@article{bib1,
	author = {Zhou, Yimin and Jiang, Guolai and Lin, Yaorong},
	title = {A novel finger and hand pose estimation technique for real-time hand gesture recognition},
	journal = {Pattern Recognition},
	volume = {49},
	pages = {102-114},
	abstract = {This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time.},
	keywords = {Computer vision
	Finger modelling
	Salient hand edge
	Convolution operator
	Real-time hand gesture recognition},
	ISSN = {0031-3203},
	DOI = {10.1016/j.patcog.2015.07.014},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320315002745},
	year = {2016},
	type = {Journal Article}
}
@inproceedings{bib2,
	author = {Venkatnarayan, Raghav H and Page, Griffin and Shahzad, Muhammad},
	title = {Multi-User Gesture Recognition Using WiFi},
	booktitle = {Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services},
	publisher = {Association for Computing Machinery},
	pages = {401–413},
	keywords = {WiFi, Gesture recognition, Multi-user},
	DOI = {10.1145/3210240.3210335},
	type = {Conference Proceedings}
}
@inproceedings{bib17,
	author = {Abdelnasser, Heba and Youssef, Moustafa and Harras, Khaled A},
	title = {WiGest: A ubiquitous WiFi-based gesture recognition system},
	booktitle = {2015 IEEE Conference on Computer Communications (INFOCOM)},
	pages = {1472-1480},
	ISBN = {0743-166X},
	DOI = {10.1109/INFOCOM.2015.7218525},
	type = {Conference Proceedings}
}

@inproceedings{bib6,
	author = {Amirtha Varshini, AS and Bhavani, Guguloth and Vithya and Thilagavathy, R},
	title = {Real-time hand gesture recognition for robotic arm and home automation},
	booktitle = {2021 International Symposium on Electrical, Electronics and Information Engineering},
	pages = {218-223},
	DOI = {10.1145/3459104.3459142},
	type = {Conference Proceedings}
}

@article{bib15,
	author = {Anishchenko, Lesya and Zhuravlev, Andrey and Chizh, Margarita},
	title = {Fall detection using multiple bioradars and convolutional neural networks},
	journal = {Sensors},
	volume = {19},
	number = {24},
	pages = {5569},
	ISSN = {1424-8220},
	DOI = {10.3390/s19245569},
	url = {https://mdpi-res.com/d_attachment/sensors/sensors-19-05569/article_deploy/sensors-19-05569.pdf?version=1576567845},
	year = {2019},
	type = {Journal Article}
}

@article{bib34,
	author = {Bengio, Y. and Simard, P. and Frasconi, P.},
	title = {Learning long-term dependencies with gradient descent is difficult},
	journal = {IEEE Transactions on Neural Networks},
	volume = {5},
	number = {2},
	pages = {157-166},
	ISSN = {1941-0093},
	DOI = {10.1109/72.279181},
	url = {https://ieeexplore.ieee.org/document/279181/},
	year = {1994},
	type = {Journal Article}
}

@article{bib3,
	author = {Botros, Fady S and Phinyomark, Angkoon and Scheme, Erik J},
	title = {Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?},
	journal = {IEEE Transactions on Industrial Informatics},
	volume = {18},
	number = {1},
	pages = {174-184},
	ISSN = {1941-0050},
	DOI = {10.1109/TII.2020.3041618},
	url = {https://ieeexplore.ieee.org/document/9275331/},
	year = {2022},
	type = {Journal Article}
}

@article{bib14,
	author = {Chen, Zhaoxi and Li, Gang and Fioranelli, Francesco and Griffiths, Hugh},
	title = {Personnel recognition and gait classification based on multistatic micro-Doppler signatures using deep convolutional neural networks},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {15},
	number = {5},
	pages = {669-673},
	ISSN = {1545-598X},
	DOI = {10.1109/LGRS.2018.2806940},
	url = {https://ieeexplore.ieee.org/document/8307105/},
	year = {2018},
	type = {Journal Article}
}

