{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:50:16Z","timestamp":1764784216237,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,26]],"date-time":"2019-03-26T00:00:00Z","timestamp":1553558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Projects of the Ministry of Science and Technology of China","award":["2017YFD0701603"],"award-info":[{"award-number":["2017YFD0701603"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31401285"],"award-info":[{"award-number":["31401285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Educational Commission of Anhui Province of China","award":["KJ2016A305"],"award-info":[{"award-number":["KJ2016A305"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance.<\/jats:p>","DOI":"10.3390\/s19061464","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T05:03:12Z","timestamp":1553662992000},"page":"1464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Posture Recognition Method Based on Indoor Positioning Technology"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0332-1999","authenticated-orcid":false,"given":"Xiaoping","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Intelligent Machines, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui University, Hefei 230039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zelin","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gong, S., Wang, Y., Zhang, M., and Wang, C. (2017, January 18\u201320). Design of Remote Elderly Health Monitoring System Based on MEMS Sensors. Proceedings of the IEEE International Conference on Information and Automation (ICIA), Macau, China.","DOI":"10.1109\/ICInfA.2017.8078958"},{"key":"ref_2","unstructured":"(2019, March 25). Aging Stats, Available online: https:\/\/agingstats.gov\/."},{"key":"ref_3","first-page":"1899","article-title":"3D Human Pose Estimation Based on Multi-kernel Sparse Coding","volume":"44","author":"Yu","year":"2016","journal-title":"Acta Electron. Sin."},{"key":"ref_4","first-page":"279","article-title":"Articulated Human Pose Estimation with Occlusion Level","volume":"29","author":"Dai","year":"2017","journal-title":"J. Comput.-Aided Des. Comput. Graphics"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kien, H.K., Hung, N.K., Chau, M.T., Duyen, N.T., and Thanh, N.X. (2017, January 19\u201321). Single view image based-3D human pose reconstruction. Proceedings of the 9th International Conference on Knowledge and Systems Engineering (KSE), Hue, Vietnam.","DOI":"10.1109\/KSE.2017.8119445"},{"key":"ref_6","first-page":"3742","article-title":"Image-Based Three-Dimensional Human Pose Recovery by Multiview Locality-Sensitive Sparse Retrieval","volume":"62","author":"Hong","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","first-page":"285","article-title":"A Novel Human Activity Recognition Method Using Joint Points Information","volume":"36","author":"Tian","year":"2014","journal-title":"Robot"},{"key":"ref_8","unstructured":"Abbondanza, P., Giancola, S., Sala, R., and Tarabini, M. (2016, January 14\u201316). Accuracy of the Microsoft Kinect System in the Identification of the Body Posture. Proceedings of the 6th International Conference on Wireless Mobile Communication and Healthcare, Milan, Italy."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sombandith, V., Walairacht, A., and Walairacht, S. (2017, January 27\u201330). Recognition of Lao Sentence Sign Language Using Kinect Sensor. Proceedings of the 14th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Phuket, Thailand.","DOI":"10.1109\/ECTICon.2017.8096323"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tripathy, S.R., Chakravarty, K., Sinha, A., Chatterjee, D., and Saha, S.K. (2017, January 28\u201331). Constrained Kalman Filter for Improving Kinect Based Measurements. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.","DOI":"10.1109\/ISCAS.2017.8050664"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/THMS.2014.2377111","article-title":"Human Activity Recognition Process Using 3-D Posture Data","volume":"45","author":"Gaglio","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_12","unstructured":"(2019, March 25). Kinect for Windows. Available online: https:\/\/developer.microsoft.com\/en-us\/windows\/kinect."