Protótipo de tecnologia assistiva para detecção de movimento e força dos dedos para reabilitação pós-acidente vascular cerebral (AVC)
Resumo
O Acidente Vascular Cerebral (AVC) compromete com frequência os movimentos finos das mãos e dos dedos, reduzindo a autonomia dos pacientes. Nesse contexto, tecnologias como luvas inteligentes têm se destacado como apoio à reabilitação motora dos membros superiores. Este trabalho teve como objetivo desenvolver e avaliar preliminarmente uma luva inteligente capaz de detectar e monitorar o movimento e a força individual dos dedos. O protótipo foi construído com sensores flex, sensores de força e microcontrolador ESP-WROOM-32. Os testes, realizados em ambiente controlado, mostraram que o dispositivo foi capaz de identificar variações de movimento e força ao longo de ciclos repetitivos. Conclui-se que a luva apresenta potencial como ferramenta de apoio à reabilitação motora, embora ainda sejam necessários estudos clínicos com mais pacientes.
Referências
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Barthels, D. and Das, H. (2020). Current advances in ischemic stroke research and therapies. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1866(4):165260.
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Dhiman, H. and Kumar, R. (2025). An internet of things-enabled smart glove for brain stroke rehabilitation. Measurement: Digitalization, page 100001.
Dias, P., Silva, R., Amorim, P., Lains, J., Roque, E., Serôdio, I., Pereira, F., and Santos, B. S. (2019). Using virtual reality to increase motivation in poststroke rehabilitation. IEEE computer graphics and applications, 39(1):64–70.
Dong, Y.-S., Ao, S.-J., and Gong, H. (2025). Adaptive robotic polishing based on distributed flexible force sensors. Robotics and Computer-Integrated Manufacturing, 95:103020.
Gong, D., Li, X., Chen, Y., and Jiao, T. (2025). Recent progress in mof-based flexible sensors: A review. Sustainable Materials and Technologies, page e01429.
Gorelick, P. B. (2019). The global burden of stroke: persistent and disabling. The Lancet Neurology, 18(5):417–418.
Guo, N., Wang, X., Duanmu, D., Huang, X., Li, X., Fan, Y., Li, H., Liu, Y., Yeung, E. H. K., To, M. K. T., et al. (2022). Ssvep-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE transactions on neural systems and rehabilitation engineering, 30:1737–1744.
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Henderson, J., Condell, J., Connolly, J., Kelly, D., and Curran, K. (2021). Reliability and validity of clinically accessible smart glove technologies to measure joint range of motion. Sensors, 21(5):1555.
Jurgens, C. Y., Lee, C. S., Aycock, D. M., Masterson Creber, R., Denfeld, Q. E., DeVon, H. A., Evers, L. R., Jung, M., Pucciarelli, G., Streur, M. M., et al. (2022). State of the science: the relevance of symptoms in cardiovascular disease and research: a scientific statement from the american heart association. Circulation, 146(12):e173–e184.
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Korzeniewska, E., Krawczyk, A., Mróz, J., Wyszyńska, E., and Zawiślak, R. (2020). Applications of smart textiles in post-stroke rehabilitation. Sensors, 20(8):2370.
Kuriakose, D. and Xiao, Z. (2020). Pathophysiology and treatment of stroke: present status and future perspectives. International journal of molecular sciences, 21(20):7609.
Liu, M., Wilder, S., Sanford, S., Glassen, M., Dewil, S., Saleh, S., and Nataraj, R. (2023). Augmented feedback modes during functional grasp training with an intelligent glove and virtual reality for persons with traumatic brain injury. Frontiers in Robotics and AI, 10:1230086.
Phatsornsiri, P., Kumngern, M., Khateb, F., Torteanchai, U., Wongprommoon, N., and Kulej, T. (2025). 16-nw 0.5-v low-pass filter for bio-signal applications. AEU-International Journal of Electronics and Communications, 188:155570.
Shin, S., Lee, H.-J., Chang, W. H., Ko, S. H., Shin, Y.-I., and Kim, Y.-H. (2022). A smart glove digital system promotes restoration of upper limb motor function and enhances cortical hemodynamic changes in subacute stroke patients with mild to moderate weakness: a randomized controlled trial. Journal of Clinical Medicine, 11(24):7343.
Sommer, D., Lermer, E., Wahl, F., and Lopera G, L. I. (2025). Assistive technologies in healthcare: utilization and healthcare workers perceptions in germany. BMC Health Services Research, 25(1):223.
Stinear, C. M., Lang, C. E., Zeiler, S., and Byblow, W. D. (2020). Advances and challenges in stroke rehabilitation. The Lancet Neurology, 19(4):348–360.
