Towards CSI-based diversity activity recognition via LSTM-CNN encoder-decoder neural network
Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals ar...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 444; s. 260 - 273 |
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Elsevier B.V
15.07.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals are different for individuals under different place conditions even in the same environment. In this paper, we focus on how to weaken the accuracy differences among individuals on activity recognition and improve the robustness in one indoor environment. Based on this, we design a novel deep learning model called LCED which consists of one LSTM-based Encoder, features image presentation, and one CNN-based Decoder to weaken the accuracy differences among individuals on activity recognition. We first use a low-pass filter to remove high-frequency noise data in time-sequence signal data and design variance-based window method to determine the start and the end of time-sequence signal data corresponding to an activity. After that, we utilize the proposed LCED model to learn informative features space of activity data and improve the accuracy of sixteen activities. Experimental results show that the average accuracy of sixteen activities is high 95% and the accuracy differences among individuals on activity recognition averagely decreases by 3%. |
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| AbstractList | Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals are different for individuals under different place conditions even in the same environment. In this paper, we focus on how to weaken the accuracy differences among individuals on activity recognition and improve the robustness in one indoor environment. Based on this, we design a novel deep learning model called LCED which consists of one LSTM-based Encoder, features image presentation, and one CNN-based Decoder to weaken the accuracy differences among individuals on activity recognition. We first use a low-pass filter to remove high-frequency noise data in time-sequence signal data and design variance-based window method to determine the start and the end of time-sequence signal data corresponding to an activity. After that, we utilize the proposed LCED model to learn informative features space of activity data and improve the accuracy of sixteen activities. Experimental results show that the average accuracy of sixteen activities is high 95% and the accuracy differences among individuals on activity recognition averagely decreases by 3%. |
| Author | Lin, Chuang Guo, Linlin Wang, Chao Wang, Lei Zhang, Hang Diao, Guangqiang Guo, Weiyu Lu, Bingxian |
| Author_xml | – sequence: 1 givenname: Linlin surname: Guo fullname: Guo, Linlin email: linlin.teresa.guo@gmail.com organization: School of Software Technology, Dalian University of Technology, China – sequence: 2 givenname: Hang surname: Zhang fullname: Zhang, Hang email: hang.zhang1@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology Chinese Academcy of Sciences, University of Chinese Academy of Science, China – sequence: 3 givenname: Chao surname: Wang fullname: Wang, Chao email: chao.wang@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology Chinese Academcy of Sciences, University of Chinese Academy of Science, China – sequence: 4 givenname: Weiyu surname: Guo fullname: Guo, Weiyu email: guoweiyu96@gmail.com organization: Shenzhen Institutes of Advanced Technology Chinese Academcy of Sciences, University of Chinese Academy of Science, China – sequence: 5 givenname: Guangqiang surname: Diao fullname: Diao, Guangqiang