{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T04:22:54Z","timestamp":1769919774645,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T00:00:00Z","timestamp":1588723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000360","name":"Scottish Funding Council","doi-asserted-by":"publisher","award":["EPSRC DTG EP\/N509668\/1 Eng"],"award-info":[{"award-number":["EPSRC DTG EP\/N509668\/1 Eng"]}],"id":[{"id":"10.13039\/501100000360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person\u2019s body. However, putting devices on a person\u2019s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.<\/jats:p>","DOI":"10.3390\/s20092653","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T03:10:38Z","timestamp":1588821038000},"page":"2653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":144,"title":["An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare"],"prefix":"10.3390","volume":"20","author":[{"given":"William","family":"Taylor","sequence":"first","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-1121","authenticated-orcid":false,"given":"Syed Aziz","family":"Shah","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"},{"name":"Centre of Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-6487","authenticated-orcid":false,"given":"Kia","family":"Dashtipour","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]},{"given":"Adnan","family":"Zahid","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7097-9969","authenticated-orcid":false,"given":"Qammer H.","family":"Abbasi","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-9136","authenticated-orcid":false,"given":"Muhammad Ali","family":"Imran","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012014","DOI":"10.1088\/1742-6596\/1437\/1\/012014","article-title":"Human Posture Recognition in Intelligent Healthcare","volume":"1437","author":"Yang","year":"2020","journal-title":"J. Physics Conf. Ser."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abbasi, Q.H., Rehman, M.U., Qaraqe, K., and Alomainy, A. (2016). Advances in Body-Centric Wireless Communication: Applications and State-of-the-Art, Institution of Engineering and Technology.","DOI":"10.1049\/PBTE065E"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1049\/htl.2017.0021","article-title":"Monitoring of atopic dermatitis using leaky coaxial cable","volume":"4","author":"Dong","year":"2017","journal-title":"Healthc. Technol. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Mishmish, H., Alkhayyat, A., Rahim, H.A., Hammood, D.A., Ahmad, R.B., and Abbasi, Q.H. (2018). Critical data-based incremental cooperative communication for wireless body area network. Sensors, 18.","DOI":"10.3390\/s18113661"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mercuri, M., Garripoli, C., Karsmakers, P., Soh, P.J., Vandenbosch, G.A., Pace, C., Leroux, P., and Schreurs, D. (2016). Healthcare system for non-invasive fall detection indoor environment. Applications in Electronics Pervading Industry, Environment and Society, Springer.","DOI":"10.1007\/978-3-319-20227-3_19"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2019.02.011","article-title":"An efficient monitoring of eclamptic seizures in wireless sensors networks","volume":"75","author":"Haider","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49088","DOI":"10.1109\/ACCESS.2019.2909828","article-title":"A novel cloud-based framework for the elderly healthcare services using digital twin","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32507","DOI":"10.1109\/ACCESS.2018.2846605","article-title":"Breathing rhythm analysis in body centric networks","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Shang, C., Chang, C.Y., Chen, G., Zhao, S., and Chen, H. (2019). BIA: Behavior Identification Algorithm using Unsupervised Learning Based on Sensor Data for Home Elderly. IEEE J. Biomed. Health Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/JERM.2018.2827705","article-title":"Monitoring of patients suffering from REM sleep behavior disorder","volume":"2","author":"Yang","year":"2018","journal-title":"IEEE J. Electromagn. Microwaves Med. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131102","DOI":"10.1109\/ACCESS.2019.2940386","article-title":"WiGrus: A Wifi-Based Gesture Recognition System Using Software-Defined Radio","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tahir, A., Ahmad, J., Shah, S.A., Morison, G., Skelton, D.A., Larijani, H., Abbasi, Q.H., Imran, M.A., and Gibson, R.M. (2019). WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinsons Leveraging 5G Spectrum with Deep Learning. Electronics, 8.","DOI":"10.3390\/electronics8121433"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, L., Shah, S.A., Zhao, G., and Yang, X. (2018). Respiration symptoms monitoring in body area networks. Appl. Sci., 8.","DOI":"10.3390\/app8040568"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s00521-019-04037-8","article-title":"Diagnosis of the Hypopnea syndrome in the early stage","volume":"32","author":"Yang","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/JBHI.2016.2618890","article-title":"Anatomical