@article{bib25,
	author = {Chen, Zhenghua and Zhang, Le and Jiang, Chaoyang and Cao, Zhiguang and Cui, Wei},
	title = {WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM},
	journal = {IEEE Transactions on Mobile Computing},
	volume = {18},
	pages = {2714-2724},
	DOI = {10.1109/TMC.2018.2878233},
	url = {https://ieeexplore.ieee.org/document/8514811/},
	year = {2019},
	type = {Journal Article}
}

@article{bib12,
	author = {Choi, Hyuckjin and Fujimoto, Manato and Matsui, Tomokazu and Misaki, Shinya and Yasumoto, Keiichi},
	title = {Wi-cal: Wifi sensing and machine learning based device-free crowd counting and localization},
	journal = {IEEE Access},
	volume = {10},
	pages = {24395-24410},
	ISSN = {2169-3536},
	DOI = {10.1109/ACCESS.2022.3155812},
	url = {https://ieeexplore.ieee.org/ielx7/6287639/9668973/09724220.pdf?tp=\&arnumber=9724220\&isnumber=9668973\&ref=},
	year = {2022},
	type = {Journal Article}
}

@inproceedings{bib4,
	author = {Chu, Xianzhi and Liu, Jiang and Shimamoto, Shigeru},
	title = {A Sensor-Based Hand Gesture Recognition System for Japanese Sign Language},
	booktitle = {2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech)},
	pages = {311-312},
	DOI = {10.1109/LifeTech52111.2021.9391981},
	type = {Conference Proceedings}
}

@article{bib30,
	author = {Chung, Junyoung and GUlcehre, caglar and Cho, KyungHyun and Bengio, Yoshua},
	title = {Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling},
	journal = {arXiv preprint arXiv:1412.3555},
	DOI = {10.48550/arXiv.1412.3555},
	url = {https://arxiv.org/pdf/1412.3555.pdf},
	year = {2014},
	type = {Journal Article}
}

@inproceedings{bib32,
	author = {Fang, Biyi and Lane, Nicholas D and Zhang, Mi and Boran, Aidan and Kawsar, Fahim},
	title = {BodyScan: A wearable device for contact-less radio-based sensing of body-related activities},
	booktitle = {Proceedings of ACM MobiSys},
	publisher = {Association for Computing Machinery},
	pages = {97–110},
	DOI = {10.1145/2906388.2906411},
	type = {Conference Proceedings}
}

@inproceedings{bib21,
	author = {Gringoli, Francesco and Schulz, Matthias and Link, Jakob and Hollick, Matthias},
	title = {Free Your CSI: A Channel State Information Extraction Platform For Modern Wi-Fi Chipsets},
	booktitle = {Proceedings of the 13th International Workshop on Wireless Network Testbeds, Experimental Evaluation \& Characterization},
	publisher = {Association for Computing Machinery},
	pages = {21–28},
	abstract = {Modern wireless transmission systems heavily benefit from knowing the channel response. The evaluation of Channel State Information (CSI) during the reception of a frame preamble is fundamental to properly equalizing the rest of the transmission at the receiver side. Reporting this state information back to the transmitter facilitates mechanisms such as beamforming and MIMO, thus boosting the network performance. While these features are an integral part of standards such as 802.11ac, accessing CSI data on commercial devices is either not possible, limited to outdated chipsets or very inflexible. This hinders the research and development of innovative CSI-dependent techniques including localization, object tracking, and interference evaluation. To help researchers and practitioners, we introduce the nexmon CSI Extractor Tool. It allows per-frame CSI extraction for up to four spatial streams using up to four receive chains on modern Broadcom and Cypress Wi-Fi chips with up to 80MHz bandwidth in both the 2.4 and 5GHz bands. The tool supports devices ranging from the low-cost Raspberry Pi platform, over mobile platforms such as Nexus smartphones to state-of-the-art Wi-Fi APs. We release all tools and Wi-Fi firmware patches as extensible open source project. It includes our user-friendly smartphone application to demonstrate the CSI extraction capabilities in form of a waterfall diagram.},
	keywords = {ofdm
	csi extraction
	802.11
	channel-state-information
	wi-fi},
	DOI = {10.1145/3349623.3355477},
	type = {Conference Proceedings}
}