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6733","DOI":"10.1109\/JSEN.2016.2585667","article-title":"A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor","volume":"16","author":"Pierleoni","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Musalek, M. (2017, January 8\u201311). A wearable fall detector for elderly people. Proceedings of the 28th DAAAM International Symposium, Zadar, Croatia.","DOI":"10.2507\/28th.daaam.proceedings.141"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, G., Chen, R., Ye, F., Chen, L., Pan, Y., Liu, M., and Cao, Z. (2019). A pose awareness solution for estimating pedestrian walking speed. Remote Sens., 11.","DOI":"10.3390\/rs11010055"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, Z., Zhang, W., Patil, A., Patil, K., Zhu, T., Shiroma, E.J., Schepps, M.A., and Harris, T.B. (2016, January 5\u20138). Wearable sensor based human posture recognition. Proceedings of the IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7841004"},{"key":"ref_17","unstructured":"Caroppo, A., Leone, A., Rescio, G., Diraco, G., and Siciliano, P. (2016, January 23\u201325). Multi-sensor Platform for Detection of Anomalies in Human Sleep Patterns. Proceedings of the 3rd National Conference Sensors, Rome, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, Z., and Dong, T. (2017). A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors, 17.","DOI":"10.3390\/s17020341"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TMC.2017.2715820","article-title":"Optimization of fingerprints reporting strategy for WLAN indoor localization","volume":"17","author":"Tian","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zuo, Z., Liu, L., Zhang, L., and Fang, Y. (2018). Indoor positioning based on Bluetooth low-energy beacons adopting graph optimization. Sensors, 18.","DOI":"10.3390\/s18113736"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/JLT.2015.2507182","article-title":"Indoor position tracking using multiple optical receivers","volume":"34","author":"Yasir","year":"2016","journal-title":"J. Lightware Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Antoniazzi, F., Paolini, G., Roffia, L., Masotti, D., Costanzo, A., and Cinotti, T.S. (2017, January 6\u201310). A web of things approach for indoor position monitoring of elderly and impaired people. Proceedings of the Conference of Open Innovation Association (FRUCT), Helsinki, Finland.","DOI":"10.23919\/FRUCT.2017.8250164"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dabove, P., Di Pietra, V., Piras, M., Jabbar, A.A., and Kazim, S.A. (2018, January 23\u201326). Indoor positioning using ultra-wide band (UWB) technologies: positioning accuracies and sensors performances. Proceedings of the IEEE\/ION Position, Location and Navigation Symposium (PLANS 2018), Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373379"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1587\/transfun.E101.A.185","article-title":"Proposals and implementation of high band IR-UWB for increasing propagation distance for indoor positioning","volume":"E101A","author":"Li","year":"2018","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ridolfi, M., Vandermeeren, S., Defraye, J., Steendam, H., Gerlo, J., De Clercq, D., Hoebeke, J., and De Poorter, E. (2018). Experimental Evaluation of UWB Indoor Positioning for Sport Postures. Sensors, 18.","DOI":"10.3390\/s18010168"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, J., Yu, X., Wang, Y., and Xiao, X. (2016). An integrated wireless wearable sensor system for posture recognition and indoor localization. Sensors, 16.","DOI":"10.3390\/s16111825"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3991\/ijoe.v14i10.9307","article-title":"Underground operator monitoring based on ultra-wide band WSN","volume":"14","author":"Xie","year":"2018","journal-title":"Int. J. Online Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xia, B., Lao, Z., Zhang, R., Tian, Y., Chen, G., Sun, Z., Wang, W., Sun, W., Lai, Y., and Wang, M. (2018). Online parameter identification and state of charge estimation of lithium-ion batteries based on forgetting factor recursive least squares and nonlinear Kalman filter. Energies, 11.","DOI":"10.3390\/en11010003"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isatra.2018.02.007","article-title":"An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs","volume":"75","author":"Xiao","year":"2018","journal-title":"ISA Trans."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, H.Y., and Chang, S.H. (2014, January 10\u201312). A skeleton-occluded repair method from Kinect. Proceedings of the International Symposium on Computer, Consumer and Control, Taichung, Taiwan.","DOI":"10.1109\/IS3C.2014.77"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/6\/1464\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:40:36Z","timestamp":1760186436000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/6\/1464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,26]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["s19061464"],"URL":"https:\/\/doi.org\/10.3390\/s19061464","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,3,26]]}}}