region-specific in vivo wireless communication channel characterization","volume":"21","author":"Demir","year":"2016","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Santos, G.L., Endo, P.T., Monteiro, K.H.d.C., Rocha, E.d.S., Silva, I., and Lynn, T. (2019). Accelerometer-based human fall detection using convolutional neural networks. Sensors, 19.","DOI":"10.3390\/s19071644"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/LAWP.2018.2881303","article-title":"Millimeter-wave liquid crystal polymer based conformal antenna array for 5G applications","volume":"18","author":"Jilani","year":"2018","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yao, X., Khan, A., and Jin, Y. (July, January 29). Energy Efficient Communication among Wearable Devices using Optimized Motion Detection. Proceedings of the 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain.","DOI":"10.1109\/ISCC47284.2019.8969572"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/LAWP.2011.2163378","article-title":"Spatial correlation analysis of on-body radio channels considering statistical significance","volume":"10","author":"Yang","year":"2011","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, L., Wei, Z., Zhang, C., Wang, W., and Fan, Y. (2019). R-DEHM: CSI-based robust duration estimation of human motion with WiFi. Sensors, 19.","DOI":"10.3390\/s19061421"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TTHZ.2016.2599075","article-title":"THz time-domain spectroscopy of human skin tissue for in-body nanonetworks","volume":"6","author":"Chopra","year":"2016","journal-title":"IEEE Trans. Terahertz Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lolla, S., and Zhao, A. (2019, January 16). WiFi Motion Detection: A Study into Efficacy and Classification. Proceedings of the 2019 IEEE Integrated STEM Education Conference (ISEC), Princeton, NJ, USA.","DOI":"10.1109\/ISECon.2019.8882085"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MAES.2019.2953803","article-title":"Development and Calibration of a Low-Cost Radar Testbed Based on the Universal Software Radio Peripheral","volume":"34","author":"Christiansen","year":"2019","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, S.C. (2017, January 4\u20137). Device-free activity recognition using CSI & big data analysis: A survey. Proceedings of the 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy.","DOI":"10.1109\/ICUFN.2017.7993844"},{"key":"ref_25","unstructured":"Tichy, M., and Ulovec, K. (2012, January 17\u201318). OFDM system implementation using a USRP unit for testing purposes. Proceedings of the 22nd International Conference Radioelektronika 2012, Brno, Czech Republic."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ashleibta, A., Shah, S., Zahid, A., Imran, M.A., and Abbasi, Q.H. (2020). Software Defined Radio Based Testbed for Large Scale Body Movements. IEEE Access, Accepted for Publication.","DOI":"10.1109\/IEEECONF35879.2020.9330027"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xu, W., Hu, W., and Kanhere, S.S. (2017, January 7\u201310). WiCare: Towards In-Situ Breath Monitoring. Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Melbourne, VIC, Australia.","DOI":"10.1145\/3144457.3144467"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chin, Z.H., Ng, H., Yap, T.T.V., Tong, H.L., Ho, C.C., and Goh, V.T. (2019). Daily Activities Classification on Human Motion Primitives Detection Dataset. Computational Science and Technology, Springer.","DOI":"10.1007\/978-981-13-2622-6_12"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shah, S.A., Fan, D., Ren, A., Zhao, N., Yang, X., and Tanoli, S.A.K. (2018). Seizure episodes Detection via Smart Medical Sensing System, Springer.","DOI":"10.1007\/s12652-018-1142-3"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MPOT.2019.2906977","article-title":"Radar for Health Care: Recognizing Human Activities and Monitoring Vital Signs","volume":"38","author":"Fioranelli","year":"2019","journal-title":"IEEE Potentials"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ding, C., Zou, Y., Sun, L., Hong, H., Zhu, X., and Li, C. (2019, January 19\u201322). Fall detection with multi-domain features by a portable FMCW radar. Proceedings of the 2019 IEEE MTT-S International Wireless Symposium (IWS), Guangzhou, China.","DOI":"10.1109\/IEEE-IWS.2019.8804036"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shah, S.A., and Fioranelli, F. (2019, January 23\u201327). Human Activity Recognition: Preliminary Results for Dataset Portability using FMCW Radar. Proceedings of the 2019 International Radar Conference (RADAR), Toulon, France.","DOI":"10.1109\/RADAR41533.2019.171307"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MAES.2019.2933971","article-title":"RF sensing technologies for assisted daily living in healthcare: A comprehensive review","volume":"34","author":"Shah","year":"2019","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1109\/TII.2019.2947435","article-title":"NOMA-based Resource Allocation for Cluster-based Cognitive Industrial Internet of