{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T07:17:28Z","timestamp":1781594248571,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"School of Engineering at University of Dayton"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.<\/jats:p>","DOI":"10.3390\/s22155470","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"5470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-3761","authenticated-orcid":false,"given":"Mubarak A.","family":"Alanazi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah K.","family":"Alhazmi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5964-1046","authenticated-orcid":false,"given":"Osama","family":"Alsattam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kara","family":"Gnau","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meghan","family":"Brown","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shannon","family":"Thiel","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-1927","authenticated-orcid":false,"given":"Kurt","family":"Jackson","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9707-2964","authenticated-orcid":false,"given":"Vamsy P.","family":"Chodavarapu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ergon.2018.02.002","article-title":"A Survey on Health Monitoring Systems for Health Smart Homes","volume":"66","author":"Mshali","year":"2018","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.2147\/CIA.S173239","article-title":"Frailty Assessment in Older Adults with Chronic Obstructive Respiratory Diseases","volume":"13","author":"Guan","year":"2018","journal-title":"Clin. Interv. Aging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8543","DOI":"10.1109\/JSEN.2018.2883786","article-title":"On Research Challenges in Hybrid Medium-Access Control Protocols for IEEE 802.15.6 WBANs","volume":"19","author":"Saboor","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Saboor, A., Mustafa, A., Ahmad, R., Khan, M.A., Haris, M., and Hameed, R. (2019, January 13\u201315). Evolution of Wireless Standards for Health Monitoring. Proceedings of the 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), Jaipur, India.","DOI":"10.1109\/IEMECONX.2019.8877040"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Saboor, A., Kim, H., and Park, H. (2021). A Systematic Review of Location Aware Schemes in the Internet of Things. Sensors, 21.","DOI":"10.3390\/s21093228"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Agham, N., and Chaskar, U. (2019, January 26\u201328). Prevalent Approach of Learning Based Cuffless Blood Pressure Measurement System for Continuous Health-Care Monitoring. Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul, Turkey.","DOI":"10.1109\/MeMeA.2019.8802170"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10916-019-1365-7","article-title":"A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults\u2014A Focus on Ageing Population and Independent Living","volume":"43","author":"Baig","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alanazi, M.A., Alhazmi, A.K., Yakopcic, C., and Chodavarapu, V.P. (2021, January 24). Machine Learning Models for Human Fall Detection Using Millimeter Wave Sensor. Proceedings of the 2021 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA.","DOI":"10.1109\/CISS50987.2021.9400259"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1109\/JSYST.2022.3140546","article-title":"Noninvasive Human Activity Recognition Using Millimeter-Wave Radar","volume":"16","author":"Yu","year":"2022","journal-title":"IEEE Syst. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"di Biase, L., Di Santo, A., Caminiti, M.L., De Liso, A., Shah, S.A., Ricci, L., and Di Lazzaro, V. (2020). Gait Analysis in Parkinson\u2019s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring. Sensors, 20.","DOI":"10.3390\/s20123529"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Usmani, S., Saboor, A., Haris, M., Khan, M.A., and Park, H. (2021). Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21155134"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"160635","DOI":"10.1109\/ACCESS.2021.3132559","article-title":"Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera","volume":"9","author":"Alrashdi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.neucom.2020.07.136","article-title":"Multi-Frequency and Multi-Domain Human Activity Recognition Based on SFCW Radar Using Deep Learning","volume":"444","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1049\/iet-its.2020.0009","article-title":"Predictive Model for Battery Life in IoT Networks","volume":"14","author":"Srivastava","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.parkreldis.2021.07.032","article-title":"Digital Health Technology for Non-Motor Symptoms in People with Parkinson\u2019s Disease: Futile or Future?","volume":"89","author":"Sringean","year":"2021","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Jin, F., and Cao, S. (2020, January 21). NLP Based Skeletal Pose Estimation Using MmWave Radar Point-Cloud: A Simulation Approach. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266600"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, X., Liu, J., Chen, Y., Guo, X., and Xie, Y. (2020, January 6\u20139). MU-ID: Multi-User Identification Through Gaits Using Millimeter Wave Radios. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155471"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, Z., Pathak, P.H., Zeng, Y., Liran, X., and Mohapatra, P. (2016, January 5). Monitoring Vital Signs Using Millimeter Wave. Proceedings of the Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Paderborn, Germany.","DOI":"10.1145\/2942358.2942381"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cen, S.H., and Newman, P. (2018, January 21\u201325). Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460687"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1080\/10749357.2017.1285746","article-title":"A Systematic Review of Mechanisms of Gait Speed Change Post-Stroke. Part 1: Spatiotemporal Parameters and Asymmetry Ratios","volume":"24","author":"Wonsetler","year":"2017","journal-title":"Top. Stroke Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1007\/s40520-020-01715-9","article-title":"Clinical Assessment of Gait and Functional Mobility in Italian Healthy and Cognitively Impaired Older Persons Using Wearable Inertial Sensors","volume":"33","author":"Mulas","year":"2021","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"167830","DOI":"10.1109\/ACCESS.2020.3022818","article-title":"Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review","volume":"8","author":"Saboor","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zebin, T., Scully, P.J., and Ozanyan, K.B. (November, January 30). Human Activity Recognition with Inertial Sensors Using a Deep Learning Approach. Proceedings of the 2016 IEEE SENSORS, Orlando, FL, USA.","DOI":"10.1109\/ICSENS.2016.7808590"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.3390\/s140203362","article-title":"Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.eswa.2018.01.047","article-title":"Supervised Machine Learning Scheme for Electromyography-Based Pre-Fall Detection System","volume":"100","author":"Rescio","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1109\/TBME.2015.2410142","article-title":"Acoustic Gaits: Gait Analysis With Footstep Sounds","volume":"62","author":"Butko","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chiang, T.-H., Su, Y.-J., Shiu, H.-R., and Tseng, Y.-C. (2020, January 20\u201324). 3D Gait Tracking by Acoustic Doppler Effects. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175663"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/S0021-9290(02)00032-5","article-title":"Ultrasonic Motion Analysis System\u2014Measurement of Temporal and Spatial Gait Parameters","volume":"35","author":"Huitema","year":"2002","journal-title":"J. Biomech."},{"key":"ref_30","first-page":"282","article-title":"A New Ultrasonic Stride Length Measuring System","volume":"48","author":"Maki","year":"2012","journal-title":"Biomed. Sci. Instrum."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Steinert, A., Sattler, I., Otte, K., R\u00f6hling, H., Mansow-Model, S., and M\u00fcller-Werdan, U. (2019). Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System. Sensors, 20.","DOI":"10.3390\/s20010125"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, C., Ugbolue, U., Carse, B., Stankovic, V., Stankovic, L., and Rowe, P. (2013, January 15\u201318). Multiple Marker Tracking in a Single-Camera System for Gait Analysis. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738644"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, M., Li, T., Alsheikh, M.A., Tian, Y., Zhao, H., Torralba, A., and Katabi, D. (2018, January 18\u201323). Through-Wall Human Pose Estimation Using Radio Signals. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00768"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1109\/LGRS.2015.2452946","article-title":"Micro-Doppler-Based Human Activity Classification Using the Mote-Scale BumbleBee Radar","volume":"12","author":"Cagliyan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","unstructured":"Sengupta, A., and Cao, S. (2022). MmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation Using MmWave Radars. IEEE Trans. Neural Netw. Learn. Syst., 1\u201312."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3477030","article-title":"MARS: MmWave-Based Assistive Rehabilitation System for Smart Healthcare","volume":"20","author":"An","year":"2021","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1109\/TBME.2019.2893528","article-title":"Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures","volume":"66","author":"Seifert","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s10072-011-0914-8","article-title":"Avascular Necrosis of the Femoral Head in Multiple Sclerosis: Report of Five Patients","volume":"33","author":"Sahraian","year":"2012","journal-title":"Neurol. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.neucli.2008.07.006","article-title":"Postural Disorders in Parkinson\u2019s Disease","volume":"38","author":"Benatru","year":"2008","journal-title":"Neurophysiol. Clin. Neurophysiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1177\/014556139907800810","article-title":"Equilibrium and Balance in the Elderly","volume":"78","author":"Hobeika","year":"1999","journal-title":"Ear. Nose. Throat J."},{"key":"ref_41","unstructured":"Instruments, T. (2022, June 28). IWR6843 Intelligent MmWave Overhead Detection Sensor (ODS) Antenna Plug-in Module. Available online: https:\/\/www.ti.com\/tool\/IWR6843ISK-ODS."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1023\/A:1009745219419","article-title":"Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications","volume":"2","author":"Sander","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","article-title":"Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network","volume":"35","author":"Anthimopoulos","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_44","unstructured":"(2022, June 28). Jetson Nano Developer Kit. Available online: https:\/\/developer.nvidia.com\/embedded\/jetson-nano-developer-kit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:54:36Z","timestamp":1760140476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,22]]},"references-count":44,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155470"],"URL":"https:\/\/doi.org\/10.3390\/s22155470","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,22]]}}}