Triccas, L. T., Kennedy, N., Smith, T., and Pomeroy, V. (2019). Predictors of upper limb spasticity after stroke? a systematic review and meta-analysis. Physiotherapy, 105(2):163–173.
Tseng, K. C., Wang, L., Hsieh, C., and Wong, A. M. (2024). Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis. Annals of medicine, 56(1):2337735.
Veldema, J. and Jansen, P. (2020). Resistance training in stroke rehabilitation: systematic review and meta-analysis. Clinical rehabilitation, 34(9):1173–1197.
Wang, Y., Li, X., Sun, C., and Xu, R. (2022). Effectiveness of kinesiology taping on the functions of upper limbs in patients with stroke: a meta-analysis of randomized trial. Neurological Sciences, 43(7):4145–4156.
Yang, G., Deng, J., Pang, G., Zhang, H., Li, J., Deng, B., Pang, Z., Xu, J., Jiang, M., Liljeberg, P., et al. (2018). An iot-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE journal of translational engineering in health and medicine, 6:1–10.
Zakri, A. A., Yuliani Putri, R., Romadoni, S., and Hasneli, Y. (2024). IoT-based flex sensor gloves for immobility patients: A prototype. International Journal on Advanced Science, Engineering & Information Technology, 14(3).
Zhao, J., Zhou, H., Liu, P., Dong, Z., Huo, H., Li, H., Wang, M., and Zhang, S. (2025). Sensitivity enhancement for resonant multi-dimensional force sensor through integrated optimization of elastic structure and piezoelectric patch layout. Sensors and Actuators A: Physical, page 116691.
Zille, M., Farr, T. D., Keep, R. F., Römer, C., Xi, G., and Boltze, J. (2022). Novel targets, treatments, and advanced models for intracerebral haemorrhage. EBioMedicine, 76.
Almeida, C., Filho, P., Rosal, I., Gonçalves, R., Aguilar, P., Nunes, M., and Martins, M. (2024). Proposta de tecnologia assistiva para auxiliar pessoas com deficiência visual na detecção de obstáculos. In Anais do LI Seminário Integrado de Software e Hardware, pages 288–299, Porto Alegre, RS, Brasil. SBC.
Ase, H., Honaga, K., Tani, M., Takakura, T., Wada, F., Murakami, Y., Isayama, R., Tanuma, A., and Fujiwara, T. (2025). Effects of home-based virtual reality upper extremity rehabilitation in persons with chronic stroke: a randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 22(1):20.
Barthels, D. and Das, H. (2020). Current advances in ischemic stroke research and therapies. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1866(4):165260.
Devittori, G., Dinacci, D., Romiti, D., Califfi, A., Petrillo, C., Rossi, P., Ranzani, R., Gassert, R., and Lambercy, O. (2024). Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience. Journal of neuroengineering and rehabilitation, 21(1):52.
Dhiman, H. and Kumar, R. (2025). An internet of things-enabled smart glove for brain stroke rehabilitation. Measurement: Digitalization, page 100001.
Dias, P., Silva, R., Amorim, P., Lains, J., Roque, E., Serôdio, I., Pereira, F., and Santos, B. S. (2019). Using virtual reality to increase motivation in poststroke rehabilitation. IEEE computer graphics and applications, 39(1):64–70.
Dong, Y.-S., Ao, S.-J., and Gong, H. (2025). Adaptive robotic polishing based on distributed flexible force sensors. Robotics and Computer-Integrated Manufacturing, 95:103020.
Gong, D., Li, X., Chen, Y., and Jiao, T. (2025). Recent progress in mof-based flexible sensors: A review. Sustainable Materials and Technologies, page e01429.
Gorelick, P. B. (2019). The global burden of stroke: persistent and disabling. The Lancet Neurology, 18(5):417–418.
Guo, N., Wang, X., Duanmu, D., Huang, X., Li, X., Fan, Y., Li, H., Liu, Y., Yeung, E. H. K., To, M. K. T., et al. (2022). Ssvep-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE transactions on neural systems and rehabilitation engineering, 30:1737–1744.
He, X., Cui, Z., Zhang, F., Li, Y., Tu, J., Cao, J., Wang, J., Qiao, Y., Xi, P., Xu, T., et al. (2024). Multiscale heterogeneities-based piezoresistive interfaces with ultra-low detection limitation and adaptively switchable pressure detectability. ACS nano, 18(11):8296–8306.
Henderson, J., Condell, J., Connolly, J., Kelly, D., and Curran, K. (2021). Reliability and validity of clinically accessible smart glove technologies to measure joint range of motion. Sensors, 21(5):1555.