Things","volume":"16","author":"Liu","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1109\/JIOT.2018.2847731","article-title":"A novel multichannel Internet of things based on dynamic spectrum sharing in 5G communication","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1007\/s42835-019-00187-w","article-title":"A wrist worn acceleration based human motion analysis and classification for ambient smart home system","volume":"14","author":"Jalal","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"167055","DOI":"10.1109\/ACCESS.2019.2953772","article-title":"Recognizing Ping-Pong Motions Using Inertial Data Based on Machine Learning Classification Algorithms","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, P., Su, Z., Dong, Z., and Pahlavan, K. (2020, January 6\u20138). Complex Motion Detection Based on Channel State Information and LSTM-RNN. Proceedings of the 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC47524.2020.9031214"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Al-Khafajiy, M., Otoum, S., Baker, T., Asim, M., Maamar, Z., Aloqaily, M., Taylor, M., and Randles, M. (2020). Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model. ACM Trans. Internet Technol., submitted for publication.","DOI":"10.1145\/3382770"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., and Baker, T. (2018). An edge computing based smart healthcare framework for resource management. Sensors, 18.","DOI":"10.3390\/s18124307"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12199","DOI":"10.1109\/ACCESS.2017.2719706","article-title":"Social behaviometrics for personalized devices in the internet of things era","volume":"5","author":"Anjomshoa","year":"2017","journal-title":"IEEE Access"},{"key":"ref_42","unstructured":"Nipu, M.N.A., Talukder, S., Islam, M.S., and Chakrabarty, A. (2018, January 25\u201329). Human identification using wifi signal. Proceedings of the 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tanoli, S.A.K., Rehman, M., Khan, M.B., Jadoon, I., Ali Khan, F., Nawaz, F., Shah, S.A., Yang, X., and Nasir, A.A. (2018). An experimental channel capacity analysis of cooperative networks using Universal Software Radio Peripheral (USRP). Sustainability, 10.","DOI":"10.3390\/su10061983"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"348","DOI":"10.3102\/1076998619832248","article-title":"Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language","volume":"44","author":"Hao","year":"2019","journal-title":"J. Educ. Behav. Stat."},{"key":"ref_45","unstructured":"Pappalardo, L. (2019). Scikit-Mobility: A Python Library for the Analysis, Generation and Risk Assessment of Mobility Data. arXiv, Available online: https:\/\/arxiv.org\/abs\/1907.07062."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.bspc.2017.01.012","article-title":"Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation","volume":"52","author":"Shaikhina","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103366","DOI":"10.1016\/j.compbiomed.2019.103366","article-title":"Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm","volume":"112","author":"Joof","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, K., Gu, Y., Zhang, P., An, W., and Li, W. (2019, January 10). Research on KNN Algorithm in Malicious PDF Files Classification under Adversarial Environment. Proceedings of the 2019 4th International Conference on Big Data and Computing, New York, NY, USA.","DOI":"10.1145\/3335484.3335527"},{"key":"ref_49","unstructured":"Jain, M., Narayan, S., Balaji, P., Bhowmick, A., Muthu, R.K., Bharath, K.P., and Karthik, R. (2020). Speech emotion recognition using support vector machine. arXiv, Available online: https:\/\/arxiv.org\/abs\/2002.07590."},{"key":"ref_50","first-page":"1","article-title":"Ids using machine learning-current state of art and future directions","volume":"15","author":"Hamid","year":"2016","journal-title":"Curr. J. Appl. Sci. Technol."},{"key":"ref_51","first-page":"2319","article-title":"Intrusion Detection System Using Weka Data Mining Tool","volume":"6","author":"Hassan","year":"2017","journal-title":"Int. J. Sci. Res."},{"key":"ref_52","first-page":"101","article-title":"Intrusion detection using machine learning: A comparison study","volume":"118","author":"Biswas","year":"2018","journal-title":"Int. J. Pure Appl. Math."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tandon, S., Tripathi, S., Saraswat, P., and Dabas, C. (2019, January 7\u20139). Bitcoin Price Forecasting using LSTM and 10-Fold Cross validation. Proceedings of the 2019 International Conference on Signal Processing and Communication (ICSC), Noida, India.","DOI":"10.1109\/ICSC45622.2019.8938251"},{"key":"ref_54","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2013), Computational Intelligence and Machine Learning, Bruges, Belgium."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2653\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:26:01Z","timestamp":1760174761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,6]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092653"],"URL":"https:\/\/doi.org\/10.3390\/s20092653","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,6]]}}}