{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T07:58:53Z","timestamp":1780559933777,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"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>Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.<\/jats:p>","DOI":"10.3390\/s22041678","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds"],"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"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","first-page":"e4062","DOI":"10.1002\/dac.4062","article-title":"GUIDE: Smartphone Sensors-Based Pedestrian Indoor Localization with Heterogeneous Devices","volume":"32","author":"Ashraf","year":"2019","journal-title":"Int. J. Commun. Syst."},{"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), Tokyo, 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","first-page":"1391","DOI":"10.1038\/s41598-021-81009-w","article-title":"Estimation of Stride-by-Stride Spatial Gait Parameters Using Inertial Measurement Unit Attached to the Shank with Inverted Pendulum Model","volume":"11","author":"Mao","year":"2021","journal-title":"Sci. Rep."},{"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","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_14","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_15","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_16","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 2012 IEEE\/ION Position, Location and Navigation Symposium, Myrtle Beach, SC, USA.","DOI":"10.1109\/PLANS.2012.6236875"},{"key":"ref_17","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_18","unstructured":"Weinberg, H. (2002). Using the ADXL202 in Pedometer and Personal Navigation Applications, Analog Devices. Analog Devices AN-602 Application Note."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"e6091261","DOI":"10.1155\/2017\/6091261","article-title":"Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm","volume":"2017","author":"Xing","year":"2017","journal-title":"J. Sens."},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/S1474-4422(19)30044-4","article-title":"Gait Impairments in Parkinson\u2019s Disease","volume":"18","author":"Mirelman","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Tachos, N., Christodoulakis, G., Maniadi, E., Kefalopoulou, Z., Fotiadis, D.I., and Tsiknakis, M. (2021). The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson\u2019s Patients. Sensors, 21.","DOI":"10.3390\/s21082821"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e19068","DOI":"10.2196\/19068","article-title":"Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study","volume":"22","author":"Evers","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Haji Ghassemi, N., Hannink, J., Martindale, C.F., Ga\u00dfner, H., M\u00fcller, M., Klucken, J., and Eskofier, B.M. (2018). Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson\u2019s Disease. Sensors, 18.","DOI":"10.3390\/s18010145"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/JBHI.2015.2461555","article-title":"An Emerging Era in the Management of Parkinson\u2019s Disease: Wearable Technologies and the Internet of Things","volume":"19","author":"Pasluosta","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.neucom.2020.08.079","article-title":"Wearables-Based Multi-Task Gait and Activity Segmentation Using Recurrent Neural Networks","volume":"432","author":"Martindale","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kluge, F., Ga\u00dfner, H., Hannink, J., Pasluosta, C., Klucken, J., and Eskofier, B.M. (2017). Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters. Sensors, 17, Available online: https:\/\/www.mad.tf.fau.de\/research\/activitynet\/sensor-based-gait-analysis-validation-data-kluge-et-al-2017\/.","DOI":"10.3390\/s17071522"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.gaitpost.2016.09.023","article-title":"Evaluation of the Performance of Accelerometer-Based Gait Event Detection Algorithms in Different Real-World Scenarios Using the MAREA Gait Database","volume":"51","author":"Khandelwal","year":"2017","journal-title":"Gait Posture"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Martindale, C.F., Roth, N., Hannink, J., Sprager, S., and Eskofier, B.M. (, January March). Smart Annotation Tool for Multi-Sensor Gait-Based Daily Activity Data. Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Available online: https:\/\/www.mad.tf.fau.de\/research\/activitynet\/benchmark-cyclic-activity-recognition-database-using-wearables\/.","DOI":"10.1109\/PERCOMW.2018.8480193"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.gaitpost.2008.07.011","article-title":"Gait Analysis in Multiple Sclerosis: Characterization of Temporal-Spatial Parameters Using GAITRite Functional Ambulation System","volume":"29","author":"Givon","year":"2009","journal-title":"Gait Posture"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.gaitpost.2020.10.025","article-title":"A Comparison of Centre of Pressure Behaviour and Ground Reaction Force Magnitudes When Individuals Walk Overground and on an Instrumented Treadmill","volume":"83","author":"Hutchinson","year":"2021","journal-title":"Gait Posture"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lueken, M., ten Kate, W., Batista, J.P., Ngo, C., Bollheimer, C., and Leonhardt, S. (2019, January 19\u201322). Peak Detection Algorithm for Gait Segmentation in Long-Term Monitoring for Stride Time Estimation Using Inertial Measurement Sensors. Proceedings of the 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), Chicago, IL, USA.","DOI":"10.1109\/BHI.2019.8834542"},{"key":"ref_36","unstructured":"Zhao, N. (2010). Full-Featured Pedometer Design Realized with 3-Axis Digital Accelerometer, Analog Devices."