Abstract
WiFi has recently established itself as a powerful medium for radio frequency (RF) sensing due to its low cost and convenience. Many tasks, such as gesture recognition, activity recognition, and fall detection, can be implemented by measuring and calculating how the propagation of WiFi signals is affected by human activities. However, current WiFi-based sensing solutions have limited scales as they are designed for only a few activities and need to collect data and create training models in the same domain because the model established in a deployment environment is usually not applicable to new objects in the target domain. This paper presents TransferSense, an environment independent and one-shot WiFi sensing method based on deep learning. Firstly, amplitude and phase information of channel state information (CSI) are combined to increase the number of features to solve the problem of insufficient features due to single-source information. Secondly, TransferSense converts RF sensing tasks to image classification tasks and fuses low-level and high-level semantic features extracted from a pre-trained convolutional neural network to achieve an end-to-end high-precision sensing for activity recognition. Finally, TransferSense applies a transfer learning method with a small number of labeled samples in the target domain to perform high-precision cross-domain sensing, which can reduce the data collection cost in the target domain. We verified the effectiveness of TransferSense using two representative WiFi sensing applications, gait identification and sign recognition. In a single deployment environment, TransferSense achieved more than 97% human gait identification accuracy for 44 users and more than 81% sign language recognition for 100 isolated sign language words. In the case of new object recognition in the cross-domain sensing, TransferSense achieved more than 77% human gait identification accuracy for 10 new users, more than 88% sign language recognition for 10 new isolated sign language words, and more than 81% gesture identification for 2 new gestures.
Similar content being viewed by others
References
Virmani A, Shahzad M (2017) Position and orientation agnostic gesture recognition using wifi. In: Proceedings of the 15th annual international conference on mobile systems, applications, and services, MobiSys ’17. ACM, New York, pp 252–264
Zou H, Yang J, Zhou Y, Xie L, Spanos CJ (2018) Robust wifi-enabled device-free gesture recognition via unsupervised adversarial domain adaptation. In: 27th International Conference on Computer Communication and Networks, ICCCN 2018, Hangzhou, China, July 30 - August 2, 2018. IEEE, pp 1–8
Abdelnasser H, Youssef M, Harras KA (2015) Wigest: A ubiquitous wifi-based gesture recognition system. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp 1472–1480
He W, Wu K, Zou Y, Ming Z (2015) Wig: Wifi-based gesture recognition system. In: 2015 24th International Conference on Computer Communication and Networks (ICCCN), pp 1–7
Sigg S, Shi S, Ji Y (2014) Teach your wifi-device: Recognise simultaneous activities and gestures from time-domain rf-features. IJACI 6(1):20–34
Venkatnarayan RH, Page G, Shahzad M (2018) Multi-user gesture recognition using wifi. In: Ott J, Dressler F, Saroiu S, Dutta P (eds) Proceedings of the 16th annual international conference on mobile systems, applications, and services, MobiSys 2018, Munich, Germany, June 10-15, 2018. ACM, pp 401–413
Zou H, Zhou Y, Yang J, Jiang H, Xie L, Spanos CJ (2018) Wifi-enabled device-free gesture recognition for smart home automation. In: 14th IEEE international conference on control and automation, ICCA 2018, Anchorage, AK, USA, June 12-15, 2018, pp 476–481
Jiang W, Miao C, Ma F, Yao S, Wang Y, Ye Y, Xue H, Song C, Ma X, Koutsonikolas D, Wenyao X, Lu S (2018) Towards environment independent device free human activity recognition. In: Shorey R, Murty R, Chen YJ, Jamieson K (eds) Proceedings of the 24th annual international conference on mobile computing and networking, MobiCom 2018, New Delhi, India, October 29 - November 02, 2018. ACM, pp 289–304