@article{bib18,
	author = {Halperin, Daniel and Hu, Wenjun and Sheth, Anmol and Wetherall, David},
	title = {Tool release: Gathering 802.11 n traces with channel state information},
	journal = {ACM SIGCOMM computer communication review},
	volume = {41},
	number = {1},
	pages = {53},
	DOI = {10.1145/1925861.1925870},
	year = {2011},
	type = {Journal Article}
}

@article{bib9,
	author = {Huang, Jingchang and Duan, Ning and Ji, Peng and Ma, Chunyang and hu, feng and Ding, Yuanyuan and Yu, Yipeng and Zhou, Qianwei and Sun, Wei},
	title = {A Crowdsource-Based Sensing System for Monitoring Fine-Grained Air Quality in Urban Environments},
	journal = {IEEE Internet of Things Journal},
	volume = {6},
	number = {2},
	pages = {3240-3247},
	ISSN = {2327-4662
	2372-2541},
	DOI = {10.1109/jiot.2018.2881240},
	year = {2019},
	type = {Journal Article}
}

@inproceedings{bib5,
	author = {Huu, Phat Nguyen and Ngoc, Tien Luong and Minh, Quang Tran},
	title = {Proposing Gesture Recognition Algorithm Using Two-Stream Convolutional Network and LSTM},
	booktitle = {2020 IEEE Eighth International Conference on Communications and Electronics (ICCE)},
	pages = {427-432},
	DOI = {10.1109/ICCE48956.2021.9352147},
	type = {Conference Proceedings}
}

@article{bib16,
	author = {Jaehyun, Park and Yeojin, Jeong and Gaeun, Lee and Juntaek, Oh and Jongryul, Yang},
	title = {915-MHz continuous-wave Doppler radar sensor for detection of vital signs},
	journal = {Electronics},
	volume = {8},
	number = {5},
	pages = {561},
	ISSN = {2079-9292},
	DOI = {10.3390/electronics8050561},
	year = {2019},
	type = {Journal Article}
}

@article{bib24,
	author = {Jordan, Michael I and Mitchell, Tom M},
	title = {Machine learning: Trends, perspectives, and prospects},
	journal = {Science},
	volume = {349},
	number = {6245},
	pages = {255-260},
	ISSN = {0036-8075},
	DOI = {10.1126/science.aaa8415},
	year = {2015},
	type = {Journal Article}
}

@article{bib7,
	author = {Lien, Jaime and Gillian, Nicholas and Karagozler, M Emre and Amihood, Patrick and Schwesig, Carsten and Olson, Erik and Raja, Hakim and Poupyrev, Ivan},
	title = {Soli: Ubiquitous gesture sensing with millimeter wave radar},
	journal = {ACM Transactions on Graphics (TOG)},
	volume = {35},
	number = {4},
	pages = {1-19},
	ISSN = {0730-0301},
	DOI = {10.1145/2897824.2925953},
	year = {2016},
	type = {Journal Article}
}

@article{bib22,
	author = {Ma, Yongsen and Zhou, Gang and Wang, Shuangquan},
	title = {WiFi Sensing with Channel State Information: A Survey},
	journal = {ACM Comput. Surv.},
	volume = {52},
	number = {3},
	pages = {1-36},
	note = {0360-0300},
	abstract = {With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.},
	keywords = {WiFi imaging
	localization
	human identification
	human counting
	channel state information
	gesture recognition
	respiration monitoring
	activity recognition
	WiFi sensing},
	ISSN = {0360-0300},
	DOI = {10.1145/3310194},
	url = {https://doi.org/10.1145/3310194},
	year = {2019},
	type = {Journal Article}
}

