indonesia, (2018).

Google Scholar

[3] N. Uddin, K. Sundaraj, B. Ahmad, and M. Rahman, A framework for the development of measurement and quality assurance in software-based medical rehabilitation systems,, Int. Symp. Robot. Intell. Sensors, vol. 41, no. Iris, p.53–60, (2012).

DOI: 10.1016/j.proeng.2012.07.142

Google Scholar

[4] T. Triwiyanto, I. P. A. Pawana, B. G. Irianto, T. B. Indrato, and I. D. G. H. Wisana, Embedded system for upper-limb exoskeleton based on electromyography control,, Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 6, p.2992–3002, (2019).

DOI: 10.12928/telkomnika.v17i6.11670

Google Scholar

[5] Triwiyanto, O. Wahyunggoro, H. A. Nugroho, and Herianto, String actuated upper limb exoskeleton based on surface electromyography control,, Proc. - 2016 6th Int. Annu. Eng. Semin. Ina. 2016, p.176–181, (2017).

DOI: 10.1109/inaes.2016.7821929

Google Scholar

[6] A. Rahmatillah, O. N. Rahma, M. Amin, S. I. Wicaksana, K. Ain, and R. Rulaningtyas, Post-Stroke Rehabilitation Exosceleton Movement Control using EMG Signal,, Int. J. Adv. Sci. Eng. Infrormation Technol., vol. 8, no. 2, p.616–621, (2018).

DOI: 10.18517/ijaseit.8.2.4960

Google Scholar

[7] C. I. De Luca, The Use of Surface Electromyography in Biomechanics,, J. Appl. Biomech., vol. 13, no. 02, p.135–163, (1997).

Google Scholar

[8] R. Ambar and Y. Yusof, Design of Accelerometer based Wrist Rehabilitation Device,, in 2017 6th ICT International Student Project Conference (ICT-ISPC), 2017, p.2–5.

DOI: 10.1109/ict-ispc.2017.8075342

Google Scholar

[9] S. W. Pu, J. Y. Chang, Y. C. Pei, C. C. Kuo, and M. J. Wang, Anthropometry-based structural design of a hand exoskeleton for rehabilitation,, in M2VIP 2016 - Proceedings of 23rd International Conference on Mechatronics and Machine Vision in Practice, (2017).

DOI: 10.1109/m2vip.2016.7827282

Google Scholar

[10] R. Ismail, M. Ariyanto, K. A. Pambudi, J. W. Syafei, and G. P. Ananto, Extra robotic thumb and exoskeleton robotic fingers for patient with hand function disability,, in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, vol. 2017–Decem, no. September, p.19–21.

DOI: 10.1109/eecsi.2017.8239166

Google Scholar

[11] M. DiCicco, L. Lucas, Y. Matsuoka, M. D. I, L. Lucas, and Y. Matsuokd, Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand," in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ,04. 2004, 2004, vol. 2, no. April, p.1622–1627 Vol.2.

DOI: 10.1109/robot.2004.1308056

Google Scholar

[12] C. Ockenfeld, R. K. Y. Tong, E. A. Susanto, S. K. Ho, and X. L. Hu, Fine finger motor skill training with exoskeleton robotic hand in chronic stroke: Stroke rehabilitation,, in IEEE International Conference on Rehabilitation Robotics, 2013, p.5–8.

DOI: 10.1109/icorr.2013.6650392

Google Scholar

[13] T. Triwiyanto, O. Wahyunggoro, H. A. Nugroho, and H. Herianto, Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature,, Comput. Electr. Eng., vol. 71, no. July, p.284–293, Oct. (2018).

DOI: 10.1016/j.compeleceng.2018.07.026

Google Scholar

[14] M. A. Muqeet, Real-time Monitoring of Electromyography ( EMG ) using IoT and ThingSpeak,, Sci. Technol. Dev., vol. VIII, no. Ix, p.9–13, (2019).

Google Scholar

[15] S. Martinez Conde and E. Perez Lugue, Exoskeleton For Hand Rehabilitation,, University of Skovde, (2018).

Google Scholar

[16] F. J. Badesa et al., Hand exoskeleton for rehabilitation therapies with integrated optical force sensor,, Adv. Mech. Eng., vol. 10, no. 2, p.1687814017753881, (2018).