Wang W, Liu AX, Shahzad M, Ling K, Lu S (2015) Understanding and modeling of wifi signal based human activity recognition. In: Proceedings of the 21st annual international conference on mobile computing and networking, MobiCom ’15, pages 65–76, New York, NY, USA. ACM
Yang J, Zou H, Jiang H, Xie L (2018) Fine-grained adaptive location-independent activity recognition using commodity wifi. In: Proceedings 2018 IEEE wireless communications and networking conference, WCNC 2018, Barcelona, Spain, April 15-18, 2018. IEEE, pp 1–6
Thanh TN, Makihara Y, Nagahara H, Mukaigawa Y, Yagi Y (2014) The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognit 47 (1):228–237
Depatla S, Mostofi Y (2018) Crowd counting through walls using wifi. In: 2018 IEEE international conference on pervasive computing and communications, PerCom 2018, Athens, Greece, March 19-23, 2018. IEEE Computer Society, pp 1–10
Domenico SD, Sanctis MD, Cianca E, Bianchi G (2016) A trained-once crowd counting method using differential wifi channel state information. In: Lane ND, Zhou X, Kawsar F (eds) Proceedings of the 3rd International on Workshop on Physical Analytics, WPA@MobiSys 2015, Singapore, June 26, 2016. ACM, pp 37–42
Zhang X, Zhang L (2014) Real time crowd counting with human detection and human tracking. In: Loo CK, Yap KS, Wong KW, Jin ATB, Huang K (eds) Neural information processing - 21st international conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III, volume 8836 of Lecture Notes in Computer Science. Springer, pp 1–8
Marsden M, McGuinness K, Little S, O’Connor NE (2017) Resnetcrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification. In: 14th IEEE international conference on advanced video and signal based surveillance, AVSS 2017, Lecce, Italy, August 29 - September 1, 2017. IEEE Computer Society, pp 1–7
Wang Y, Wu K, Ni LM (2017) Wifall: Device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581– 594
Zhang D, Wang H, Wang Y, Ma J (2015) Anti-fall: A non-intrusive and real-time fall detector leveraging csi from commodity wifi devices. In: Geissbühler A, Demongeot J, Mokhtari M, Abdulrazak B, Aloulou H (eds) Inclusive smart cities and e-Health. Springer International Publishing, Cham, pp 181–193
Ali K, Liu AX, Wang W, Shahzad M (2015) Keystroke recognition using wifi signals. In: Fdida S, Pau G, Kasera SK, Zheng H (eds) Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, MobiCom 2015, Paris, France, September 7-11, 2015. ACM, pp 90–102
Ali K, Liu AX, Wang W, Shahzad M (2017) Recognizing keystrokes using wifi devices. IEEE J Sel Areas Commun 35(5):1175–1190
Chen Y, Zhu Y, Zhou H, Chen W, Zhang W (2018) Enhanced keystroke recognition based on moving distance of keystrokes through wifi. In: Au MH, Yiu S-M, Li J, Luo X, Wang C, Castiglione A, Kluczniak K (eds) Network and system security - 12th international conference, NSS 2018, Hong Kong, China, August 27-29, 2018, Proceedings, volume 11058 of Lecture Notes in Computer Science. Springer, pp 237–250
Wang B, Liu X, Baoguo Y u, Jia R, Gan X (2019) An improved wifi positioning method based on fingerprint clustering and signal weighted euclidean distance. Sensors 19(10):2300
Rahman MT, Han S, Tadayon N, Valaee S (2019) Ising model formulation of outlier rejection, with application in wifi based positioning. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2019, Brighton, United Kingdom, May 12-17, 2019. IEEE, pp 4405–4409
Xuan D u, Yang K, Zhou D (2018) Mapsense: Mitigating inconsistent wifi signals using signal patterns and pathway map for indoor positioning. IEEE Internet Things J 5(6):4652– 4662
Kotaru M, Katti S (2018) Position tracking for virtual reality using commodity wifi. In: Pradhan S, Saha SK (eds) Proceedings of the 10th on Wireless of the Students, by the Students, and for the Students Workshop, S3@MobiCom 2018, New Delhi, India, November 2, 2018. ACM, pp 15–17
Bisio I, Sciarrone A, Bedogni L, Bononi L (2018) Wifi meets barometer: Smartphone-based 3d indoor positioning method. In: 2018 IEEE International Conference on Communications, ICC 2018, Kansas City, MO, USA, May 20-24, 2018. IEEE, pp 1–6
Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 46(3):33:1–33:33
Chang W-C, Wu C-W, Tsai RY-C, Lin KC-J, Tseng Y-C (2018) Eye on you: Fusing gesture data from depth camera and inertial sensors for person identification. In: 2018 IEEE international conference on robotics and automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018. IEEE, pp 2021–2026