Jurgens, C. Y., Lee, C. S., Aycock, D. M., Masterson Creber, R., Denfeld, Q. E., DeVon, H. A., Evers, L. R., Jung, M., Pucciarelli, G., Streur, M. M., et al. (2022). State of the science: the relevance of symptoms in cardiovascular disease and research: a scientific statement from the american heart association. Circulation, 146(12):e173–e184.
Kang, M.-G., Yun, S. J., Lee, S. Y., Oh, B.-M., Lee, H. H., Lee, S.-U., and Seo, H. G. (2020). Effects of upper-extremity rehabilitation using smart glove in patients with subacute stroke: results of a prematurely terminated multicenter randomized controlled trial. Frontiers in neurology, 11:580393.
Klamroth-Marganska, V. (2018). Stroke rehabilitation: therapy robots and assistive devices. Sex-Specific Analysis of Cardiovascular Function, pages 579–587.
Korzeniewska, E., Krawczyk, A., Mróz, J., Wyszyńska, E., and Zawiślak, R. (2020). Applications of smart textiles in post-stroke rehabilitation. Sensors, 20(8):2370.
Kuriakose, D. and Xiao, Z. (2020). Pathophysiology and treatment of stroke: present status and future perspectives. International journal of molecular sciences, 21(20):7609.
Liu, M., Wilder, S., Sanford, S., Glassen, M., Dewil, S., Saleh, S., and Nataraj, R. (2023). Augmented feedback modes during functional grasp training with an intelligent glove and virtual reality for persons with traumatic brain injury. Frontiers in Robotics and AI, 10:1230086.
Phatsornsiri, P., Kumngern, M., Khateb, F., Torteanchai, U., Wongprommoon, N., and Kulej, T. (2025). 16-nw 0.5-v low-pass filter for bio-signal applications. AEU-International Journal of Electronics and Communications, 188:155570.
Shin, S., Lee, H.-J., Chang, W. H., Ko, S. H., Shin, Y.-I., and Kim, Y.-H. (2022). A smart glove digital system promotes restoration of upper limb motor function and enhances cortical hemodynamic changes in subacute stroke patients with mild to moderate weakness: a randomized controlled trial. Journal of Clinical Medicine, 11(24):7343.
Sommer, D., Lermer, E., Wahl, F., and Lopera G, L. I. (2025). Assistive technologies in healthcare: utilization and healthcare workers perceptions in germany. BMC Health Services Research, 25(1):223.
Stinear, C. M., Lang, C. E., Zeiler, S., and Byblow, W. D. (2020). Advances and challenges in stroke rehabilitation. The Lancet Neurology, 19(4):348–360.
Triccas, L. T., Kennedy, N., Smith, T., and Pomeroy, V. (2019). Predictors of upper limb spasticity after stroke? a systematic review and meta-analysis. Physiotherapy, 105(2):163–173.
Tseng, K. C., Wang, L., Hsieh, C., and Wong, A. M. (2024). Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis. Annals of medicine, 56(1):2337735.
Veldema, J. and Jansen, P. (2020). Resistance training in stroke rehabilitation: systematic review and meta-analysis. Clinical rehabilitation, 34(9):1173–1197.
Wang, Y., Li, X., Sun, C., and Xu, R. (2022). Effectiveness of kinesiology taping on the functions of upper limbs in patients with stroke: a meta-analysis of randomized trial. Neurological Sciences, 43(7):4145–4156.
Yang, G., Deng, J., Pang, G., Zhang, H., Li, J., Deng, B., Pang, Z., Xu, J., Jiang, M., Liljeberg, P., et al. (2018). An iot-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE journal of translational engineering in health and medicine, 6:1–10.
Zakri, A. A., Yuliani Putri, R., Romadoni, S., and Hasneli, Y. (2024). IoT-based flex sensor gloves for immobility patients: A prototype. International Journal on Advanced Science, Engineering & Information Technology, 14(3).
Zhao, J., Zhou, H., Liu, P., Dong, Z., Huo, H., Li, H., Wang, M., and Zhang, S. (2025). Sensitivity enhancement for resonant multi-dimensional force sensor through integrated optimization of elastic structure and piezoelectric patch layout. Sensors and Actuators A: Physical, page 116691.
Zille, M., Farr, T. D., Keep, R. F., Römer, C., Xi, G., and Boltze, J. (2022). Novel targets, treatments, and advanced models for intracerebral haemorrhage. EBioMedicine, 76.
Publicado
19/07/2026
Como Citar
MATOS, Pedro Italo Lobo; SILVA, Arthur Roberto da; BRITO, William Marreiro; SILVA, Marcelo Martins da.
Protótipo de tecnologia assistiva para detecção de movimento e força dos dedos para reabilitação pós-acidente vascular cerebral (AVC). In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 554-565.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.23839.
