{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:36:17Z","timestamp":1773239777406,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association","doi-asserted-by":"publisher","award":["2021289"],"award-info":[{"award-number":["2021289"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.<\/jats:p>","DOI":"10.3390\/s22082840","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"2840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6611-3425","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"first","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-4609","authenticated-orcid":false,"given":"Fuping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, L., Xiong, Z., and Zhao, R. (2019, January 15\u201317). An Indoor Pedestrian Navigation Algorithm Based on Smartphone Mode Recognition. Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China.","DOI":"10.1109\/ITNEC.2019.8729458"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gang, H.-S., and Pyun, J.-Y. (2019). A Smartphone Indoor Positioning System Using Hybrid Localization Technology. Energies, 12.","DOI":"10.3390\/en12193702"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Abdelbar, M., and Buehrer, R.M. (2018, January 20\u201324). Pedestrian GraphSLAM Using Smartphone-Based PDR in Indoor Environments. Proceedings of the 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, USA.","DOI":"10.1109\/ICCW.2018.8403691"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Angrisano, A., Vultaggio, M., Gaglione, S., and Crocetto, N. (2019, January 9\u201312). Pedestrian Localization with PDR Supplemented by GNSS. Proceedings of the 2019 European Navigation Conference (ENC), Warsaw, Poland.","DOI":"10.1109\/EURONAV.2019.8714150"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., Shafiq, M., Kumari, S., and Park, Y. (2019). GUIDE: Smartphone Sensors-Based Pedestrian Indoor Localization with Heterogeneous Devices. Int. J. Commun. Syst., 32.","DOI":"10.1002\/dac.4062"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-017-0579-0","article-title":"Real Time Indoor Localization Integrating a Model Based Pedestrian Dead Reckoning on Smartphone and BLE Beacons","volume":"10","author":"Ciabattoni","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, H.-Y., Hsieh, C.-Y., Liu, K.-C., Cheng, H.-C., Hsu, S.J., and Chan, C.-T. (2018, January 13\u201317). Multimodal Sensors Data Fusion for Improving Indoor Pedestrian Localization. Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan.","DOI":"10.1109\/ICASI.2018.8394588"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6419","DOI":"10.3390\/s150306419","article-title":"Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data","volume":"15","author":"Barth","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mao, Y., Ogata, T., Ora, H., Tanaka, N., and Miyake, Y. (2021). Estimation of Stride-by-Stride Spatial Gait Parameters Using Inertial Measurement Unit Attached to the Shank with Inverted Pendulum Model. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-81009-w"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1109\/TBME.2014.2368211","article-title":"Inertial Sensor-Based Stride Parameter Calculation from Gait Sequences in Geriatric Patients","volume":"62","author":"Rampp","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kang, X., Huang, B., and Qi, G. (2018). A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones. Sensors, 18.","DOI":"10.3390\/s18010297"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kang, X., Huang, B., Yang, R., and Qi, G. (2018, January 8\u201312). Accurately Counting Steps of the Pedestrian with Varying Walking Speeds. Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Guangzhou, China.","DOI":"10.1109\/SmartWorld.2018.00134"},{"key":"ref_13","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 (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533814"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Shkel, A.M. (2019). Adaptive Threshold for Zero-Velocity Detector in ZUPT-Aided Pedestrian Inertial Navigation. IEEE Sens. Lett., 3.","DOI":"10.1109\/LSENS.2019.2946129"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Yang, S., Ni, Z., Qian, W., Gu, C., and Cao, Z. (2020). Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance. Sensors, 20.","DOI":"10.3390\/s20051530"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Nilsson, J.-O., Skog, I., H\u00e4ndel, P., and Hari, K.V.S. (2012, January 23\u201326). Foot-Mounted INS for Everybody\u2014An Open-Source Embedded Implementation. Proceedings of the Proceedings of the 2012 IEEE\/ION Position, Location and Navigation Symposium, Myrtle Beach, SC, USA.","DOI":"10.1109\/PLANS.2012.6236875"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhang, H., Sun, Y., Zhu, Y., Zhuang, X., Jia, J., and Zhang, P. (2018). An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model. Sensors, 18.","DOI":"10.3390\/s18051404"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Feng, Q., and Gao, M. (2020, January 18\u201319). Study on Application of Zero-Velocity Update Technology to Tracked Vehicle Inertial Navigation. Proceedings of the 2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Zhangjiajie, China.","DOI":"10.1109\/ICVRIS51417.2020.00040"},{"key":"ref_21","first-page":"1","article-title":"Using the ADXL202 in Pedometer and Personal Navigation Applications","volume":"2","author":"Weinberg","year":"2002","journal-title":"Analog Devices AN-602 Appl. Note"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.5081\/jgps.3.1.273","article-title":"A Step, Stride and Heading Determination for the Pedestrian Navigation System","volume":"3","author":"Kim","year":"2004","journal-title":"J. GPS"},{"key":"ref_23","unstructured":"Ladetto, Q. (2000, January 22). On Foot Navigation: Continuous Step Calibration Using Both Complementary Recursive Prediction and Adaptive Kalman Filtering. Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000), Salt Lake City, UT, USA."