email: dgq@sdyu.edu.cn organization: Shandong Youth University of Political Science, China – sequence: 6 givenname: Bingxian surname: Lu fullname: Lu, Bingxian email: bingxian.lu@dlut.edu.cn organization: School of Software Technology, Dalian University of Technology, China – sequence: 7 givenname: Chuang surname: Lin fullname: Lin, Chuang email: chuang.lin@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology Chinese Academcy of Sciences, University of Chinese Academy of Science, China – sequence: 8 givenname: Lei surname: Wang fullname: Wang, Lei email: lei.wang@dlut.edu.cn organization: School of Software Technology, Dalian University of Technology, China |
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| Cites_doi | 10.1145/3314420 10.1145/3025453.3025678 10.1145/3351279 10.4018/ijaci.2014010102 10.1109/MCOM.2018.1701277 10.1145/3310194 10.1145/3307334.3326081 10.1109/TVT.2016.2555986 10.1145/2543581.2543592 10.1145/2500423.2500436 10.1145/3264958 10.1145/3356250.3360031 10.1145/2632048.2632095 10.1145/3191783 10.1145/3241539.3241548 10.1145/2789168.2790093 10.1145/2702123.2702200 10.1109/CVPR.2018.00768 10.1109/ICCCN.2018.8487345 10.1109/INFOCOM.2014.6847948 10.1145/2639108.2639143 |
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| Keywords | WiFi signals Human activity recognition Convolutional neural network Channel state information Long short term memory |
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| References | Ioffe, Szegedy (b0060) 2015 Wang, Zhang, Gao, Yue, Wang (b0125) 2017; 66 W. Wei, X.Liu, A., M, S., Gait Recognition Using Wi-Fi Signals, in: Proc. of ACM UbiComp, 2016. Halperin, Hu, Sheth, Wetherall (b0050) 2011; 41 Y. Zeng, D. Wu, R. Gao, T. Gu, D. Zhang, FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2018, pp. 148:1–148:19. M. Zhao, T. Li, M.A. Alsheikh, Y. Tian, H. Zhao, A. Torralba, D. Katabi, Through-Wall Human Pose Estimation Using Radio Signals, in: Proc. of ACM CVPR, ACM, 2018, pp. 7356–7365. L. Chen, X. Chen, S.I. Lee, K. Chen, D. Han, D. Fang, Z. Tang, Z. Wang, WIDESEE: Towards Wide-Area Contactless Wireless Sensing, in: Proc. of ACM SenSys, 2019. H. Zou, J. Yang, Y. Zhou, L. Xie, C.J. Spanos, Robust WiFi-enabled Device-free Gesture Recognition via Unsupervised Adversarial Domain Adaption, in: Proc. of IEEE ICCCN, 2018, pp. 1–8. B. Fang, N.D. Lane, M. Zhang, A. Boran, F. Kawsar, BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring, in: Proc. of IEEE MobiSys, 2016. N. Yu, W. Wang, A.X. Liu, L. Kong, QGesture: Quantifying Gesture Distance and Direction with WiFi Signals, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2018, pp. 1–23. Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home Gesture Recognition Using Wireless Signals, in: Proc. of ACM MobiCom, 2013. Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, H. Liu, E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures, in: Proc. of ACM Mobicom, 2014. Gu, Wang, Kuen, Ma, Shahroudy, Bing, Liu, Wang, Gang (b0035) 2015 Chen, Zhang, Jiang, Cui (b0020) 2018 Sigg, Shi, Ji (b0115) 2014; 6 F. Adib, H. Mao, Z. Kebelac, D. Katabi, C. Miller, Smart Homes That Monitor Breathing and Heart Rate, in: Proc. of ACM CHI, 2015. W. Jiang, C. Miao, S.Y. Fenglong Ma, Y. Wang, Y. Yuan, H. Xue, C. Song, D.K. Xin Ma, W. Xu, L. Su, Towards Environment Independent Device Free Human Activity Recognition, in: Proc. of ACM MobiCom, 2018, pp. 1–16. C. Olah, Understanding LSTM Networks, 2015. URL Niu, Zhang, Jie Xiong, Yi, Zhang (b0095) 2018 Abdelnasser, Youssef, Harras (b0005) 2015 F. Zhang, K. Niu, J. Xiong, B. Jin, T. Gu, Y. Jiang, D. Zhang, Towards a Diffraction-based Sensing Approach