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The demand for hand rehabilitation services is increasing currently, and there are many choices of hand rehabilitation products available on the market. Here, we report a flexible curvature sensor-based data glove, equipped with a built-in light trigger system, which is expected to provide active signal feedback for better human machine interface experience. By analyzing user needs and leveraging advancements in human-computer interaction, this new design not only facilitates physical recovery but also fosters a sense of ownership and motivation among patients. The incorporation of real-time feedback mechanisms further amplifies the efficacy of the rehabilitation process, propelling patients toward greater independence and self-assurance. Specially from an emotional point of view, the elements, such as functionality, design, color, material, and human-computer interaction, based on user needs, would be analyzed to summarize some core requirements. Then the performance of hand movements is integrated into the rehabilitation process through visual feedback to address the problem of monotony during training. This design can leverage the feedback mechanism between visual cues and hand movements, enhancing the sensory interaction experience between individuals and the product. Finally, it is proved by experiment this proposed design is very potential to help improve patients’ motivation and self-efficacy in training, thereby enhancing the effectiveness of rehabilitation. By combining these elements, we believed that the design is expected to make up some emotional support for better human machine interface experience.
I. V. A. N. Grubišić, S. K. Hana, and G. Simeon, Novel approaches in hand rehabilitation, Period. Biologorum, vol. 117, no. 1, pp. 139–145, 2015.
L. Zhou, Intelligent music rehabilitation hand based on voice recognition, IOP Conf. Ser.: Mater. Sci. Eng., vol. 612, no. 4, p. 042080, 2019.
S. H. Yang, C. L. Koh, C. H. Hsu, P. C. Chen, J. W. Chen, Y. H. Lan, Y. Yang, Y. D. Lin, C. H. Wu, H. K. Liu, et al., An instrumented glove-controlled portable hand-exoskeleton for bilateral hand rehabilitation, Biosensors, vol. 11, no. 12, pp. 495, 2021.
S. Ueki, H. Kawasaki, S. Ito, Y. Nishimoto, M. Abe, T. Aoki, Y. Ishigure, T. Ojika, and T. Mouri, Development of a hand-assist robot with multi-degrees-of-freedom for rehabilitation therapy, IEEE/ASME Trans. Mechatron., vol. 17, no. 1, pp. 136–146, 2012.
F. Pichiorri, G. Morone, M. Petti, J. Toppi, I. Pisotta, M. Molinari, S. Paolucci, M. Inghilleri, L. Astolfi, F. Cincotti, et al., Brain–computer interface boosts motor imagery practice during stroke recovery, Ann. Neurol., vol. 77, no. 5, pp. 851–865, 2015.
M. Pust, E. Ivanova, H. Schmidt, and J. Krüger, Design of a pressure sensitive matrix for analyzing direct haptic patient-therapist interaction in motor rehabilitation after stroke, Curr. Dir. Biomed. Eng., vol. 3, no. 1, pp. 57–61, 2017.
P. Heo, G. M. Gu, S. J. Lee, K. Rhee, and J. Kim, Current hand exoskeleton technologies for rehabilitation and assistive engineering, Int. J. Precis. Eng. Manuf., vol. 13, no. 5, pp. 807–824, 2012.
Z. Yue, X. Zhang, and J. Wang, Hand rehabilitation robotics on poststroke motor recovery, Behav. Neurol., vol. 2017, p. 3908135, 2017.
M. V. Arteaga, J. C. Castiblanco, I. F. Mondragon, J. D. Colorado, and C. Alvarado-Rojas, EMG-driven hand model based on the classification of individual finger movements, Biomed. Signal Process. Contr., vol. 58, pp. 101834, 2020.
Y. Liu, X. Li, A. Zhu, Z. Zheng, and H. Zhu, Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot, Int. J. Adv. Rob. Syst., vol. 18, no. 3, p. 172988142110034, 2021.
T. du Plessis, K. Djouani, and C. Oosthuizen, A review of active hand exoskeletons for rehabilitation and assistance, Robotics, vol. 10, no. 1, pp. 40, 2021.
P. Tran, S. Jeong, K. Herrin, and J. Desai, Review: Hand exoskeleton systems, clinical rehabilitation practices, and future prospects, IEEE Trans. Med. Robot. Bionics, vol. 3, no. 3, pp. 606–622, 2021.
C. Laschi, B. Mazzolai, and M. Cianchetti, Soft robotics: Technologies and systems pushing the boundaries of robot abilities, Sci. Robot., vol. 1, no. 1, p. eaah3690, 2016.
T. Shahid, D. Gouwanda, S. G. Nurzaman, and A. A. Gopalai, Moving toward soft robotics: A decade review of the design of hand exoskeletons, Biomimetics, vol. 3, no. 3, p. 17, 2018.
C. G. Rose and M. K. O’Malley, Hybrid rigid-soft hand exoskeleton to assist functional dexterity, IEEE Robot. Autom. Lett., vol. 4, no. 1, pp. 73–80, 2019.
M. Sarac, M. Solazzi, and A. Frisoli, Design requirements of generic hand exoskeletons and survey of hand exoskeletons for rehabilitation, assistive, or haptic use, IEEE Trans. Haptics, vol. 12, no. 4, pp. 400–413, 2019.
M. H. Abdelhafiz, L. N. S. Andreasen Struijk, S. Dosen, and E. G. Spaich, Biomimetic tendon-based mechanism for finger flexion and extension in a soft hand exoskeleton: Design and experimental assessment, Sensors, vol. 23, no. 4, p. 2272, 2023.
T. Bagneschi, D. Chiaradia, G. Righi, G. Del Popolo, A. Frisoli, and D. Leonardis, A soft hand exoskeleton with a novel tendon layout to improve stable wearing in grasping assistance, IEEE Trans. Haptics, vol. 16, no. 2, pp. 311–321, 2023.
R. C. Silva, B. G. Lourenço, P. H. F. Ulhoa, E. A. F. Dias, F. L. da Cunha, C. P. Tonetto, L. G. Villani, C. B. S. Vimieiro, G. A. Lepski, M. Monjardim, et al., Biomimetic design of a tendon-driven myoelectric soft hand exoskeleton for upper-limb rehabilitation, Biomimetics, vol. 8, no. 3, p. 317, 2023.
T. Bützer, O. Lambercy, J. Arata, and R. Gassert, Fully wearable actuated soft exoskeleton for grasping assistance in everyday activities, Soft Robot., vol. 8, no. 2, pp. 128–143, 2021.
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