},{"key":"ref_24","unstructured":"(2022, January 07). Enhancing the Performance of Pedometers Using a Single Accelerometer|Analog Devices. Available online: https:\/\/www.analog.com\/en\/analog-dialogue\/articles\/enhancing-pedometers-using-single-accelerometer.html."},{"key":"ref_25","unstructured":"(2021, December 26). A Reliable and Accurate Indoor Localization Method Using Phone Inertial Sensors|Proceedings of the 2012 ACM Conference on Ubiquitous Computing. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/2370216.2370280."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, H. (2020). Wearable Sensor Data-Driven Walkability Assessment for Elderly People. Sustainability, 12.","DOI":"10.3390\/su12104041"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xing, H., Li, J., Hou, B., Zhang, Y., and Guo, M. (2017). Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm. J. Sens., 2017.","DOI":"10.1155\/2017\/6091261"},{"key":"ref_28","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_29","unstructured":"(2021, December 07). Continuous Home Monitoring of Parkinson\u2019s Disease Using Inertial Sensors: A Systematic Review. Available online: https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0246528."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mikos, V., Yen, S.-C., Tay, A., Heng, C.-H., Chung, C.L.H., Liew, S.H.X., Tan, D.M.L., and Au, W.L. (2018). Regression Analysis of Gait Parameters and Mobility Measures in a Healthy Cohort for Subject-Specific Normative Values. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0199215"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/0954411919889237","article-title":"Measures of Dynamic Balance during Level Walking in Healthy Adult Subjects: Relationship with Age, Anthropometry and Spatio-Temporal Gait Parameters","volume":"234","author":"Lencioni","year":"2020","journal-title":"Proc. Inst. Mech. Eng. H"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.gaitpost.2019.03.031","article-title":"The Influence of Childhood Obesity on Spatio-Temporal Gait Parameters","volume":"71","year":"2019","journal-title":"Gait Posture"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jbiomech.2019.05.028","article-title":"Gait Modification When Decreasing Double Support Percentage","volume":"92","author":"Williams","year":"2019","journal-title":"J. Biomech."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"McGrath, R.L., Ziegler, M.L., Pires-Fernandes, M., Knarr, B.A., Higginson, J.S., and Sergi, F. (2019). The Effect of Stride Length on Lower Extremity Joint Kinetics at Various Gait Speeds. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0200862"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tiwari, A., Bajpai, R., Khatavkar, R., Gupta, M., Garg, B., and Joshi, D. (2022, January 21\u201322). Exploring the Center of Pressure Shift Feedback at Heel Strike to Modulate the Step Length. Proceedings of the 2022 International Conference for Advancement in Technology (ICONAT), Goa, India.","DOI":"10.1109\/ICONAT53423.2022.9726031"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"91063","DOI":"10.1109\/ACCESS.2019.2927053","article-title":"Pedestrian Dead Reckoning Using Pocket-Worn Smartphone","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"31:1","DOI":"10.1145\/3314418","article-title":"ShoesLoc: In-Shoe Force Sensor-Based Indoor Walking Path Tracking","volume":"3","author":"Yu","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M. (2007). Information Retrieval for Music and Motion, Springer.","DOI":"10.1007\/978-3-540-74048-3"},{"key":"ref_39","unstructured":"Berndt, D.J., and Clifford, J. (2022, February 21). Using Dynamic Time Warping to Find Patterns in Time Series. KDD Workshop, Available online: https:\/\/www.aaai.org\/Papers\/Workshops\/1994\/WS-94-03\/WS94-03-031.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, F., Xu, Z., and Wei, J. (2022). The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds. Sensors, 22.","DOI":"10.3390\/s22041678"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lu, W., Wu, F., Zhu, H., and Zhang, Y. (2020). A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection. J. Sens., 2020.","DOI":"10.1155\/2020\/8818130"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Buntine, W., Grobelnik, M., Mladeni\u0107, D., and Shawe-Taylor, J. (2009). The Feature Importance Ranking Measure. Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-642-04180-8"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., and Ifrim, G. (2019). Visualizing the Feature Importance for Black Box Models. Machine Learning and Knowledge Discovery in Databases, Springer International Publishing.","DOI":"10.1007\/978-3-030-10928-8"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Du, P., Bai, X., Tan, K., Xue, Z., Samat, A., Xia, J., Li, E., Su, H., and Liu, W. (2020). Advances of Four Machine Learning Methods for Spatial Data Handling: A Review. J. Geovis. Spat. Anal., 4.","DOI":"10.1007\/s41651-020-00048-5"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"149890","DOI":"10.1109\/ACCESS.2019.2947359","article-title":"Feature Learning Viewpoint of Adaboost and a New Algorithm","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, W., and Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conf. Ser. Mater. Sci. Eng., 324.","DOI":"10.1088\/1757-899X\/324\/1\/012049"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.gaitpost.2009.07.002","article-title":"Simultaneous Estimation of Effects of Gender, Age and Walking Speed on Kinematic Gait Data","volume":"30","author":"Skare","year":"2009","journal-title":"Gait Posture"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2840\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:56Z","timestamp":1760136596000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/2840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22082840"],"URL":"https:\/\/doi.org\/10.3390\/s22082840","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]}}}