on Human Activity Recognition, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2019, pp. 33–57. Wang, Wang, Mao (b0135) 2018 Z. Yang, Z. Zhou, Y. Liu, From RSSI to CSI: Indoor Localization via Channel Response, ACM Comput. Surv. 46 (2013) 25:1–25:32. C. Han, K. Wu, Y. Wang, L.M. Ni, WiFall: Device-free fall detection by wireless networks, in: Proc. of IEEE INFOCOM, 2014. Y. Zheng, Y. Zhang, K. Qian, G. Zhang, Y. Liu, C. Wu, Z. Yang, Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi, in: Proc. of ACM Mobisys, 2019, pp. 1–13. W. Wang, A.X. Liu, M. Shahzad, K. Ling, S. Lu, Understanding and Modeling of WiFi Signal Based Human Activity Recognition, in: Proc. of ACM MobiCom, 2015. D. Halperin, W. Hu, A. Sheth, D. Wetherall, Linux 802.11n csi tool, 2010. Z.C. Lipton, J. Berkowitz, A Critical Review of Recurrent Neural Networks for Sequence Learning, 2015. CoRR abs/1506.00019. URL . Wang, Song, Zhang, Han, Huang (b0120) 2019 Guo, Zhang, Wang, Guo, Wang, Lin (b0040) 2019 Y. Ma, G. Zhou, S. Wang, WiFi Sensing with Channel State Information: A Survey, ACM Comput. Surv., 2019. abs/1506.00019, 46:1–46:36. URL:https://doi.org/10.1145/3310194. K. Qian, Z. Zhou, Y. Zheng, Z. Yang, Y. Liu, Inferring Motion Direction using Commodity WiFi for Interactive Exergames, in: Proc. of ACM CHI, 2017. P. Melgarejo, X. Zhang, P. Ramanathan, D. Chu, Leveraging Directional Antenna Capabilities for Fine-grained Gesture Recognition, in: Proc. of ACM UbiComp, 2014. Y. Zeng, D. Wu, J. Xiong, E. Yi, R. Gao, D. Zhang, FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas, in: Proc. of ACM UbiComp, 2019. Zheng, Wang, Shangguan, Zhou, Liu (b0185) 2016 Guo, Lei Wang, Lu (b0070) 2018 Zou, Zhou, Yang, Jiang, Xie, Spanos (b0195) 2018 H. Chen-Yu, L. Yuchen, K. Zach, H. Rumen, K. Dina, L. Christine, Extracting Gait Velocity and Stride Length from Surrounding Radio Signals, in: Proc. of ACM CHI, 2017. Liu, Cao, Tang, Wen (b0080) 2014 arXiv:1506.00019. 10.1016/j.neucom.2020.02.137_b0055 Liu (10.1016/j.neucom.2020.02.137_b0080) 2014 10.1016/j.neucom.2020.02.137_b0110 10.1016/j.neucom.2020.02.137_b0010 10.1016/j.neucom.2020.02.137_b0175 10.1016/j.neucom.2020.02.137_b0075 10.1016/j.neucom.2020.02.137_b0130 10.1016/j.neucom.2020.02.137_b0030 10.1016/j.neucom.2020.02.137_b0015 10.1016/j.neucom.2020.02.137_b0155 Niu (10.1016/j.neucom.2020.02.137_b0095) 2018 Ioffe (10.1016/j.neucom.2020.02.137_b0060) 2015 Zou (10.1016/j.neucom.2020.02.137_b0195) 2018 Guo (10.1016/j.neucom.2020.02.137_b0040) 2019 Wang (10.1016/j.neucom.2020.02.137_b0125) 2017; 66 10.1016/j.neucom.2020.02.137_b0190 10.1016/j.neucom.2020.02.137_b0090 Guo (10.1016/j.neucom.2020.02.137_b0070) 2018 Sigg (10.1016/j.neucom.2020.02.137_b0115) 2014; 6 10.1016/j.neucom.2020.02.137_b0150 10.1016/j.neucom.2020.02.137_b0170 10.1016/j.neucom.2020.02.137_b0165 10.1016/j.neucom.2020.02.137_b0065 Wang (10.1016/j.neucom.2020.02.137_b0135) 2018 Chen (10.1016/j.neucom.2020.02.137_b0020) 2018 10.1016/j.neucom.2020.02.137_b0085 10.1016/j.neucom.2020.02.137_b0140 Zheng (10.1016/j.neucom.2020.02.137_b0185) 2016 10.1016/j.neucom.2020.02.137_b0025 Wang (10.1016/j.neucom.2020.02.137_b0120) 2019 10.1016/j.neucom.2020.02.137_b0145 10.1016/j.neucom.2020.02.137_b0045 10.1016/j.neucom.2020.02.137_b0100 10.1016/j.neucom.2020.02.137_b0105 Abdelnasser (10.1016/j.neucom.2020.02.137_b0005) 2015 Gu (10.1016/j.neucom.2020.02.137_b0035) 2015 Halperin (10.1016/j.neucom.2020.02.137_b0050) 2011; 41 10.1016/j.neucom.2020.02.137_b0160 10.1016/j.neucom.2020.02.137_b0180 |