Wang Y, Song L, Zhaoquan G, Li D (2016) Intenct: Efficient multi-target counting and tracking by binary proximity sensors. In: 13th Annual IEEE international conference on sensing, communication, and networking, SECON 2016, London, United Kingdom, June 27-30, 2016. IEEE, pp 1–9
Ming X, Feng H, Qirong B u, Zhang J, Yang G, Zhang T (2019) Humanfi: Wifi-based human identification using recurrent neural network. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2019, Leicester, United Kingdom, August 19-23, 2019. IEEE, pp 640– 647
Zhang J, Tang Z, Li M, Fang D, Nurmi P, Wang Z (2018) Crosssense: Towards cross-site and large-scale wifi sensing. In: Shorey R, Murty R, Chen YJ, Jamieson K (eds) Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, MobiCom 2018, New Delhi, India, October 29 - November 02, 2018. ACM, pp 305–320
Changlai D u, Yuan X, Lou W, Thomas Hou Y (2018) Context-free fine-grained motion sensing using wifi. In: 15th Annual IEEE international conference on sensing, communication, and networking, SECON 2018, Hong Kong, China, June 11-13, 2018. IEEE, pp 199–207
Yu G, Zhan J, Ji Y, Li J, Ren F, Gao S (2017) Mosense: An rf-based motion detection system via off-the-shelf wifi devices. IEEE Internet Things J 4(6):2326–2341
Guo L, Wang L, Liu J, Zhou W (2016) A survey on motion detection using wifi signals. In: 12th international conference on mobile Ad-Hoc and sensor networks, MSN 2016, Hefei, China, December 16-18, 2016. IEEE Computer Society, pp 202–206
Zhao J, Liu L, Wei Z, Zhang C, Wang W, Fan Y (2019) R-DEHM: csi-based robust duration estimation of human motion with wifi. Sensors 19(6):1421
Arshad SA, Feng C, Liu Y, Yupeng H u, Ruiyun Y u, Zhou S, Li H (2017) Wi-chase: A wifi based human activity recognition system for sensorless environments. In: 18th IEEE international symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2017, Macau, China, June 12-15, 2017. IEEE, pp 1–6
Ma Y, Zhou G, Wang S, Zhao H, Jung W (2018) Signfi: Sign language recognition using wifi. IMWUT 2(1):23:1–23:21
Pan X, Jiang T, Li X, Ding X, Wang Y, Li Y (2019) Dynamic hand gesture detection and recognition with wifi signal based on 1d-cnn. In: 17th IEEE international conference on communications workshops, ICC Workshops 2019, Shanghai, China, May 20-24, 2019. IEEE, pp 1–6
Kong H, Li L, Jiadi Y, Chen Y, Kong L, Li M (2019) Fingerpass: Finger gesture-based continuous user authentication for smart homes using commodity wifi. In: Proceedings of the twentieth ACM international symposium on mobile Ad Hoc networking and computing, Mobihoc 2019, Catania, Italy, July 2-5, 2019. ACM, pp 201–210
Qirong B, Yang G, Feng J, Ming X (2018) Wi-fi based gesture recognition using deep transfer learning. In: Wang G, Han Q, Bhuiyan MZA, Ma X, Loulergue F, Li P, Roveri M, Chen L (eds) 2018 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2018, Guangzhou, China, October 8-12, 2018. IEEE, pp 590–595
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, pp 815–823
Zou H, Yang J, Zhou Y, Spanos CJ (2018) Joint adversarial domain adaptation for resilient wifi-enabled device-free gesture recognition. In: Wani MA, Kantardzic MM, Mouchaweh MS, Gama J, Lughofer E (eds) 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, December 17-20, 2018. IEEE, pp 202–207
Hristov HD (2000) Fresnel zones in wireless links zone plate lenses and antennas
Liu Z, Giannakis GB, Zhou S, Muquet B (2001) Space-time coding for broadband wireless communications. Wirel Commun Mob Comput 1(1):35–53
Maaten LJPVD, Hinton GE (2008) Visualizing high-dimensional data using t-sne. J Mach Learn Res 9:2579–2605
Funding
This work is supported by Public cultural service equipment research and development and application demonstrations (2020YFC1523300) and Hierarchical Pedigree Analysis with Missing and Unapplicable Data (2019JM-494)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bu, Q., Ming, X., Hu, J. et al. TransferSense: towards environment independent and one-shot wifi sensing. Pers Ubiquit Comput 26, 555–573 (2022). https://doi.org/10.1007/s00779-020-01480-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-020-01480-6