{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:08Z","timestamp":1760240828224,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T00:00:00Z","timestamp":1569369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872385, 61673396, 61772551, 61801517"],"award-info":[{"award-number":["61872385, 61673396, 61772551, 61801517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["18CX02133A, 18CX02134A,18CX02137A"],"award-info":[{"award-number":["18CX02133A, 18CX02134A,18CX02137A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Small-scale motion recognition has received wide attention recently with the development of environmental perception technology based on WiFi, and some state-of-the-art techniques have emerged. The wide application of small-scale motion recognition has aroused people\u2019s concern. Handwritten letter is a kind of small scale motion, and the recognition for small-scale motion based on WiFi has two characteristics. Small-scale action has little impact on WiFi signals changes in the environment. The writing trajectories of certain uppercase letters are the same as the writing trajectories of their corresponding lowercase letters, but they are different in size. These characteristics bring challenges to small-scale motion recognition. The system for recognizing small-scale motion in multiple classes with high accuracy urgently needs to be studied. Therefore, we propose MCSM-Wri, a device-free handwritten letter recognition system using WiFi, which leverages channel state information (CSI) values extracted from WiFi packets to recognize handwritten letters, including uppercase letters and lowercase letters. Firstly, we conducted data preproccessing to provide more abundant information for recognition. Secondly, we proposed a ten-layers convolutional neural network (CNN) to solve the problem of the poor recognition due to small impact of small-scale actions on environmental changes, and it also can solve the problem of identifying actions with the same trajectory and different sizes by virtue of its multi-scale characteristics. Finally, we collected 6240 instances for 52 kinds of handwritten letters from 6 volunteers. There are 3120 instances from the lab and 3120 instances are from the utility room. Using 10-fold cross-validation, the accuracy of MCSM-Wri is 95.31%, 96.68%, and 97.70% for the lab, the utility room, and the lab+utility room, respectively. Compared with Wi-Wri and SignFi, we increased the accuracy from 8.96% to 18.13% for recognizing handwritten letters.<\/jats:p>","DOI":"10.3390\/s19194162","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T03:06:51Z","timestamp":1569467211000},"page":"4162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MCSM-Wri: A Small-Scale Motion Recognition Method Using WiFi Based on Multi-Scale Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7579-5678","authenticated-orcid":false,"given":"Shiyuan","family":"Ma","sequence":"first","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Tingpei","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Shibao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Junwei","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Tiantian","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Jianhang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer &amp; Communication Engineering, China University of Petroleum, Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,25]]},"reference":[{"key":"ref_1","unstructured":"(2019, September 25). Leap Motion. Available online: https:\/\/www.leapmotion.com."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chuan, C.H., Regina, E., and Guardino, C. (2014, January 3\u20136). American Sign Language Recognition Using Leap Motion Sensor. Proceedings of the International Conference on Machine Learning & Applications, Detroit, MI, USA.","DOI":"10.1109\/ICMLA.2014.110"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fang, B., Co, J., and Zhang, M. (2018, January 4\u20137). DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, Shenzhen, China.","DOI":"10.1145\/3131672.3131693"},{"key":"ref_4","first-page":"1","article-title":"Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect","volume":"6","author":"Chao","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H., and Presti, P. (2011, January 14\u201318). American sign language recognition with the kinect. Proceedings of the International Conference on Multimodal Interfaces, Alicante, Spain.","DOI":"10.1145\/2070481.2070532"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schick, A., Morlock, D., Amma, C., Schultz, T., and Stiefelhagen, R. (2012, January 22\u201326). Vision-based handwriting recognition for unrestricted text input in mid-air. Proceedings of the ACM International Conference on Multimodal Interaction, Santa Monica, CA, USA.","DOI":"10.1145\/2388676.2388719"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1142\/S0218126607003757","article-title":"A novel vision-based finger-writing character recognition system","volume":"16","author":"Jin","year":"2007","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_8","unstructured":"Joshi, K., Bharadia, D., Kotaru, M., and Katti, S. (, January March). WiDeo: Fine-grained device-free motion tracing using RF backscatter. Proceedings of the Usenix Conference on Networked Systems Design & Implementation, Oakland, CA, 2015."