{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:50:12Z","timestamp":1760244612982,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China","award":["62002287","2022SF-037"],"award-info":[{"award-number":["62002287","2022SF-037"]}]},{"name":"Key research and development plan of Shaanxi Province\u2014General Projects","award":["62002287","2022SF-037"],"award-info":[{"award-number":["62002287","2022SF-037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the continuous improvement of people\u2019s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.<\/jats:p>","DOI":"10.3390\/e24111700","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T03:02:16Z","timestamp":1669086136000},"page":"1700","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-170X","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"},{"name":"School of Information Science and Technology, NorthWest University, Xi\u2019an 701127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0870-454X","authenticated-orcid":false,"given":"Zhongmin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruohan","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 13). The Health Diary. Available online: https:\/\/thehealthdiary.com\/6-healthy-drinks-to-quench-your-thirst\/."},{"key":"ref_2","unstructured":"(2022, November 13). Biotech Advanced. Available online: https:\/\/www.adv-bio.com\/quenching-thirst-and-boosting-health-with-functional-drinks\/."},{"key":"ref_3","unstructured":"(2022, November 13). Biotech Advanced. Available online: https:\/\/www.adv-bio.com\/transforming-energy-drinks-the-demand-for-natural-compounds\/."},{"key":"ref_4","unstructured":"(2022, November 13). Dealy Meal. Available online: https:\/\/www.thedailymeal.com\/drink\/beer-every-day-benefits-gallery."},{"key":"ref_5","unstructured":"(2022, November 13). WebMD. Available online: https:\/\/www.webmd.com\/osteoporosis\/news\/20041004\/beer-wine-may-make-bones-stronger."},{"key":"ref_6","unstructured":"Tschinkel, A. (2022, November 13). 2019. Available online: https:\/\/www.insider.com\/drinking-health-benefits-heart-nutrition-2019-10."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/BF01333881","article-title":"Torsion pendula with electromagnetic drive and detection system for measuring the complex shear modulus of liquids in the frequency range 80\u20132500 Hz","volume":"23","author":"Blom","year":"1984","journal-title":"Rheol. Acta"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/697062","article-title":"Characterizing Vibrating Cantilevers for Liquid Viscosity and Density Sensing","volume":"2008","author":"Riesch","year":"2008","journal-title":"J. Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1063\/1.1287606","article-title":"Simultaneous liquid viscosity and density determination with piezoelectric unimorph cantilevers","volume":"89","author":"Shih","year":"2001","journal-title":"J. Appl. Phys."},{"key":"ref_10","unstructured":"Note, A. (2005). Agilent Basics of Measuring the Dielectric Properties of Materials, Keysight Technologies."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ha, U., Ma, Y., Zhong, Z., Hsu, T.M., and Adib, F. (2018, January 15\u201316). Learning Food Quality and Safety from Wireless Stickers. Proceedings of the 17th ACM Workshop, Redmond, WA, USA.","DOI":"10.1145\/3286062.3286078"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1109\/TMC.2019.2946072","article-title":"Simultaneous Material Identification and Target Imaging with Commodity RFID Devices","volume":"20","author":"Wang","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_13","unstructured":"Xie, B., Xiong, J., Chen, X., Chai, E., Li, L., Tang, Z., and Fang, D. (2019, January 10\u201313). Tagtag: Material Sensing with Commodity RFID. Proceedings of the SenSys \u201919: The 17th Conference on Embedded Networked Sensor Systems, New York, NY, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dhekne, A., Gowda, M., Zhao, Y., Hassanieh, H., and Choudhury, R.R. (2018, January 10\u201315). LiquID: A Wireless Liquid IDentifier. Proceedings of the 16th Annual International Conference, Munich, Germany.","DOI":"10.1145\/3210240.3210345"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gu, Y., Zhu, Y., Li, J., and Ji, Y. (2021, January 7\u201311). WiMate: Location-independent Material Identification Based on Commercial WiFi Devices. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685094"},{"key":"ref_16","unstructured":"(2022, September 27). Imaherb Biotech. Available online: https:\/\/www.imaherb.com\/functional-drinks\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1109\/LAWP.2020.2977995","article-title":"Static Hand Gesture Recognition With Electromagnetic Scattered Field via Complex Attention Convolutional Neural Network","volume":"19","author":"Tan","year":"2020","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gupta, V., Basak, B., and Roy, B. (2020, January 23\u201320). A Fault-Detecting and Motion-Sensing Wireless Light Controller for LED Lighting System. Proceedings of the 2020 IEEE Calcutta Conference (CALCON), Duhok, Iraq.","DOI":"10.1109\/CALCON49167.2020.9106427"},{"key":"ref_19","first-page":"2620","article-title":"UltraGesture: Fine-Grained Gesture Sensing and Recognition","volume":"21","author":"Ling","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103543","DOI":"10.1016\/j.dsp.2022.103543","article-title":"Enhanced compressed sensing autofocus for high-resolution airborne synthetic aperture radar","volume":"127","author":"Beheshti","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_21","first-page":"1","article-title":"Indoor Regional People Counting Method Based on Bi-Motion-Model-Framework Using UWB Radar","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_22","first-page":"1","article-title":"Multipath Ghost Recognition for Indoor MIMO Radar","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, X., Ding, Y., and Zhang, L. (2021). Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion. Remote. Sens., 13.","DOI":"10.3390\/rs13183791"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2543581.2543592","article-title":"From RSSI to CSI: Indoor Localization via Channel Response","volume":"46","author":"Zheng","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, M., Yan, M., Liu, J., Zhu, H., Liang, X., Yao, L., and Na, R. (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_26","doi-asserted-by":"crossref","first-page":"205","DOI":"10.23919\/JCC.2021.03.016","article-title":"WiFi CSI gesture recognition based on parallel LSTM-FCN deep space-time neural network","volume":"18","author":"Tang","year":"2021","journal-title":"China Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s40747-021-00593-6","article-title":"Intelligent user identity authentication in vehicle security system based on wireless signals","volume":"8","author":"Zhang","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_28","unstructured":"Jiang, Z. (2022, September 27). PicoScenes. Available online: https:\/\/ps.zpj.io\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4476","DOI":"10.1109\/JIOT.2021.3104666","article-title":"Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform","volume":"9","author":"Jiang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_30","unstructured":"Jiang, Z. (2022, September 27). PicoScenes MATLAB Toolbox. Available online: https:\/\/ps.zpj.io\/matlab.html."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, W., Liu, A.X., Shahzad, M., Ling, K., and Lu, S. (2015, January 7\u201311). Understanding and Modeling of WiFi Signal Based Human Activity Recognition. Proceedings of the MobiCom \u201915 21st Annual International Conference on Mobile Computing and Networking, New York, NY, USA.","DOI":"10.1145\/2789168.2790093"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TWC.2021.3098526","article-title":"On CSI and Passive Wi-Fi Radar for Opportunistic Physical Activity Recognition","volume":"21","author":"Li","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, M., and Cao, Z. (2022, January 3\u20137). WiHF: Gesture and User Recognition With WiFi. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, New York, NY, USA. MobiCom \u201922.","DOI":"10.1109\/TMC.2020.3009561"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Venkatnarayan, R.H., Page, G., and Shahzad, M. (2018, January 10\u201315). Multi-User Gesture Recognition Using WiFi. Proceedings of the MobiSys \u201918 16th Annual International Conference on Mobile Systems, Applications, and Services, New York, NY, USA.","DOI":"10.1145\/3210240.3210335"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TMC.2016.2557792","article-title":"WiFall: Device-Free Fall Detection by Wireless Networks","volume":"16","author":"Han","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Pathak, P.H., and Mohapatra, P. (2015, January 16). Analyzing Shopper\u2019s Behavior through WiFi Signals. Proceedings of the 2nd Workshop on Workshop on Physical Analytics, New York, NY, USA. WPA \u201915.","DOI":"10.1145\/2753497.2753508"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.eswa.2009.06.060","article-title":"A multidimensional hybrid intelligent method for gear fault diagnosis","volume":"37","author":"Lei","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Pathak, P.H., and Mohapatra, P. (2016, January 111\u201314). WiWho: WiFi-Based Person Identification in Smart Spaces. Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks, Vienna, Austria. IPSN \u201916.","DOI":"10.1109\/IPSN.2016.7460727"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Pathak, P.H., and Mohapatra, P. (2014, January 11). Your AP Knows How You Move: Fine-Grained Device Motion Recognition through WiFi. Proceedings of the 1st ACM Workshop on Hot Topics in Wireless, Maui, HI, USA. HotWireless \u201914.","DOI":"10.1145\/2643614.2643620"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chang, J.-b., Li, T.-f., and Li, P.-f. (2010, January 9\u201310). The selection of time domain characteristic parameters of rotating machinery fault diagnosis. Proceedings of the 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM), Harbin, China. ICLSIM \u201910.","DOI":"10.1109\/ICLSIM.2010.5461346"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wei, B., Hu, W., and Kanhere, S.S. (2016, January 26\u201328). WiFi-ID: Human Identification Using WiFi Signal. Proceedings of the 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS), Washington, DC, USA.","DOI":"10.1109\/DCOSS.2016.30"},{"key":"ref_42","unstructured":"Bodarw\u00e9, K.