| References_xml | – reference: F. Zhang, K. Niu, J. Xiong, B. Jin, T. Gu, Y. Jiang, D. Zhang, Towards a Diffraction-based Sensing Approach on Human Activity Recognition, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2019, pp. 33–57. – reference: Y. Zeng, D. Wu, J. Xiong, E. Yi, R. Gao, D. Zhang, FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas, in: Proc. of ACM UbiComp, 2019. – year: 2018 ident: b0070 article-title: HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data publication-title: Wireless Communications and Mobile Computing – reference: D. Halperin, W. Hu, A. Sheth, D. Wetherall, Linux 802.11n csi tool, 2010. – reference: Y. Ma, G. Zhou, S. Wang, WiFi Sensing with Channel State Information: A Survey, ACM Comput. Surv., 2019. abs/1506.00019, 46:1–46:36. URL:https://doi.org/10.1145/3310194. – year: 2015 ident: b0005 article-title: WiGest: a ubiquitous WiFi-based gesture recognition system publication-title: Proc. of IEEE INFOCOM. – reference: H. Chen-Yu, L. Yuchen, K. Zach, H. Rumen, K. Dina, L. Christine, Extracting Gait Velocity and Stride Length from Surrounding Radio Signals, in: Proc. of ACM CHI, 2017. – start-page: 1 year: 2018 end-page: 13 ident: b0095 article-title: Boosting fine-grained activity sensing by embracing wireless multipath effects publication-title: Proc. of ACM CoNEXT – year: 2016 ident: b0185 article-title: Smokey: Ubiquitous Smoking Detection with Commerical WiFi Infrastructures publication-title: Proc. of IEEE INFOCOM – year: 2015 ident: b0035 article-title: Recent Advances in Convolutional Neural Networks – reference: C. Olah, Understanding LSTM Networks, 2015. URL: – reference: K. Qian, Z. Zhou, Y. Zheng, Z. Yang, Y. Liu, Inferring Motion Direction using Commodity WiFi for Interactive Exergames, in: Proc. of ACM CHI, 2017. – reference: , arXiv:1506.00019. – reference: P. Melgarejo, X. Zhang, P. Ramanathan, D. Chu, Leveraging Directional Antenna Capabilities for Fine-grained Gesture Recognition, in: Proc. of ACM UbiComp, 2014. – reference: Y. Zheng, Y. Zhang, K. Qian, G. Zhang, Y. Liu, C. Wu, Z. Yang, Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi, in: Proc. of ACM Mobisys, 2019, pp. 1–13. – start-page: 1 year: 2019 end-page: 8 ident: b0040 article-title: Towards Diversity Activity Recognition Via LSTM-CNN Encoder-Decoder Neural Network publication-title: Proc. of ACM IJCAI Workshop – reference: Y. Zeng, D. Wu, R. Gao, T. Gu, D. Zhang, FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2018, pp. 148:1–148:19. – year: 2014 ident: b0080 article-title: Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals publication-title: Pro of IEEE RTSS – reference: W. Wei, X.Liu, A., M, S., Gait Recognition Using Wi-Fi Signals, in: Proc. of ACM UbiComp, 2016. – start-page: 1 year: 2018 end-page: 6 ident: b0195 article-title: DeepSense: Device-Free Human Activity Recognition via Autoencoder Long-Term Recurrent Convolutional Network publication-title: IEEE International Conference on Communications (ICC) – reference: Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, H. Liu, E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures, in: Proc. of ACM Mobicom, 2014. – reference: W. Jiang, C. Miao, S.Y. Fenglong Ma, Y. Wang, Y. Yuan, H. Xue, C. Song, D.K. Xin Ma, W. Xu, L. Su, Towards Environment Independent Device Free Human Activity Recognition, in: Proc. of ACM MobiCom, 2018, pp. 1–16. – reference: Z. Yang, Z. Zhou, Y. Liu, From RSSI to CSI: Indoor Localization via Channel Response, ACM Comput. Surv. 46 (2013) 25:1–25:32. – reference: B. Fang, N.D. Lane, M. Zhang, A. Boran, F. Kawsar, BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring, in: Proc. of IEEE MobiSys, 2016. – reference: N. Yu, W. Wang, A.X. Liu, L. Kong, QGesture: Quantifying Gesture Distance and Direction with