[1] [1] ABDELGHFAR H A, AHMED A M, ALANI A A, et al. QSLRS-CNN: Qur'anic sign language recognition system based on convolutional neural networks[J]. The Imaging Science Journal, 2024, 72(2): 254-266.

[2] [2] XIA K, LU W, FAN H, et al. A sign language recognition system applied to deaf-mute medical consultation[J]. Sensors (Basel), 2022, 22(23): 9107.

[4] [4] KRALJEVI L, RUSSO M, PAUKOVI M, et al. A dynamic gesture recognition interface for smart home control based on Croatian sign language[J]. Applied Sciences, 2020, 10(7): 2300.

[5] [5] AKDAG A, BAYKAN O K. Enhancing signer-independent recognition of isolated sign language through advanced deep learning techniques and feature fusion[J]. Electronics, 2024, 13(7): 1188.

[6] [6] ZHANG Y Q, JIANG X W. Recent advances on deep learning for sign language recognition[J]. Computer Modeling in Engineering & Sciences, 2024, 139(3): 2399-2450.

[7] [7] XIONG B P, CHEN W S, NIU Y X, et al. A global and local feature fused CNN architecture for the sEMG-based hand gesture recognition[J]. Computers in Biology and Medicine, 2023, 166: 107497.

[8] [8] NARAYAN S, MAZUMDAR A P, VIPPARTHI S K. SBI-DHGR: skeleton-based intelligent dynamic hand gestures recognition[J]. Expert Systems with Applications, 2023, 232: 120735.

[9] [9] AROOJ S, ALTAF S, AHMAD S, et al. Enhancing sign language recognition using CNN and SIFT: a case study on Pakistan sign language[J]. Journal of King Saud University-Computer and Information Sciences, 2024, 36(2): 101934.

[10] [10] SAMAAN G H, WADIE A R, ATTIA A K, et al. MediaPipe's landmarks with RNN for dynamic sign language recognition[J]. Electronics, 2022, 11(19): 3228.

[11] [11] YU J, HU C H, JING X Y, et al. Deep metric learning with dynamic margin hard sampling loss for face verification[J]. Signal, Image and Video Processing, 2020, 14(4): 791-798.

[12] [12] HAMZA M Z, EVEN L F, FILIPPO S. Empowering human-robot interaction using sEMG sensor: hybrid deep learning model for accurate hand gesture recognition[J]. Results in Engineering, 2023, 20: 101639.

[13] [13] GAO Y, YAN K, DAI B S, et al. Recognition of aggressive behavior of group-housed pigs based on CNN-GRU hybrid model with spatio-temporal attention mechanism[J]. Computers and Electronics in Agriculture, 2023, 205: 107606.

[14] [14] RAHAMAN M A, OYSHE K U, CHOWDHURY P K, et al. Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs[J]. Biomimetic Intelligence and Robotics, 2024, 4(1): 100141.

[15] [15] JI A, WANG Y, MIAO X, et al. Dataglove for sign language recognition of people with hearing and speech impairment via wearable inertial sensors[J]. Sensors (Basel), 2023, 23(15): 6693.

[16] [16] KUMARI D, ANAND R S. Isolated video-based sign language recognition using a hybrid CNN-LSTM framework based on attention mechanism[J]. Electronics, 2024, 13(7): 1229.

[17] [17] RAWF H M K, ABDULRAHMAN A O, MOHAMMED A A. Improved recognition of Kurdish sign language using modified CNN[J]. Computers, 2024, 13(2): 37.

[18] [18] FANG Y C, WANG L J, LIN S Q, et al. Visual feature segmentation with reinforcement learning for continuous sign language recognition[J]. International Journal of Multimedia Information Retrieval, 2023, 12(2): 39.

[19] [19] XUE C H, JIA J L, YU M, et al. Continuous sign language recognition based on hierarchical memory sequence network[J]. IET Computer Vision, 2024, 18(2): 247-259.

[20] [20] QIU S, FAN T Q, JIANG J H, et al. A novel two-level interactive action recognition model based on inertial data fusion[J]. Information Sciences, 2023, 633: 264-279.

[21] [21] WU J, REN P, SONG B, et al. Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism[J]. PLoS One, 2023, 18(11): e0294174.

[22] [22] FU R R, ZHANG B Z, LIANG H F, et al. Gesture recognition of sEMG signal based on GASF-LDA feature enhancement and adaptive ABC optimized SVM[J]. Biomedical Signal Processing and Control, 2023, 85: 105104.

[23] [23] KIM M, CHO J, LEE S, et al. IMU sensor-based hand gesture recognition for human-machine interfaces[J]. Sensors (Basel), 2019, 19(18): 3827.

[24] [24] SIDDIQUI N, CHAN R H M. Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist[J]. PLoS One, 2020, 15(1): e0227039.

[25] [25] PLAWIAK P, SOSNICKI T, NIEDZWIECKI M, et al. Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3): 1104-1113.

[26] [26] GUO H F, XIANG C C, CHEN S Q. Wearable sensors for human activity recognition based on a self-attention CNN-BiLSTM model[J]. Sensor Review, 2023, 43(5/6): 347-358.

[27] [27] BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[EB/OL]. [2024-06-01]. http://de.arxiv.org/pdf/1409.0473.

[28] [28] SANDOVAL-ESPINO J A, ZAMUDIO-LARA A, MARBN-SALGADO J A, et al. Selection of the best set of features for sEMG-based hand gesture recognition applying a CNN architecture[J]. Sensors (Basel), 2022, 22(13): 4972.

[30] [30] ZHOU H, ZHAO Y, LIU Y, et al. Multi-sensor data fusion and CNN-LSTM model for human activity recognition system[J]. Sensors (Basel), 2023, 23(10): 4750.

[31] [31] DUA N, SINGH S N, SEMWAL V B. Multi-input CNN-GRU based human activity recognition using wearable sensors[J]. Computing, 2021, 103(7): 1461-1478.

[32] [32] ZHANG Y P, WILKER K. Visual-and-language multimodal fusion for sweeping robot navigation based on CNN and GRU[J]. Journal of Organizational and End User Computing, 2024, 36(1): 1-21.