{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:24:32Z","timestamp":1771064672499,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.<\/jats:p>","DOI":"10.3390\/s21113808","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T21:42:06Z","timestamp":1622497326000},"page":"3808","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6598-5344","authenticated-orcid":false,"given":"Ran","family":"Wei","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Hongda","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Mingkun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xinguo","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhuoling","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Bo","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8514","DOI":"10.1109\/JSEN.2018.2866802","article-title":"Heading Drift Reduction for Foot-Mounted Inertial Navigation System via Multi-Sensor Fusion and Dual-Gait Analysis","volume":"19","author":"Zhao","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1111\/tgis.12178","article-title":"Integrating indoor and outdoor spaces for pedestrian navigation guidance: A review","volume":"20","author":"Vanclooster","year":"2016","journal-title":"Trans. GIS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1109\/TVT.2015.2391296","article-title":"Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model","volume":"64","author":"Zampella","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, Y., Li, H., Shu, Q., Chen, Y., Yang, W., Liu, Y., and Zhao, M. (2017, January 22\u201325). Research on ZUPT technology for pedestrian navigation. Proceeedings of the 2017 8th International Conference on Mechanical and Aerospace Engineering (ICMAE), Prague, Czech Republic.","DOI":"10.1109\/ICMAE.2017.8038739"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xinfeng, B.A., and Wang, P. (2012, January 29\u201331). Design of soldier status monitoring and command and control system based on Beidou system. Proceedings of the 2012 2nd International Conference on Computer Science and Network Technology, Changchun, China.","DOI":"10.1109\/ICCSNT.2012.6526174"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ilyas, M., Cho, K., Baeg, S., and Park, S. (June, January 31). Drift reduction in IMU-only pedestrian navigation system in unstructured environment. Proceedings of the 2015 10th Asian Control Conference (ASCC), Kota Kinabalu, Malaysia.","DOI":"10.1109\/ASCC.2015.7244849"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCG.2005.140","article-title":"Pedestrian tracking with shoe-mounted inertial sensors","volume":"25","author":"Foxlin","year":"2005","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6766","DOI":"10.1109\/JSEN.2016.2585599","article-title":"Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet","volume":"16","author":"Norrdine","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_9","unstructured":"Nilsson, J.O., Skog, I., and Hndel, P. (2012, January 13\u201315). A note on the limitations of ZUPTs and the implications on sensor error modeling. Proceedings of the Indoor Positioning and Indoor Navigation 2012 International Conference, Sydney, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chernyshoff, A., and Shkel, A.M. (2018, January 24\u201327). Error Analysis of ZUPT-Aided Pedestrian Inertial Navigation. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533814"},{"key":"ref_11","unstructured":"Li, X., Mao, Y., Xie, L., Chen, J., and Song, C. (2014, January 8\u201310). Applications of zero-velocity detector and Kalman filter in zero velocity update for inertial navigation system. Proceedings of the IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, W., Li, X., Wei, D., Ji, X., and Yuan, H. (2017, January 18\u201321). A foot-mounted PDR system based on IMU\/EKF + HMM + ZUPT + ZARU + HDR + compass algorithm. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115916"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, X., Liu, B., Lan, X., Xiao, Z., Lin, S., Yan, B., and Zhou, L. (2019, January 9\u201313). AZUPT: Adaptive Zero Velocity Update Based on Neural Networks for Pedestrian Tracking. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014070"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1109\/TMECH.2015.2430357","article-title":"Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System","volume":"20","author":"Wang","year":"2015","journal-title":"IEEE\/ASME Trans. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., and Song, S. (2016, January 21\u201323). Going Deeper with Embedded FPGA Platform for Convolutional Neural Network. Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2847263.2847265"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/SURV.2012.121912.00075","article-title":"A Survey of Indoor Inertial Positioning Systems for Pedestrians","volume":"15","author":"Harle","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, A.R., Seco, F., Prieto, J.C., and Guevara, J. (2010, January 11\u201312). Indoor pedestrian navigation using an INS\/EKF framework for yaw drift reduction and a foot-mounted IMU. Proceedings of the 2010 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany.","DOI":"10.1109\/WPNC.2010.5649300"},{"key":"ref_19","unstructured":"Anacleto, R., Figueiredo, L., Almeida, A., and Novais, P. (2014, January 7\u201310). Localization system for pedestrians based on sensor and information fusion. Proceedings of the 17th International Conference on Information Fusion, Salamanca, Spain."