1. GBD 2016 Stroke Collaborators. Global, regional, and national burden of stroke, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2019; 18: 439–458.
Google Scholar2. Lawrence, ES, Coshall, C, Dundas, R, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke 2001; 32: 1279–1284.
Google Scholar | Crossref | Medline | ISI3. GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2019; 18: 56–87.
Google Scholar4. Kang, Y, Ding, H, Zhou, H, et al. Epidemiology of worldwide spinal cord injury: a literature review. J Neurorestoratol 2018; 6: 1–9.
Google Scholar | Crossref5. Peters, HT, Page, SJ, Persch, A. Giving them a hand: wearing a myoelectric elbow-wrist-hand orthosis reduces upper extremity impairment in chronic stroke. Arch Phys Med Rehabil 2017; 98: 1821–1827.
Google Scholar | Crossref | Medline6. Kang, BB, Choi, H, Lee, H, et al. Exo-glove poly II: a polymer-based soft wearable robot for the hand with a tendon-driven actuation system. Soft Robot 2019; 6: 214–227.
Google Scholar | Crossref | Medline7. In, BH, Kang, BB, Sin, M, et al. Exo-glove a wearable robot for the hand with a soft tendon routing system. IEEE Robot Autom Mag 2015; 22: 97–105.
Google Scholar | Crossref | ISI8. Heo, P, Gu, GM, Lee, S, et al. Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int J Precis Eng Manuf 2012; 13: 807–824.
Google Scholar | Crossref | ISI9. Arata, J, Ohmoto, K, Gassert, R, et al. A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism. In: Proceedings of the IEEE international conference on robotics and automation, Karlsruhe, Germany, , pp. 3902–3907. NewYork: IEEE.
Google Scholar10. Heo, P, Kim, J. Power-assistive finger exoskeleton with a palmar opening at the fingerpad. IEEE Trans Biomed Eng 2014; 61: 2688–2697.
Google Scholar | Crossref | Medline11. Yap, HK, Lim, JH, Nasrallah, F, et al. Characterisation and evaluation of soft elastomeric actuators for hand assistive and rehabilitation applications. J Med Eng Technol 2016; 40: 199–209.
Google Scholar | Crossref | Medline12. Conti, R, Meli, E, Ridolfi, A, et al. Kinematic synthesis and testing of a new portable hand exoskeleton. Meccanica 2017; 52: 2873–2897.
Google Scholar | Crossref13. Anon . What is a MyoPro orthosis, http://myomo.com/what-is-a-myopro-orthosis (2018; accessed 29 October 2019).
Google Scholar14. Clarkson, HM. Musculoskeletal assessment: joint range of motion manual muscle strength. 2nd ed. New York: LWW, 2000, pp. 239–255.
Google Scholar15. Parker, P, Englehart, K, Hudgins, B. Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol 2006; 16: 541–548.
Google Scholar | Crossref | Medline | ISI16. Hume, MC, Gellman, H, McKellop, H, et al. Functional range of motion of the joints of the hand. J Hand Surg 1990; 15A: 240–243.
Google Scholar | Crossref | ISI17. Smaby, N, Johanson, ME, Baker, B, et al. Identification of key pinch forces required to complete functional tasks. J Rehabil Res Dev 2004; 41: 215–224.
Google Scholar | Crossref | Medline18. Nycz, CJ, Meier, TB, Carvalho, P, et al. Design criteria for hand exoskeletons: measurement of forces needed to assist finger extension in traumatic brain injury patients. IEEE Rob Autom Lett 2018; 3: 3285–3292.
Google Scholar | Crossref19. Scheme, E, Englehart, K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 2011; 48: 643–659.
Google Scholar | Crossref | Medline20. Ryser, F, Butzer, T, Held, JP, et al. Fully embedded myoelectric control for a wearable robotic hand orthosis. IEEE Int Conf Rehabil Robot 2017; 2017: 615–621.
Google Scholar | Medline