Download

Full Text

• H. Van Duinen and S.C. Gandevia, “Constraints for control of the human hand,” J. Physiol. 589 (23) (2011) 5583–5593, doi: 10.1113/jphysiol.2011.217810
• Rustam, H., Muneeb, M., Alsuhibany, S.A., et al., “Home automation-based health assessment along gesture recognition via inertial sensors,” CMC-Comput. Mater. Con. 75 (1) (2023) 2331–2346, doi: 10.32604/cmc.2023.028712.
• L.A. Ngiejungbwen, H. Hamdaoui, and M. Chen, “Polymer optical fiber and fiber Bragg grating sensors for biomedical engineering applications:a comprehensive review,” Opt. Laser. Technol. 170,(2024) 110187,doi:10.1016/j.optlastec.2023.110187.
• P. Lei, Y. Bao, L. Gao, et al., “Bioinspired integrated multidimensional sensor for adaptive grasping by robotic hands and physical movement guidance,” Adv. Funct. Mater. 34 (26) (2024) 2313787, doi: 10.1002/adfm.202313787.
• Di Qi, T., Cibrian, F.L., Raswan, M., et al., “Toward intuitive 3D interactions in Virtual reality: a deep learning-based dual-hand gesture recognition approach,” IEEE Access. 12 (2024) 67438–67452, doi: 10.1109/ACCESS.2024.3400295.
• D. Sarma, and M.K. Bhuyan, “Methods, databases and recent advancement of vision based hand gesture recognition for HCI systems: a review,” SN. Comput. Sci. 2 (6) (2021) 436, doi:10.1007/s42979-021-00827-x.
• K.R. Pyun, et al., “Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications,” Natl. Sci. Rev. 11 (2) (2024) 231–263, doi: 10.1093/nsr/nwad298.
• M.A. Ozdemir, D.H. Kisa, O. Guren, A. Onan, and A. Akan, “EMG based hand gesture recognition using Deep Learning,” 2020 Medical Technologies Congress (TIPTEKNO)., Antalya, Turkey,2020,7–19, doi:10.29081/jesr.v29i2.001.
• A. Jaramillo-Yanez, M.E. Benalcazar and E. Mena-Maldonado,Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review, Sensors.2467, 20 (9) (2020) doi:10.3390/s20092467.
• J. Chen, Y. Zhou, X. Chen, et al., “Recent progress in essential functions of soft electronic skin,” Adv. Funct. Mater. 31 (42) (2021),doi: 10.1002/adfm.202104686.
• H. Rao, B. Luo, D. Wu, et al., “Study on the design and performance of a glove based on the FBG array for hand posture sensing,” Sensors 23 (20) (2023) 8495, doi: 10.3390/s23208495.
• C. Zhang, H. Zhou, H. Nong, et al., “FBG-SenseGlove: a digital twin data glove for underwater gesture recognition using deep learning,” IEEE Sens. J. 25 (5) (2025) 8347–8354,doi: 10.1109/JSEN.2025.3532601.
• C.K. Jha, K. Gajapure, and A.L. Chakraborty, “Design and evaluation of an FBG sensor-based glove to simultaneously monitor flexure of ten finger joints,” IEEE Sens. J. 21 (6) (2021) 7620–7630, 2021, doi: 10.1109/JSEN.2020.3046521.
• C.K. Jha, S. Agarwal, A.L. Chakraborty, et al., “An FBG-based sensing glove to measure dynamic finger flexure with an angular resolution of 0.1° up to speeds of 80°/s,” J. Lightw. Technol. 37 (18) (2019) 4734–4740, doi: 10.1109/JLT.2019.2919496.
• L. Li, R. He, M.S. Soares, et al., “Embedded FBG-based sensor for joint movement monitoring,” IEEE Sens. J. 21 (2020) 26793–26798,doi: 10.1109/JSEN.2021.3120995.
• A.B. Socorro-Leranoz, S. Diaz, S. Castillo, et al., “Optical system based on multiplexed FBGs to monitor hand movements,” IEEE Sens. J. 21 (13) (2021) 14081–14089,doi: 10.1109/JSEN.2020.3002827.
• B.J. Zha, Z. Wang, L. Ma, et al., Intelligent wearable photonic sensing system for remote healthcare monitoring using stretchable elastomer optical fiber, IEEE Internet. Things. J..17317–17329, 11 (10) (2024),doi: 10.1109/JIOT.2024.3356574.
• N. Wang, Y. Yao, P. Wu, et al., “Soft polymer optical fiber sensors for intelligent recognition of elastomer deformations and wearable applications,” Sensors 2253, 24 (7) (2024),doi: 10.3390/s24072253.
