[1] 武霞, 张崎, 许艳旭. 手势识别研究发展现状综述[J]. 电子科技, 2013, 26(6): 171-174.
[2] Hassan M, Assaleh K, Shanableh T. Userdependent sign language recognition using motion detection[C]//2016 International Conference on Computational Science and Computational Intelligence (CSCI). Las Vegas: IEEE, 2016: 852-856.
[3] Hyun D, Jegal M, Yang H S. Compact selfcontained navigation system with MEMS inertial sensor and optical navigation sensor for 3D pipeline mapping[C]//2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei: IEEE, 2010: 1488-1493.
[4] Cao Y, Xi Z W. A review of MEMS inertial switches[J]. Microsystem Technologies, 2019, 25(12): 4405-4425.
[5] Ciuti G, Ricotti L, Menciassi A, et al. MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy[J]. Sensors, 2015, 15(3): 6441-6468.
[6] Zhang X, Chen X, Li Y, et al. A framework for hand gesture recognition based on accelerometer and EMG sensors[J]. IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, 2011, 41(6): 1064-1076.
[7] Moin A, Zhou A, Benatti S, et al. Adaptive EMGbased hand gesture recognition using hyperdimensional computing. (2019-08-30)[2019-12-28]. https://arxiv.org/abs/1901-00234.
[8] Li H, Yang W, Wang J, et al. WiFinger: talk to your smart devices with fingergrained gesture[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg: 2016: 250-261.
[9] Wang F X, Gong W, Liu J C, et al. Channel selective activity recognition with WiFi: A deep learning approach exploring wideband information[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(1): 181-192.
[10] Ji Y, Kim S,Lee K B. Sign language learning system with image sampling and convolutional neural network[C]//2017 First IEEE International Conference on Robotic Computing (IRC), Taiwan: IEEE, 2017: 371-375.