-A., Noack, J., and Philipp, J.-J. (December, January 30). Emotion-based music recommendation using supervised learning. Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM \u201915), Linz, Austria."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dai, Y., Huang, X., and Chen, Z. (2021, January 15\u201317). Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals. Proceedings of the 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shanghai, China.","DOI":"10.1109\/AINIT54228.2021.00123"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chai, H., Huang, H., Yan, Z., Zhang, X., Li, Y., Gan, P., and Huang, Y. (2018, January 28\u201331). Multi-Threshold Wavelet Packet-Based Method to Attenuate Noise from Seismic Signal. Proceedings of the the 7th International Conference on Informatics, Environment, Energy and Applications, Beijing, China. IEEA \u201918.","DOI":"10.1145\/3208854.3208876"},{"key":"ref_45","unstructured":"Suresh, S., and Yeow, R.C.-H. (2014, January 20\u201322). ADetection of anxiety through analysis of occipital EEG. Proceedings of the International Convention on Rehabilitation Engineering & Assistive Technology (i-CREATe 2014). Midview, Singapore."},{"key":"ref_46","unstructured":"Nakayama, M., and Katsukura, M. (, January January). Assessing Usability for Input Operation Using Frequency Components of Eye-Movements. Proceedings of the Tenth Australasian Conference on User Interfaces, Wellington, New Zealand."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11667","DOI":"10.1109\/JIOT.2020.2999210","article-title":"WiEps: Measurement of Dielectric Property With Commodity WiFi Device\u2014An Application to Ethanol\/Water Mixture","volume":"7","author":"Song","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_48","first-page":"1","article-title":"FG-LiquID: A Contact-Less Fine-Grained Liquid Identifier by Pushing the Limits of Millimeter-Wave Sensing","volume":"5","author":"Liang","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_49","unstructured":"Pei, W., Guo, B., Tong, X., Zhu, W., and Yu, Z. (2017, January 12\u201315). TinySense: Multi-user respiration detection using Wi-Fi CSI signals. Proceedings of the IEEE International Conference on E-health Networking, Dalian, China."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dai, Q., Huang, Y., Wang, L., Ruby, R., and Wu, K. (2018, January 11\u201313). mm- Humidity: Fine-Grained Humidity Sensing with Millimeter Wave Signals. Proceedings of the 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), Singapore.","DOI":"10.1109\/PADSW.2018.8644907"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"110622","DOI":"10.1016\/j.measurement.2021.110622","article-title":"A new tool wear condition monitoring method based on deep learning under small samples","volume":"189","author":"Zhou","year":"2022","journal-title":"Measurement"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111997","DOI":"10.1016\/j.measurement.2022.111997","article-title":"A novel entropy-based sparsity measure for prognosis of bearing defects and development of a sparsogram to select sensitive filtering band of an axial piston pump","volume":"203","author":"Zhou","year":"2022","journal-title":"Measurement"},{"key":"ref_53","unstructured":"Ha, U., Leng, J., Khaddaj, A., and Adib, F. (2020, January 25\u201327). Food and Liquid Sensing in Practical Environments Using RFIDs. Proceedings of the NSDI\u201920 17th Usenix Conference on Networked Systems Design and Implementation, Santa Clara, CA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Levitas, B., Matuzas, J., Viswanath, G., Basalingappa, V., and Venkoparao, V. (2011, January 14\u201316). UWB based oil quality detection. Proceedings of the 2011 IEEE International Conference on Ultra-Wideband (ICUWB), Bologna, Italy.","DOI":"10.1109\/ICUWB.2011.6058832"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2018.2852263","article-title":"One-Shot Learning for Robust Material Classification Using Millimeter-Wave Radar System","volume":"2","author":"Santra","year":"2018","journal-title":"IEEE Sensors Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3264936","article-title":"Al-Light: An Alcohol-Sensing Smart Ice Cube","volume":"2","author":"Matsui","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Rahman, T., Adams, A.T., Schein, P., Jain, A., Erickson, D., and Choudhury, T. (2016, January 14\u201316). Nutrilyzer: A Mobile System for Characterizing Liquid Food with Photoacoustic Effect. Proceedings of the SenSys \u201916, 14th ACM Conference on Embedded Network Sensor Systems CD-ROM, Stanford, CA, USA.","DOI":"10.1145\/2994551.2994572"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/3481038","article-title":"Liquid Testing with Your Smartphone","volume":"64","author":"Yue","year":"2021","journal-title":"Commun. 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