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Su, C., Chou, J., Yi, C., Tseng, Y., and Tsai, C. (2010, January 13\u201316). Sensor-Aided Personal Navigation Systems for Handheld Devices. Proceedings of the 2010 39th International Conference on Parallel Processing Workshops, San Diego, CA, USA.","DOI":"10.1109\/ICPPW.2010.78"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kazemipur, B., Syed, Z., Georgy, J., and El-Sheimy, N. (2014, January 5\u20138). Vision-based context and height estimation for 3D indoor location. Proceedings of the 2014 IEEE\/ION Position, Location and Navigation Symposium, Monterey, CA, USA.","DOI":"10.1109\/PLANS.2014.6851508"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9836","DOI":"10.3390\/s130809836","article-title":"Enhancing indoor inertial pedestrian navigation using a shoe-worn marker","volume":"13","author":"Placer","year":"2013","journal-title":"Sensors"},{"key":"ref_23","unstructured":"Zhang, X.-D., Ren, M.-R., Pu, W., and Kai, P. (2015, January 28\u201330). A new zero velocity update algorithm for the shoe-mounted personal navigation system based on IMU. Proceedings of the 2015 34th Chinese Control Conference, Hangzhou, China."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, M., Song, Q., Li, Y., and Zhou, Z. (2017, January 15\u201317). Magnetic field aided heading estimation for indoor pedestrian positioning. Proceedings of the 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China.","DOI":"10.1109\/ITNEC.2017.8284870"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7253","DOI":"10.1109\/JIOT.2019.2915791","article-title":"Smart Insole-Based Indoor Localization System for Internet of Things Applications","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7002304","DOI":"10.1109\/LSENS.2019.2946129","article-title":"Adaptive Threshold for Zero-Velocity Detector in ZUPT-Aided Pedestrian Inertial Navigation","volume":"3","author":"Wang","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/JSEN.2017.2665678","article-title":"Adaptive Zero Velocity Update Based on Velocity Classification for Pedestrian Tracking","volume":"17","author":"Zhang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ren, M., Pan, K., Liu, Y., Guo, H., Zhang, X., and Wang, P. (2016). A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System. Sensors, 16.","DOI":"10.3390\/s16010139"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JBHI.2017.2679486","article-title":"Mobile Stride Length Estimation With Deep Convolutional Neural Networks","volume":"22","author":"Hannink","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, C., Lu, X., Markham, A., and Niki, T. (2018, January 2\u20137). IONet: Learning to Cure the Curse of Drift in Inertial Odometry. Proceedings of the 2018 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12102"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wagstaff, B., and Kelly, J. (2018, January 24\u201327). LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533770"},{"key":"ref_32","unstructured":"Zheng, H., and Zhang, B. (2019, January 18\u201320). Design of ultrasonic assisted IMU indoor positioning system based on FPGA. Proceedings of the 12th National Conference on Signal and Intelligent Information Processing and Application, Jilin, China."},{"key":"ref_33","first-page":"176","article-title":"Design of MEMS strapdown inertial navigation system based on FPGA","volume":"39","author":"Zhang","year":"2017","journal-title":"Piezoelectric Acoustooptic"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s00521-018-3761-1","article-title":"A survey of FPGA-based accelerators for convolutional neural networks","volume":"32","author":"Mittal","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Suda, N., Chandra, V., Dasika, G., Mohanty, A., Ma, Y., Vrudhula, S.B.K., Seo, J.-S., and Cao, Y. (2016, January 21\u201323). Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks. Proceedings of the ACM\/Sigda International Symposium, Monterey, CA, USA.","DOI":"10.1145\/2847263.2847276"},{"key":"ref_36","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TVLSI.2018.2815603","article-title":"Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA","volume":"26","author":"Ma","year":"2018","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shang, J., Qian, L., Zhang, Z., Xue, L., and Liu, H. (2019). LACS: A High-Computational-Efficiency Accelerator for CNNs. IEEE Access.","DOI":"10.1109\/ACCESS.2019.2962746"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ferdman, M., and Milder, P. (2017, January 24\u201328). Maximizing CNN accelerator efficiency through resource partitioning. Proceedings of the 2017 ACM\/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), Toronto, ON, Canada.","DOI":"10.1145\/3079856.3080221"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sun, F., Wang, C., Gong, L., Xu, C., Zhang, Y., Lu, Y., Li, X., and Zhou, X. (2017, January 12\u201315). A High-Performance Accelerator for Large-Scale Convolutional Neural Networks. Proceedings of the 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA\/IUCC), Guangzhou, China.","DOI":"10.1109\/ISPA\/IUCC.2017.00099"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3808\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:09:22Z","timestamp":1760162962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,31]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113808"],"URL":"https:\/\/doi.org\/10.3390\/s21113808","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,31]]}}}