WiFi Signals, in: Proc. of ACM Interact. Mob. Wearable Ubiquitous Technol, 2018, pp. 1–23. – reference: M. Zhao, T. Li, M.A. Alsheikh, Y. Tian, H. Zhao, A. Torralba, D. Katabi, Through-Wall Human Pose Estimation Using Radio Signals, in: Proc. of ACM CVPR, ACM, 2018, pp. 7356–7365. – volume: 6 start-page: 20 year: 2014 end-page: 34 ident: b0115 article-title: Teach your WiFi device: recognize simultaneous activities and gestures from time-domain RF-features publication-title: Int. J. Ambient Comput. Intell. – reference: Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home Gesture Recognition Using Wireless Signals, in: Proc. of ACM MobiCom, 2013. – start-page: 1 year: 2019 end-page: 14 ident: b0120 article-title: Temporal Unet: sample level human action recognition using WiFi publication-title: Proc. of arXiv – start-page: 62 year: 2018 end-page: 67 ident: b0135 article-title: RF Sensing in the internet of things: a general deep learning framework publication-title: IEEE Commun. Mag. – reference: . – reference: Z.C. Lipton, J. Berkowitz, A Critical Review of Recurrent Neural Networks for Sequence Learning, 2015. CoRR abs/1506.00019. URL: – year: 2018 ident: b0020 article-title: WiFi CSI based passive human activity recognition using attention based BLSTM publication-title: IEEE Trans. Mob. Comput. – year: 2015 ident: b0060 article-title: Batch normalization: accelerating deep network training by reducing internal covariate shift publication-title: International Conference on International Conference on Machine Learning – volume: 66 start-page: 1659 year: 2017 end-page: 1669 ident: b0125 article-title: Device-free simultaneous wireless localization and activity recognition with wavelet feature publication-title: IEEE Trans. Veh. Technol. – volume: 41 year: 2011 ident: b0050 article-title: Tool Release: Gathering 802.11n Traces with Channel State Information publication-title: Proc. of ACM SIGCOMM CCR – reference: C. Han, K. Wu, Y. Wang, L.M. Ni, WiFall: Device-free fall detection by wireless networks, in: Proc. of IEEE INFOCOM, 2014. – reference: W. Wang, A.X. Liu, M. Shahzad, K. Ling, S. Lu, Understanding and Modeling of WiFi Signal Based Human Activity Recognition, in: Proc. of ACM MobiCom, 2015. – reference: L. Chen, X. Chen, S.I. Lee, K. Chen, D. Han, D. Fang, Z. Tang, Z. Wang, WIDESEE: Towards Wide-Area Contactless Wireless Sensing, in: Proc. of ACM SenSys, 2019. – reference: F. Adib, H. Mao, Z. Kebelac, D. Katabi, C. Miller, Smart Homes That Monitor Breathing and Heart Rate, in: Proc. of ACM CHI, 2015. – reference: H. Zou, J. Yang, Y. Zhou, L. Xie, C.J. Spanos, Robust WiFi-enabled Device-free Gesture Recognition via Unsupervised Adversarial Domain Adaption, in: Proc. of IEEE ICCCN, 2018, pp. 1–8. – ident: 10.1016/j.neucom.2020.02.137_b0025 – ident: 10.1016/j.neucom.2020.02.137_b0075 – ident: 10.1016/j.neucom.2020.02.137_b0175 doi: 10.1145/3314420 – ident: 10.1016/j.neucom.2020.02.137_b0110 doi: 10.1145/3025453.3025678 – year: 2018 ident: 10.1016/j.neucom.2020.02.137_b0020 article-title: WiFi CSI based passive human activity recognition using attention based BLSTM publication-title: IEEE Trans. Mob. Comput. – year: 2018 ident: 10.1016/j.neucom.2020.02.137_b0070 article-title: HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data – ident: 10.1016/j.neucom.2020.02.137_b0170 doi: 10.1145/3351279 – volume: 6 start-page: 20 year: 2014 ident: 10.1016/j.neucom.2020.02.137_b0115 article-title: Teach your WiFi device: recognize simultaneous activities and gestures from time-domain RF-features publication-title: Int. J. Ambient Comput. Intell. doi: 10.4018/ijaci.2014010102 – start-page: 