Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi

FEATURE EXTRACTION IN NOISE-DIVERSE ENVIRONMENTS FOR HUMAN ACTIVITIES RECOGNITION USING WI-FI SHEHERYAR ALI ARSHAD, Ph.D. The University of Texas at Arlington, 2019 Supervising Professor: Yonghe Liu With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract quality features in different noisy environments for achieving accurate human activity recognition (HAR). In first part of the dissertation, we present WiChase framework which extracts anomalous CSI data and processes it to sense and classify surrounding human activities. Contrary to some other works that use fixed CSI window, we automate the data extraction and activity labeling based on the variance of multipleinput and multiple-output (MIMO) subcarriers. Moreover, we recognize human activities using CSI amplitude together with calibrated phase and propose the integration of these

[1]  Dun Cao,et al.  Wi-Multi: A Three-Phase System for Multiple Human Activity Recognition With Commercial WiFi Devices , 2019, IEEE Internet of Things Journal.

[2]  S. Z. Gürbüz,et al.  Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Shuangquan Wang,et al.  SignFi , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[4]  Belkacem Chikhaoui,et al.  A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors , 2018, MLDM.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Heng Li,et al.  Wi-chase: A WiFi based human activity recognition system for sensorless environments , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[7]  Qiang Yang,et al.  Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..

[8]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[9]  Ruiyun Yu,et al.  Leveraging Transfer Learning in Multiple Human Activity Recognition Using WiFi Signal , 2019, 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[10]  Philip S. Yu,et al.  Stratified Transfer Learning for Cross-domain Activity Recognition , 2017, 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[11]  Fadel Adib,et al.  Emotion recognition using wireless signals , 2016, MobiCom.

[12]  Jie Yang,et al.  Fine-grained gesture recognition using WiFi , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[13]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[14]  D. Mohr,et al.  Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. , 2017, Annual review of clinical psychology.

[15]  Yonghe Liu,et al.  MAIS: Multiple Activity Identification System Using Channel State Information of WiFi Signals , 2017, WASA.

[16]  Jiacheng Shang,et al.  A Robust Sign Language Recognition System with Multiple Wi-Fi Devices , 2017, MobiArch@SIGCOMM.

[17]  Cecilia Mascolo,et al.  A Study of Bluetooth Low Energy performance for human proximity detection in the workplace , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[18]  M Cameron,et al.  World Report on Road Traffic Injury Prevention. , 2004 .

[19]  Wei Wang,et al.  Keystroke Recognition Using WiFi Signals , 2015, MobiCom.

[20]  Jue Wang,et al.  Dude, where's my card?: RFID positioning that works with multipath and non-line of sight , 2013, SIGCOMM.

[21]  Stephan Sigg,et al.  RFexpress! - RF emotion recognition in the wild , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[22]  Guoliang Xing,et al.  PBN: towards practical activity recognition using smartphone-based body sensor networks , 2011, SenSys.

[23]  Lionel M. Ni,et al.  A Survey on Wireless Indoor Localization from the Device Perspective , 2016, ACM Comput. Surv..

[24]  Shiwen Mao,et al.  Advanced Wireless LAN Technologies: IEEE 802.11AC and Beyond , 2015, GETMBL.

[25]  Kim-Kwang Raymond Choo,et al.  Cyber-physical systems information gathering: A smart home case study , 2018, Comput. Networks.

[26]  Nan Yu,et al.  QGesture , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[27]  Dieter Fox,et al.  Fine-grained kitchen activity recognition using RGB-D , 2012, UbiComp.

[28]  Hao Jiang,et al.  DeepSense: Device-Free Human Activity Recognition via Autoencoder Long-Term Recurrent Convolutional Network , 2018, 2018 IEEE International Conference on Communications (ICC).

[29]  Sheng Tan,et al.  WiFinger: leveraging commodity WiFi for fine-grained finger gesture recognition , 2016, MobiHoc.

[30]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[31]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[32]  Liang Liu,et al.  Automatic fall detection based on Doppler radar motion signature , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[33]  Wei Wang,et al.  Gait recognition using wifi signals , 2016, UbiComp.

[34]  Khaled A. Harras,et al.  WiGest: A ubiquitous WiFi-based gesture recognition system , 2014, 2015 IEEE Conference on Computer Communications (INFOCOM).

[35]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Jie Wang,et al.  CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features , 2017, IEEE Transactions on Vehicular Technology.

[37]  Xuyu Wang,et al.  RF Sensing for Internet of Things: When Machine Learning Meets Channel State Information , 2018 .

[38]  Biao Song,et al.  Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection , 2015, Sensors.

[39]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[40]  Sang Hyuk Son,et al.  WiTraffic: Low-Cost and Non-Intrusive Traffic Monitoring System Using WiFi , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[41]  Rob Miller,et al.  3D Tracking via Body Radio Reflections , 2014, NSDI.