Google Scholar

[17] C. J. Gearhart, B. Varone, M. H. Stella, B. F. Busha, and S. Member, An Effective 3-Fingered Augmenting Exoskeleton for the Human Hand,, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, p.590–593.

DOI: 10.1109/embc.2016.7590771

Google Scholar

[18] N. S. K. Ho et al., An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation,, in 2011 IEEE international conference on rehabilitation robotics, 2011, p.1–5.

DOI: 10.1109/icorr.2011.5975340

Google Scholar

[19] Z. Lu, K. Tong, H. Shin, S. Li, and P. Zhou, Advanced myoelectric control for robotic hand-assisted training: outcome from a stroke patient,, Front. Neurol., vol. 8, no. March, p.107, (2017).

DOI: 10.3389/fneur.2017.00107

Google Scholar

[20] M. K. Burns, S. Member, D. Pei, S. Member, and R. Vinjamuri, Myoelectric Control of a Soft Hand Exoskeleton Using Neural Networks and Kinematic,, IEEE Trans. Biomed. Circuits Syst., vol. PP, no. c, p.1, (2019).

DOI: 10.1109/tbcas.2019.2950145

Google Scholar

[21] P. Heo, S. Member, and J. Kim, Power-Assistive Finger Exoskeleton With a Palmar Opening at the Fingerpad,, IEEE Trans. Biomed. Eng., vol. 61, no. 11, p.2688–2697, (2014).

DOI: 10.1109/tbme.2014.2325948

Google Scholar

[22] D. Leonardis et al., An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation,, IEEE Trans. Haptics, vol. 8, no. 2, p.140–151, (2015).

DOI: 10.1109/toh.2015.2417570

Google Scholar

[23] K. Y. Tong et al., An EMG-driven Exoskeleton Hand Robotic Training Device on Chronic Stroke Subjects Task Training System for Stroke Rehabilitation,, (2011).

DOI: 10.1109/icorr.2011.5975340

Google Scholar

[24] C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala, and S. C. Cramer, Robot-based hand motor therapy after stroke,, Brain, vol. 131, no. 2, p.425–437, (2008).

DOI: 10.1093/brain/awm311

Google Scholar

[25] C. D. Takahashi, V. H. Le, S. C. Cramer, L. Der-Yeghiaian, V. H. Le, and S. C. Cramer, A Robotic Device for Hand Motor Therapy After Stroke,, in 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., 2005, p.17–20.

DOI: 10.1109/icorr.2005.1501041

Google Scholar

[26] M. Witkowski, M. Cortese, M. Cempini, J. Mellinger, N. Vitiello, and S. R. Soekadar, Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG),, J. Neuroeng. Rehabil., vol. 11, no. 1, p.1–6, (2014).

DOI: 10.1186/1743-0003-11-165

Google Scholar

[27] K. Y. Tong et al., An intention driven hand functions task training robotic system," 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC,10, p.3406–3409, (2010).

Google Scholar

[28] K. Tadano, M. Akai, K. Kadota, and K. Kawashima, Development of Grip Amplified Glove using Bi-articular Mechanism,, in 2010 IEEE International Conference on Robotics and Automation, 2010, p.1–6.

DOI: 10.1109/robot.2010.5509393

Google Scholar

[29] I. Jo, J. Lee, Y. Park, and J. Bae, Design of a wearable hand exoskeleton for exercising flexion/extension of the fingers,, in IEEE International Conference on Rehabilitation Robotics, 2017, p.1615–1620.

DOI: 10.1109/icorr.2017.8009479

Google Scholar

[30] Y. Hasegawa, Y. Mikami, K. Watanabe, and Y. Sankai, Five-fingered assistive hand with mechanical compliance of human finger,, in Proceedings - IEEE International Conference on Robotics and Automation, 2008, p.718–724.

DOI: 10.1109/robot.2008.4543290

Google Scholar

[31] A. Wege and G. Hommel, Development and control of a hand exoskeleton for rehabilitation of hand injuries,, in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2005, no. 1, p.3461–3466.

DOI: 10.1109/iros.2005.1545506

Google Scholar

[32] Diez, J. A., A. Blanco, J. M. Catalán, F. J. Badesa, L. D. Lledó, and N. Garcia-Aracil, Hand exoskeleton for rehabilitation therapies with integrated optical force sensor,, Adv. Mech. Eng., vol. 10, no. 2, p.1–11, (2018).

DOI: 10.1177/1687814017753881

Google Scholar