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/MMUL.2013.50","article-title":"A New Writing Experience: Finger Writing in the Air Using a Kinect Sensor","volume":"20","author":"Xin","year":"2013","journal-title":"IEEE Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Amma, C., Gehrig, D., and Schultz, T. (2010, January 2\u20133). Airwriting recognition using wearable motion sensors. Proceedings of the Augmented Human International Conference, Meg\u00e8ve, France.","DOI":"10.1145\/1785455.1785465"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Amma, C., Georgi, M., and Schultz, T. (2012, January 18\u201322). Airwriting: Hands-Free Mobile Text Input by Spotting and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors. Proceedings of the International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.21"},{"key":"ref_12","unstructured":"Agrawal, S., Constandache, I., Gaonkar, S., Choudhury, R.R., Caves, K., and Deruyter, F. (July, January 28). Using mobile phones to write in air. Proceedings of the International Conference on Mobile Systems, Bethesda, MD, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Ni, L.M. (2009, January 9\u201313). Dynamic clustering for tracking multiple transceiver-free objects. Proceedings of the IEEE International Conference on Pervasive Computing & Communications, Galveston, TX, USA.","DOI":"10.1109\/PERCOM.2009.4912777"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.comcom.2016.01.010","article-title":"A few bits are enough: Energy efficient device-free localization","volume":"83","author":"Pan","year":"2016","journal-title":"Comput. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.comcom.2015.01.015","article-title":"Minimizing receivers under link coverage model for device-free surveillance","volume":"63","author":"Pan","year":"2015","journal-title":"Comput. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TMC.2013.28","article-title":"RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals","volume":"13","author":"Sigg","year":"2014","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_17","unstructured":"Abdelnasser, H., Youssef, M., and Harras, K.A. (May, January 26). WiGest: A ubiquitous WiFi-based gesture recognition system. Proceedings of the IEEE Conference on Computer Communications, Kowloon, Hong Kong."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Arshad, S., Feng, C., Liu, Y., Hu, Y., Yu, R., Zhou, S., and Li, H. (2017, January 12\u201315). Wi-chase: A WiFi based human activity recognition system for sensorless environments. Proceedings of the IEEE International Symposium on A World of Wireless, Macau, China.","DOI":"10.1109\/WoWMoM.2017.7974315"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fang, B., Lane, N.D., Zhang, M., and Kawsar, F. (2016, January 11\u201314). HeadScan: A Wearable System for Radio-Based Sensing of Head and Mouth-Related Activities. Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Vienna, Austria.","DOI":"10.1109\/IPSN.2016.7460677"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, L., Lei, W., Jialin, L., Wei, Z., and Bingxian, L. (2018). HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data. Wirel. Commun. Mob. Comput., 2018.","DOI":"10.1155\/2018\/6163475"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, M., Yan, M., Liu, J., Zhu, H., and Liang, X. (2016, January 24\u201328). When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals. Proceedings of the ACM Sigsac Conference, Vienna, Austria.","DOI":"10.1145\/2976749.2978397"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qian, K., Wu, C., Zhou, Z., Zheng, Y., Yang, Z., and Liu, Y. (2017, January 6\u201311). Inferring Motion Direction using Commodity Wi-Fi for Interactive Exergames. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA.","DOI":"10.1145\/3025453.3025678"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shang, J., and Wu, J. (2017, January 25). A Robust Sign Language Recognition System with Multiple Wi-Fi Devices. Proceedings of the Workshop on Mobility in the Evolving Internet Architecture, Los Angeles, CA, USA.","DOI":"10.1145\/3097620.3097624"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tan, S., and Yang, J. (2016, January 5\u20138). WiFinger: Leveraging commodity WiFi for fine-grained finger gesture recognition. Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking & Computing, Paderborn, Germany.","DOI":"10.1145\/2942358.2942393"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27311","DOI":"10.1109\/ACCESS.2017.2776527","article-title":"ClickLeak: Keystroke leaks through multimodal sensors in cyber-physical social networks","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Virmani, A., and Shahzad, M. (2017, January 19\u201323). Position and Orientation Agnostic Gesture Recognition Using WiFi. Proceedings of the International Conference on Mobile Systems, Niagara Falls, NY, USA.","DOI":"10.1145\/3081333.3081340"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cao, X., Bing, C., and Zhao, Y. (2017, January 12\u201315). Wi-Wri: Fine-Grained Writing Recognition Using Wi-Fi Signals. Proceedings of the Trustcom\/bigdatase\/ispa, Guangzhou, China.","DOI":"10.1109\/TrustCom.2016.0216"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TMC.2018.2831709","article-title":"Writing in the Air with WiFi Signals for Virtual Reality Devices","volume":"18","author":"Fu","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/3191755","article-title":"Signfi: Sign language recognition using wifi","volume":"2","author":"Ma","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ranzato, M.A., Huang, F.J., Boureau, Y.L., and Lecun, Y. (2007, January 17\u201322). Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383157"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 11\u201312). Going Deeper with Convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pu, Q., Gupta, S., Gollakota, S., and Patel, S. (2013, January 12\u201316). Whole-home gesture recognition using wireless signals. Proceedings of the ACM Sigcomm Conference on Sigcomm, Hong Kong, China.","DOI":"10.1145\/2486001.2491687"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/2534169.2486039","article-title":"See through walls with WiFi!","volume":"43","author":"Adib","year":"2013","journal-title":"ACM Sigcomm Comput. Commun. Rev."},{"key":"ref_34","unstructured":"Adib, F., Kabelac, Z., Katabi, D., and Miller, R.C. (2014, January 2\u20134). 3D Tracking via Body Radio Reflections. Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI, Seattle, WA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1109\/TMC.2016.2517630","article-title":"We can hear you with Wi-Fi!","volume":"15","author":"Wang","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/JSAC.2017.2679658","article-title":"Device-free Human Activity Recognition Using Commercial WiFi Devices","volume":"35","author":"Wei","year":"2017","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, L., Sen, S., Koutsonikolas, D., and Kim, K.H. (2015, January 7\u201311). Widraw: Enabling hands-free drawing in the air on commodity wifi devices. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790129"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/1925861.1925870","article-title":"Tool release:gathering 802.11n traces with channel state information","volume":"41","author":"Halperin","year":"2011","journal-title":"ACM Sigcomm Comput. Commun. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/TMC.2018.2860991","article-title":"Precise Power Delay Profiling with Commodity WiFi","volume":"18","author":"Xie","year":"2015","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ali, K., Liu, A.X., Wang, W., and Shahzad, M. (2015, January 7\u201311). Keystroke recognition using wifi signals. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790109"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Haykin, S., and Kosko, B. (2009). GradientBased Learning Applied to Document Recognition. Intelligent Signal Processing, Wiley-IEEE Press.","DOI":"10.1109\/9780470544976.ch9"},{"key":"ref_42","first-page":"396","article-title":"Handwritten digit recognition with a back-propagation network","volume":"2","author":"Cun","year":"1990","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","unstructured":"Le, Y., and Bengio, C.Y. (1994, January 9\u201313). Word-Level Training of a Handritten Word Recognizer Based on Convolutional Neural Networks. Proceedings of the International Conference on Pattern Recognition, Vol 2-conference B: Computer Vision & Image Processing, Jerusalem, Israel."},{"key":"ref_44","unstructured":"Hecht-Nielsen (2002, January 12\u201317). Theory of the backpropagation neural network. Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/JIOT.2016.2558659","article-title":"CSI Phase Fingerprinting for Indoor Localization with a Deep Learning Approach","volume":"3","author":"Wang","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_46","unstructured":"(2002). IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std."},{"key":"ref_47","unstructured":"Yang, J., Kai, Y., Gong, Y., and Huang, T.S. (2009, January 20\u201325). Linear spatial pyramid matching using sparse coding for image classification. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_48","unstructured":"Boureau, Y.L., Ponce, J., and LeCun, Y. (2010, January 21\u201324). A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel."},{"key":"ref_49","unstructured":"Tao, W., Wu, D.J., Coates, A., and Ng, A.Y. (2012, January 11\u201315). End-to-End Text Recognition with Convolutional Neural Networks. Proceedings of the International Conference on Pattern Recognition, sukuba Science City, Japan."},{"key":"ref_50","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:24:19Z","timestamp":1760189059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,25]]},"references-count":50,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194162"],"URL":"https:\/\/doi.org\/10.3390\/s19194162","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,25]]}}}