62 year: 2018 ident: 10.1016/j.neucom.2020.02.137_b0135 article-title: RF Sensing in the internet of things: a general deep learning framework publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2018.1701277 – ident: 10.1016/j.neucom.2020.02.137_b0145 – year: 2016 ident: 10.1016/j.neucom.2020.02.137_b0185 article-title: Smokey: Ubiquitous Smoking Detection with Commerical WiFi Infrastructures – ident: 10.1016/j.neucom.2020.02.137_b0085 doi: 10.1145/3310194 – volume: 41 year: 2011 ident: 10.1016/j.neucom.2020.02.137_b0050 article-title: Tool Release: Gathering 802.11n Traces with Channel State Information publication-title: Proc. of ACM SIGCOMM CCR – year: 2015 ident: 10.1016/j.neucom.2020.02.137_b0005 article-title: WiGest: a ubiquitous WiFi-based gesture recognition system publication-title: Proc. of IEEE INFOCOM. – ident: 10.1016/j.neucom.2020.02.137_b0160 doi: 10.1145/3307334.3326081 – ident: 10.1016/j.neucom.2020.02.137_b0030 – volume: 66 start-page: 1659 year: 2017 ident: 10.1016/j.neucom.2020.02.137_b0125 article-title: Device-free simultaneous wireless localization and activity recognition with wavelet feature publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2016.2555986 – ident: 10.1016/j.neucom.2020.02.137_b0150 doi: 10.1145/2543581.2543592 – ident: 10.1016/j.neucom.2020.02.137_b0105 doi: 10.1145/2500423.2500436 – start-page: 1 year: 2019 ident: 10.1016/j.neucom.2020.02.137_b0040 article-title: Towards Diversity Activity Recognition Via LSTM-CNN Encoder-Decoder Neural Network publication-title: Proc. of ACM IJCAI Workshop – ident: 10.1016/j.neucom.2020.02.137_b0165 doi: 10.1145/3264958 – year: 2015 ident: 10.1016/j.neucom.2020.02.137_b0035 – year: 2014 ident: 10.1016/j.neucom.2020.02.137_b0080 article-title: Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals – ident: 10.1016/j.neucom.2020.02.137_b0045 – ident: 10.1016/j.neucom.2020.02.137_b0015 doi: 10.1145/3356250.3360031 – ident: 10.1016/j.neucom.2020.02.137_b0090 doi: 10.1145/2632048.2632095 – start-page: 1 year: 2019 ident: 10.1016/j.neucom.2020.02.137_b0120 article-title: Temporal Unet: sample level human action recognition using WiFi publication-title: Proc. of arXiv – start-page: 1 year: 2018 ident: 10.1016/j.neucom.2020.02.137_b0195 article-title: DeepSense: Device-Free Human Activity Recognition via Autoencoder Long-Term Recurrent Convolutional Network publication-title: IEEE International Conference on Communications (ICC) – ident: 10.1016/j.neucom.2020.02.137_b0155 doi: 10.1145/3191783 – ident: 10.1016/j.neucom.2020.02.137_b0065 doi: 10.1145/3241539.3241548 – ident: 10.1016/j.neucom.2020.02.137_b0100 – ident: 10.1016/j.neucom.2020.02.137_b0130 doi: 10.1145/2789168.2790093 – ident: 10.1016/j.neucom.2020.02.137_b0010 doi: 10.1145/2702123.2702200 – ident: 10.1016/j.neucom.2020.02.137_b0180 doi: 10.1109/CVPR.2018.00768 – ident: 10.1016/j.neucom.2020.02.137_b0190 doi: 10.1109/ICCCN.2018.8487345 – start-page: 1 year: 2018 ident: 10.1016/j.neucom.2020.02.137_b0095 article-title: Boosting fine-grained activity sensing by embracing wireless multipath effects publication-title: Proc. of ACM CoNEXT – ident: 10.1016/j.neucom.2020.02.137_b0055 doi: 10.1109/INFOCOM.2014.6847948 – year: 2015 ident: 10.1016/j.neucom.2020.02.137_b0060 article-title: Batch normalization: accelerating deep network training by reducing internal covariate shift – ident: 10.1016/j.neucom.2020.02.137_b0140 doi: 10.1145/2639108.2639143 |
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| Title | Towards CSI-based diversity activity recognition via LSTM-CNN encoder-decoder neural network |
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