@article{bib13,
	author = {Meng, Wei and Chen, Xingcan and Cui, Wei and Guo, Jing},
	title = {WiHGR: A Robust WiFi-Based Human Gesture Recognition System via Sparse Recovery and Modified Attention-Based BGRU},
	journal = {IEEE Internet of Things Journal},
	volume = {9},
	number = {12},
	pages = {10272-10282},
	ISSN = {2327-4662
	2372-2541},
	DOI = {10.1109/jiot.2021.3122435},
	year = {2022},
	type = {Journal Article}
}

@inproceedings{bib31,
	author = {Qian, Kun and Wu, Chenshu and Yang, Zheng and Liu, Yunhao and Jamieson, Kyle},
	title = {Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi},
	booktitle = {Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing},
	pages = {1-10},
	ISBN = {9781450349123},
	DOI = {10.1145/3084041.3084067},
	type = {Conference Proceedings}
}

@article{bib33,
	author = {Rubner, Yossi and Tomasi, Carlo and Guibas, Leonidas J},
	title = {The earth mover's distance as a metric for image retrieval},
	journal = {International journal of computer vision},
	volume = {40},
	pages = {99-121},
	ISSN = {0920-5691},
	DOI = {10.1023/A:1026543900054},
	year = {2000},
	type = {Journal Article}
}

@article{bib38,
	author = {S, Islam M and A, Jannat M K and N, Hossain M and S, Kim W and W, Lee S and H, Yang S},
	title = {STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI},
	journal = {Sensors (Basel)},
	volume = {23},
	number = {1},
	pages = {356},
	abstract = {Human activity recognition (HAR) has emerged as a significant area of research due to its numerous possible applications, including ambient assisted living, healthcare, abnormal behaviour detection, etc. Recently, HAR using WiFi channel state information (CSI) has become a predominant and unique approach in indoor environments compared to others (i.e., sensor and vision) due to its privacy-preserving qualities, thereby eliminating the need to carry additional devices and providing flexibility of capture motions in both line-of-sight (LOS) and non-line-of-sight (NLOS) settings. Existing deep learning (DL)-based HAR approaches usually extract either temporal or spatial features and lack adequate means to integrate and utilize the two simultaneously, making it challenging to recognize different activities accurately. Motivated by this, we propose a novel DL-based model named spatio-temporal convolution with nested long short-term memory (STC-NLSTMNet), with the ability to extract spatial and temporal features concurrently and automatically recognize human activity with very high accuracy. The proposed STC-NLSTMNet model is mainly comprised of depthwise separable convolution (DS-Conv) blocks, feature attention module (FAM) and NLSTM. The DS-Conv blocks extract the spatial features from the CSI signal and add feature attention modules (FAM) to draw attention to the most essential features. These robust features are fed into NLSTM as inputs to explore the hidden intrinsic temporal features in CSI signals. The proposed STC-NLSTMNet model is evaluated using two publicly available datasets: Multi-environment and StanWiFi. The experimental results revealed that the STC-NLSTMNet model achieved activity recognition accuracies of 98.20% and 99.88% on Multi-environment and StanWiFi datasets, respectively. Its activity recognition performance is also compared with other existing approaches and our proposed STC-NLSTMNet model significantly improves the activity recognition accuracies by 4% and 1.88%, respectively, compared to the best existing method.},
	keywords = {Humans
	*Neural Networks, Computer
	*Human Activities
	Motion
	channel state information (CSI)
	deep learning (DL)
	human activity recognition (HAR)
	nested long short-term memory (NLSTM)},
	ISSN = {1424-8220 (Electronic)
	1424-8220 (Linking)},
	DOI = {10.3390/s23010356},
	year = {2022},
	type = {Journal Article}
}