[42]  Sang Hyuk Son,et al.  WiTraffic: Non-intrusive Vehicle Classification Using WiFi: Poster Abstract , 2016, SenSys.

[43]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[44]  Masashi Sugiyama,et al.  Sequential change‐point detection based on direct density‐ratio estimation , 2012, Stat. Anal. Data Min..

[45]  Dirk Pesch,et al.  Channel State Information Based Human Presence Detection using Non-linear Techniques , 2016, BuildSys@SenSys.

[46]  Yu Hu,et al.  Body-Earth Mover’s Distance: A Matching-Based Approach for Sleep Posture Recognition , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[47]  Chenglin Miao,et al.  Towards Environment Independent Device Free Human Activity Recognition , 2018, MobiCom.

[48]  Fadel Adib,et al.  Multi-Person Localization via RF Body Reflections , 2015, NSDI.

[49]  Ian Oppermann,et al.  UWB theory and applications , 2004 .

[50]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[51]  Jürgen Schmidhuber,et al.  LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.

[52]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

[53]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.

[54]  Syed Aziz Shah,et al.  Design of Software Defined Radios Based Platform for Activity Recognition , 2019, IEEE Access.

[55]  Peter E.D. Love,et al.  A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory , 2018 .

[56]  Parth H. Pathak,et al.  Your AP knows how you move: fine-grained device motion recognition through WiFi , 2014, HotWireless@MobiCom.

[57]  Mohan M. Trivedi,et al.  Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[58]  Kimiaki Shirahama,et al.  Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors , 2018, Sensors.

[59]  Jie He,et al.  WiDriver: Driver Activity Recognition System Based on WiFi CSI , 2018, Int. J. Wirel. Inf. Networks.

[60]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[61]  Ruiyun Yu,et al.  Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network , 2018, 2018 IEEE International Conference on Communications (ICC).

[62]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[63]  Chao Cai,et al.  Smart Home Based on WiFi Sensing: A Survey , 2018, IEEE Access.

[64]  Souad El Bernoussi,et al.  Mining human activity using dimensionality reduction and pattern recognition , 2016 .

[65]  Nital H. Mistry,et al.  RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[66]  Jiangchuan Liu,et al.  On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach , 2019, IEEE Internet of Things Journal.

[67]  Kaishun Wu,et al.  We Can Hear You with Wi-Fi! , 2014, IEEE Transactions on Mobile Computing.

[68]  Gengfa Fang,et al.  Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[69]  Yonghe Liu,et al.  SafeDrive-Fi: A Multimodal and Device Free Dangerous Driving Recognition System Using WiFi , 2018, 2018 IEEE International Conference on Communications (ICC).

[70]  D. Dobkin.  The RF in RFID : UHF RFID in Practice Ed. 2 , 2012 .

[71]  Saandeep Depatla,et al.  Occupancy Estimation Using Only WiFi Power Measurements , 2015, IEEE Journal on Selected Areas in Communications.

[72]  Shaojie Tang,et al.  Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals , 2014, 2014 IEEE Real-Time Systems Symposium.

[73]  Haibin Cai,et al.  An adaptive wireless passive human detection via fine-grained physical layer information , 2016, Ad Hoc Networks.

[74]  Koji Yatani,et al.  BodyScope: a wearable acoustic sensor for activity recognition , 2012, UbiComp.

[75]  Parth H. Pathak,et al.  WiWho: WiFi-Based Person Identification in Smart Spaces , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[76]  Wei Cui,et al.  WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM , 2019, IEEE Transactions on Mobile Computing.

[77]  Yu-Wei Su,et al.  A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[78]  Daniel McDuff,et al.  AutoEmotive: bringing empathy to the driving experience to manage stress , 2014, DIS Companion '14.

[79]  Wei Wang,et al.  Device-Free Human Activity Recognition Using Commercial WiFi Devices , 2017, IEEE Journal on Selected Areas in Communications.

[80]  Mauro De Sanctis,et al.  Exploring Training Options for RF Sensing Using CSI , 2018, IEEE Communications Magazine.

[81]  Tong Xin,et al.  FreeSense: Indoor Human Identification with Wi-Fi Signals , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[82]  Lionel M. Ni,et al.  An RF-Based System for Tracking Transceiver-Free Objects , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[83]  J. Andrew Zhang,et al.  Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[84]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[85]  Ohad Shamir,et al.  Better Mini-Batch Algorithms via Accelerated Gradient Methods , 2011, NIPS.

[86]  Yongdae Kim,et al.  A machine learning framework for network anomaly detection using SVM and GA , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[87]  Kaveh Pahlavan,et al.  Enlighten Wearable Physiological Monitoring Systems: On-Body RF Characteristics Based Human Motion Classification Using a Support Vector Machine , 2016, IEEE Transactions on Mobile Computing.