},{"key":"ref_37","first-page":"1","article-title":"Stride Segmentation of Inertial Sensor Data Using Statistical Methods for Different Walking Activities","volume":"40","author":"Jain","year":"2021","journal-title":"Robotica"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1109\/TNSRE.2018.2868094","article-title":"Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures from Healthy Subjects","volume":"26","author":"Figueiredo","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Ibarra, J.C., Siqueira, A.A.G., and Krebs, H.I. (December, January 29). Adaptive Gait Phase Segmentation Based on the Time-Varying Identification of the Ankle Dynamics: Technique and Simulation Results. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224404"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Oudre, L., Barrois-M\u00fcller, R., Moreau, T., Truong, C., Vienne-Jumeau, A., Ricard, D., Vayatis, N., and Vidal, P.-P. (2018). Template-Based Step Detection with Inertial Measurement Units. Sensors, 18.","DOI":"10.3390\/s18114033"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vienne-Jumeau, A., Oudre, L., Moreau, A., Quijoux, F., Vidal, P.-P., and Ricard, D. (2019). Comparing Gait Trials with Greedy Template Matching. Sensors, 19.","DOI":"10.3390\/s19143089"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ji, N., Zhou, H., Guo, K., Samuel, O.W., Huang, Z., Xu, L., and Li, G. (2019). Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals. Sensors, 19.","DOI":"10.3390\/s19163462"},{"key":"ref_43","first-page":"197","article-title":"Identification of Gait Events Using Expert Knowledge and Continuous Wavelet Transform Analysis","volume":"Volume 4","author":"Khandelwal","year":"2014","journal-title":"Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102076","DOI":"10.1016\/j.bspc.2020.102076","article-title":"Discrete Wavelet Transform Based Data Representation in Deep Neural Network for Gait Abnormality Detection","volume":"62","author":"Chakraborty","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/TBME.2019.2955423","article-title":"Gait Cycle Validation and Segmentation Using Inertial Sensors","volume":"67","author":"Prateek","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Martindale, C.F., Sprager, S., and Eskofier, B.M. (2019). Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables. Sensors, 19.","DOI":"10.3390\/s19081820"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Martindale, C.F., Hoenig, F., Strohrmann, C., and Eskofier, B.M. (2017). Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models. Sensors, 17.","DOI":"10.3390\/s17102328"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/s12984-021-00883-7","article-title":"Hidden Markov Model Based Stride Segmentation on Unsupervised Free-Living Gait Data in Parkinson\u2019s Disease Patients","volume":"18","author":"Roth","year":"2021","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Angermann, M., Robertson, P., Kemptner, T., and Khider, M. (2010, January 15\u201317). A High Precision Reference Data Set for Pedestrian Navigation Using Foot-Mounted Inertial Sensors. Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland.","DOI":"10.1109\/IPIN.2010.5646839"},{"key":"ref_50","unstructured":"(2021, November 25). ELAN: A Professional Framework for Multimodality Research\u2014ACL Anthology. Available online: https:\/\/aclanthology.org\/L06-1082\/."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Qi, Y., Wang, W.K., Bent, B., Avram, R., Olgin, J., and Dunn, J. (2020). EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies. Sensors, 20.","DOI":"10.3390\/s20092700"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mueen, A., and Keogh, E. (2016, January 13\u201317). Extracting Optimal Performance from Dynamic Time Warping. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2945383"},{"key":"ref_53","unstructured":"(2021, December 12). Stream Monitoring under the Time Warping Distance. IEEE Conference Publication. IEEE Xplore. Available online: https:\/\/ieeexplore.ieee.org\/document\/4221753."},{"key":"ref_54","unstructured":"Zhao, J., and Itti, L. (2016). ShapeDTW: Shape Dynamic Time Warping. arXiv."},{"key":"ref_55","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"},{"key":"ref_56","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_57","unstructured":"Gu, F. (2018). Indoor Localization Supported by Landmark Graph and Locomotion Activity Recognition. [Ph.D. Thesis, University of Melbourne]."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"23","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_59","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TMC.2019.2960780","article-title":"Deep Neural Network Based Inertial Odometry Using Low-Cost Inertial Measurement Units","volume":"20","author":"Chen","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","article-title":"A Time Series Forest for Classification and Feature Extraction","volume":"239","author":"Deng","year":"2013","journal-title":"Inf. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:24:04Z","timestamp":1760135044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041678"],"URL":"https:\/\/doi.org\/10.3390\/s22041678","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}