@article{bib35,
	author = {Shewalkar, Apeksha and Nyavanandi, Deepika and Ludwig, Simone A},
	title = {Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU},
	journal = {Journal of Artificial Intelligence and Soft Computing Research},
	volume = {9},
	number = {4},
	pages = {235-245},
	DOI = {10.2478/jaiscr20190006},
	year = {2019},
	type = {Journal Article}
}

@article{bib36,
	author = {Subramanian, Barathi and Olimov, Bekhzod and Naik, Shraddha M and Kim, Sangchul and Park, Kil-Houm and Kim, Jeonghong},
	title = {An integrated mediapipe-optimized GRU model for Indian sign language recognition},
	journal = {Scientific Reports},
	volume = {12},
	number = {1},
	pages = {11964},
	ISSN = {2045-2322},
	DOI = {10.1038/s41598022159987},
	year = {2022},
	type = {Journal Article}
}

@inproceedings{bib2,
	author = {Venkatnarayan, Raghav H and Page, Griffin and Shahzad, Muhammad},
	title = {Multi-User Gesture Recognition Using WiFi},
	booktitle = {Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services},
	publisher = {Association for Computing Machinery},
	pages = {401–413},
	keywords = {WiFi, Gesture recognition, Multi-user},
	DOI = {10.1145/3210240.3210335},
	url = {https://doi.org/10.1145/3210240.3210335},
	type = {Conference Proceedings}
}

@article{bib26,
	author = {Wang, Fangxin and Gong, Wei and Liu, Jiangchuan and Wu, Kui},
	title = {Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information},
	journal = {IEEE Transactions on Network Science and Engineering},
	volume = {7},
	number = {1},
	pages = {181-192},
	ISSN = {2327-4697},
	DOI = {10.1109/TNSE.2018.2825144},
	year = {2020},
	type = {Journal Article}
}

@article{bib28,
	author = {Wang, Jie and Zhang, Xiao and Gao, Qinhua and Yue, Hao and Wang, Hongyu},
	title = {Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach},
	journal = {IEEE Transactions on Vehicular Technology },
	volume = {66},
	number = {7},
	pages = {6258-6267},
	ISSN = {1939-9359},
	DOI = {10.1109/TVT.2016.2635161},
	year = {2016},
	type = {Journal Article}
}

@article{bib27,
	author = {Wang, Wei and Liu, Alex X and Shahzad, Muhammad and Ling, Kang and Lu, Sanglu},
	title = {Device-Free Human Activity Recognition Using Commercial WiFi Devices},
	journal = {IEEE Journal on Selected Areas in Communications},
	volume = {35},
	number = {5},
	pages = {1118-1131},
	DOI = {10.1109/JSAC.2017.2679658},
	year = {2017},
	type = {Journal Article}
}

@article{bib23,
	author = {Wang, Yuxi and Wu, Kaishun and Ni, Lionel M.},
	title = {WiFall: Device-free fall detection by wireless networks},
	journal = {IEEE Transactions on Mobile Computing},
	volume = {16},
	number = {2},
	pages = {581-594},
	DOI = {10.1109/TMC.2016.2557792},
	year = {2017},
	type = {Journal Article}
}

@article{bib10,
	author = {Wu, Chenshu and Wang, Beibei and Au, Oscar C and Liu, K J Ray},
	title = {Wi-Fi Can Do More: Toward Ubiquitous Wireless Sensing},
	journal = {IEEE Communications Standards Magazine},
	volume = {6},
	number = {2},
	pages = {42-49},
	ISSN = {2471-2833},
	DOI = {10.1109/MCOMSTD.0001.2100111},
	year = {2022},
	type = {Journal Article}
}

@article{bib20,
	author = {Yang, Jianfei and Chen, Xinyan and Zou, Han and Lu, Chris Xiaoxuan and Wang, Dazhuo and Sun, Sumei and Xie, Lihua},
	title = {SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing},
	journal = {Patterns},
	volume = {4},
	number = {3},
	pages = {100703},
	ISSN = {2666-3899},
	DOI = {10.1016/j.patter.2023.100703},
	year = {2023},
	type = {Journal Article}
}

