{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:56:58Z","timestamp":1790614618178,"version":"4.1.0"},"reference-count":12,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"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>This paper presents a preliminary smartphone-based inertial measurement unit (IMU) system for recognizing three common motion types, namely walking, running, and cycling, under different carrying modes. The proposed system is implemented on an Android smartphone and uses accelerometer and gyroscope signals to support both offline evaluation and on-device recognition. The current dataset contains six motion-carrying combinations formed by three motion types and two carrying modes, with more than 15 data segments collected for each category. Each segment lasts approximately 40\u201360+ s and was collected on flat ground. The raw sensor streams were processed using a sampling rate of 50 Hz, an 8 s sliding window, and a 4 s step size. To reduce data leakage, session-level splitting was adopted in the offline experiments. A K-nearest neighbor (KNN) classifier was used as the baseline recognition model, and different sensor combinations and feature settings were compared. The best offline configuration, using fused accelerometer and gyroscope features with the enhanced feature set and K = 3, achieved an accuracy of 86.57% and a macro-F1 score of 88.42%. On-device tests further showed stable recognition performance under hand-held conditions, while performance decreased under an unseen briefcase-style carrying condition, especially for walking and running. These results indicate that the proposed system is feasible as a preliminary smartphone-based motion recognition pipeline. At the current stage, the study should be interpreted as a pilot-scale feasibility evaluation under limited and partially unbalanced multi-subject data conditions, rather than a strict subject-independent generalization study. The findings nevertheless highlight the importance of carrying-mode diversity and more rigorous cross-subject evaluation in future work.<\/jats:p>","DOI":"10.3390\/s26196100","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:57:57Z","timestamp":1790611077000},"page":"6100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Preliminary Smartphone-Based IMU System for Walking, Running, and Cycling Recognition Under Different Carrying Modes"],"prefix":"10.3390","volume":"26","author":[{"given":"Longyi","family":"Tang","sequence":"first","affiliation":[{"name":"College of Software Engineering, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youli","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binyu","family":"Yan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University, Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lima, W.S., Souto, E., El-Khatib, K., Jalali, R., and Gama, J. (2019). Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview. Sensors, 19.","DOI":"10.3390\/s19143213"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"175176","DOI":"10.1109\/ACCESS.2024.3498852","article-title":"A Novel Framework for Daily Life Activity and Context Recognition In-The-Wild Using Smartphone Inertial Sensors","volume":"12","author":"Noorani","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5392","DOI":"10.1038\/s41598-024-56123-0","article-title":"Unlocking the Potential of Smartphone and Ambient Sensors for ADL Detection","volume":"14","author":"Stojchevska","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_4","first-page":"69","article-title":"Smartphone Based Human Activity Recognition Irrespective of Usage Behavior Using Deep Learning Technique","volume":"17","author":"Kundu","year":"2025","journal-title":"Int. J. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17127","DOI":"10.1109\/JSEN.2024.3380076","article-title":"A Human Activity Recognition Scheme Using Mobile Smartphones Based on Varying Orientations and Positions","volume":"24","author":"Zhang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Coskun, D., Incel, O.D., and Ozgovde, A. (2015, January 7\u20139). Phone Position\/Placement Detection Using Accelerometer: Impact on Activity Recognition. Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore.","DOI":"10.1109\/ISSNIP.2015.7106915"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ebner, M., Fetzer, T., Bullmann, M., Deinzer, F., and Grzegorzek, M. (2020). Recognition of Typical Locomotion Activities Based on the Sensor Data of a Smartphone in Pocket or Hand. Sensors, 20.","DOI":"10.3390\/s20226559"},{"key":"ref_8","first-page":"447","article-title":"Fine-Grained Accelerometer-Based Smartphone Carrying States Recognition during Walking","volume":"8","author":"Fujinami","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Motani, K., Wong, K., and Kamijo, S. (2019, January 15\u201318). Classifying Human Activity and Smartphone Holding Mode Using Accelerometer and Gyroscope. Proceedings of the 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan.","DOI":"10.1109\/GCCE46687.2019.9015384"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rahn, V., Zhou, L., Klieme, E., and Arnrich, B. (2021). Optimal Sensor Placement for Human Activity Recognition with a Minimal Smartphone\u2013IMU Setup. Proceedings of the 10th International Conference on Sensor Networks, SciTePress.","DOI":"10.5220\/0010269100370048"},{"key":"ref_11","first-page":"29","article-title":"Feature-Level Fusion Multi-Sensor Aggregation Temporal Network for Smartphone-Based Human Activity Recognition","volume":"12","author":"Pang","year":"2024","journal-title":"J. Telecommun. Digit. Econ."},{"key":"ref_12","first-page":"352","article-title":"Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone","volume":"14","author":"Sangisetti","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:59:07Z","timestamp":1790611147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,26]]},"references-count":12,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196100"],"URL":"https:\/\/doi.org\/10.3390\/s26196100","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,26]]}}}