• J. Li, B. Liu, Y. Hu, et al., “Plastic-optical-fiber-enabled smart glove for machine-learning-based gesture recognition,” IEEE Trans. Ind. Electron. 71 (4) (2024) 4252–4261,doi: 10.1109/TIE.2023.3277119.
• J. Zhang, X. Wang, H. Bi, et al., “High-precision dynamic gesture recognition based on microfiber sensor and EMT-net,” Sens. Actuators A: Phys. 379 (2024) 115852, doi: 10.1016/j.sna.2024.115852.
• S.P. Wang, X.Y. Wang, S. Wang, et al., “Optical-nanofiber-enabled gesture-recognition wristband for human-machine interaction with the assistance of machine learning,” Adv. Intell. Syst. 5 (7) (2023) 2200412,doi: 10.1002/aisy.202200412.
• X. Liang, Y. Long, K. Xiang, et al., “Advancing human-computer interaction: smartphone-integrated POF speckle sensor for gesture and identity recognition,” IEEe Sens. J. 24 (19) (2024) 30028–30037,doi: 10.1109/JSEN.2024.3437763.
• Y. Guo, J. Zhang, X. Li, et al., “Finger motion detection based on optical fiber Bragg grating with polyimide substrate,” Sens. Actuators A: Physol. 338 (2022) 113482,doi: 10.1016/j.sna.2022.113482.
• X. Li, Y. Guo, J. Zhang, “Investigation of gesture recognition based on optical fiber Bragg grating sensors,” Measurement 209 (2023) 112498, doi: 10.1016/j.measurement.2023.112498.
• P. Yi, B. Luo, D.C. Wu, et al., “Design and gesture recognition of wrist joint posture sensor based on fiber bragg gratings,” IEEe Sens. J. 24 (23) (2024) 39050–39058, doi: 10.1109/JSEN.2024.3481013.
• C. Broadway, R. Min, A.G. Leal-Junior, et al., “Toward commercial polymer fiber Bragg grating sensors: review and applications,” J. Lightw. Technol. 37 (11) (2019) 2605–2615, doi: 10.1109/JLT.2018.2885957.
• H. Qu, W. Liang, X. Cheng, et al., "Embedded POFBG for Respiratory and heart rate measurement," IEEE Sens. J. 25 (3) (2025) 4612–4619, doi: 10.1109/JSEN.2024.3515210.
• Z. Wang, M. Zhang, H. Chen, et al., “A generalized fault diagnosis framework for rotating machinery based on phase entropy,” Reliab. Eng. Syst. Saf. 110745, 256 (2025), doi: 10.1016/j.ress.2024.110745.
• Z. Wang, P. Liang, R. Bai, et al., “Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy,” Adv. Eng. Inform. 102972, 63 (2025), doi: 10.1016/j.aei.2024.102972.
• Z. Wang, Y. Liu, R. Bai, et al., “Multi-modal multi-scale multi-level fusion quadrant entropy for mechanical fault diagnosis,” Expert. Syst. Appl. 127715, 281 (2025), doi: 10.1016/j.eswa.2025.127715.
• K. Xiao, Z. Wang, Y.D. Ye, et al., “PDMS-embedded wearable FBG sensors for gesture recognition and communication assistance,” Biomed. Opt. Express. 15 (3) (2024) 1892, doi: 10.1364/BOE.517104.
• G. Woyessa, A. Fasano, A. Stefani, et al., “Single mode step-index polymer optical fiber for humidity insensitive high temperature fiber Bragg grating sensors”, Opt. Express 24 (2) (2016) 1253,doi: 10.1364/OE.24.001253.
• X. Hu, Y. Chen, S. Gao, et al., “Direct bragg grating inscription in single-mode step-index TOPAS/ZEONEX polymer optical fiber using 520 nm femtosecond pulses,” Polymers 2640, 14 (13) (2022), doi: 10.3390/polym14132640.
• S.H. Jeong, S. Zhang, K. Hjort, et al., “Stretchable electronic devices: pDMS-based elastomer tuned soft, stretchable, and sticky for epidermal electronics,” Adv. Mater. 28 (28) (2016) 5765–5775, doi: 10.1002/adma.20167019.
• Q. Li and R. Li, “Myoelectric human-computer interaction using CNN-LSTM neural network for dynamic hand gesture recognition,” J. Intell. Fuzzy Syst. 44 (3) (2023) 4207–4221, doi: 10.3233/JIFS-222985..
• A. Filipowska, W. Filipowski, P. Raif, et al., “Machine learning-based gesture recognition glove: design and implementation,” Sensors 24 (18) (2024) 6157, doi: 10.3390/s24186157.