@article{bib11,
	author = {Yanlin Ruan and LiangChe and Xin Zhou and Zhaoliang Liu and Xiaoyan Liu and Guangyi Guo and Chen, Ruizhi},
	title = {iPos-5G: Indoor Positioning via Commercial 5G NR CSI},
	journal = {IEEE Internet of Things Journal},
	volume = {10},
	number = {10},
	pages = {8718-8733},
	DOI = {10.1109/JIOT.2022.3232221},
	url = {https://ieeexplore.ieee.org/document/9999279/},
	year = {2023},
	type = {Journal Article}
}

@article{bib8,
	author = {Yousefi, Siamak and Narui, Hirokazu and Dayal, Sankalp and Ermon, Stefano and Valaee, Shahrokh},
	title = {A Survey on Behavior Recognition Using WiFi Channel State Information},
	journal = {IEEE Communications Magazine},
	volume = {55},
	number = {10},
	pages = {98-104},
	ISSN = {0163-6804},
	DOI = {10.1109/mcom.2017.1700082},
	year = {2017},
	type = {Journal Article}
}

@article{bib37,
	author = {Zhang, Biao and Xiong, Deyi and Xie, Jun and Su, Jinsong},
	title = {Neural machine translation with GRU-gated attention model},
	journal = {IEEE transactions on neural networks and learning systems},
	volume = {31},
	number = {11},
	pages = {4688-4698},
	ISSN = {2162-237X},
	DOI = {10.1109/TNNLS.2019.2957276},
	year = {2020},
	type = {Journal Article}
}

@article{bib19,
	author = {Zhang, Yi and Zheng, Yue and Qian, Kun and Zhang, Guidong and Liu, Yunhao and Wu, Chenshu and Yang, Zheng},
	title = {Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {44},
	number = {11},
	pages = {8671-8688},
	DOI = {10.1109/TPAMI.2021.3105387},
	url = {https://ieeexplore.ieee.org/document/9516988/},
	year = {2022},
	type = {Journal Article}
}

@article{bib1,
	author = {Zhou, Yimin and Jiang, Guolai and Lin, Yaorong},
	title = {A novel finger and hand pose estimation technique for real-time hand gesture recognition},
	journal = {Pattern Recognition},
	volume = {49},
	pages = {102-114},
	abstract = {This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time.},
	keywords = {Computer vision
	Finger modelling
	Salient hand edge
	Convolution operator
	Real-time hand gesture recognition},
	ISSN = {0031-3203},
	DOI = {10.1016/j.patcog.2015.07.014},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320315002745},
	year = {2016},
	type = {Journal Article}
}

@inproceedings{bib29,
	author = {Zhou, Zhiyi and Liu, Chang and Yu, Xingda and Yang, Cong and Duan, Pengsong and Cao, Yangjie},
	title = {Deep-WiID: WiFi-Based contactless human identification via deep learning},
	booktitle = {2019 IEEE SmartWorld, Ubiquitous Intelligence \& Computing, Advanced \& Trusted Computing, Scalable Computing \& Communications, Cloud \& Big Data Computing, Internet of People and Smart City Innovation},
	pages = {877-884},
	DOI = {10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00179},
	type = {Conference Proceedings}
}
@article{bib39,
	title={A Fine-grained Channel State Information-based Deep Learning System for Dynamic Gesture Recognition},
	author={Tong, Guoxiang and Li, Yueyang and Zhang, Haoyu and Xiong, Naixue},
	journal={Information Sciences},
	volume={636},
	pages={118912},
	year={2023},
	publisher={Elsevier}
}
@article{bib40,
	title={CSI-DeepNet: A Lightweight Deep Convolutional Neural Network Based Hand Gesture Recognition System Using Wi-Fi CSI Signal},
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