{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:00:49Z","timestamp":1790071249861,"version":"4.0.1"},"reference-count":16,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"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>Future industrial human activity recognition (HAR) may require discrimination among many operational elements, including movements with partially overlapping kinematics. This controlled pilot study characterized recognition across 22 upper-body movement classes with varying structural similarity and examined classifier, signal scaling, and temporal window length. Sixteen adults performed the movements with an XSENS motion-capture system; eight upper-limb inertial measurement units provided 80 synchronous time-series channels. Thirteen participants were used for model development, and three whose observed execution patterns differed comparatively from the remainder were deliberately reserved as a challenge-oriented holdout. Support vector classifier (SVC), random forest (RF), Gaussian naive Bayes (NB), and long short-term memory (LSTM) models were evaluated with min\u2013max or maximum-absolute scaling and 62-, 93-, or 124-frame windows. RF with maximum-absolute scaling and a 124-frame window achieved the best aggregate holdout performance (accuracy = 0.950; F1 = 0.939). Importantly, this performance was obtained on three entirely unseen participants who were deliberately reserved because their observed execution patterns and fluency differed from those of the model-development participants, providing a controlled, challenge-oriented test of transfer across inter-individual execution variability. Nevertheless, strong aggregate performance did not translate into uniform class-level reliability, and prominent errors remained concentrated in specific movement pairs. These findings provide empirical evidence for both the participant-independent transfer capability and the class-specific limitations of motion-only recognition, supporting its role as a methodological precursor to future AI-assisted work study and human\u2013robot collaboration rather than as evidence of end-to-end industrial HAR.<\/jats:p>","DOI":"10.3390\/s26185835","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:16:48Z","timestamp":1789460208000},"page":"5835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Participant-Independent Recognition of 22 Upper-Body Movements Using Wearable IMUs: A Controlled Pilot Study Toward Fine-Grained Industrial HAR"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7587-6424","authenticated-orcid":false,"given":"Chih-Feng","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Industrial Management, Asia Eastern University of Science and Technology, No. 58, Sec. 2, Sichuan Rd., Banqiao District, New Taipei City 220303, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-0527","authenticated-orcid":false,"given":"Chiuhsiang Joe","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Da\u2019an Dist., Taipei City 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Qin-Xuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Da\u2019an Dist., Taipei City 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1017\/S0890060424000313","article-title":"Machine learning models for sequential motion recognition in human activity for logistics operations efficiency improvement","volume":"39","author":"Cheng","year":"2025","journal-title":"Artif. Intell. Eng. Des. Anal. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s00170-026-18865-1","article-title":"Continuous sub-action recognition in manual assembly using Kinect skeletal data and dynamic time warping for AI-assisted work study","volume":"146","author":"Cheng","year":"2026","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s00170-021-08319-1","article-title":"A smart operator advice model by deep learning for motion recognition in human-robot coexisting assembly line","volume":"119","author":"Wang","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jmsy.2023.03.001","article-title":"Classification of primitive manufacturing tasks from filtered event data","volume":"68","author":"Duarte","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111710","DOI":"10.1016\/j.engappai.2025.111710","article-title":"Graph-based framework for temporal human action recognition and segmentation in industrial context","volume":"159","author":"Benmessabih","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.jmsy.2026.04.010","article-title":"A multidimensional compliance monitoring and AR-guided method for error-prone assembly process of aviation angle pieces based on TCN-improved cross-modal action recognition and intelligent process perception","volume":"86","author":"He","year":"2026","journal-title":"J. Manuf. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103584","DOI":"10.1016\/j.displa.2026.103584","article-title":"Evidential reasoning-based action segmentation fusion model for industrial behavior procedures monitoring","volume":"95","author":"Huang","year":"2026","journal-title":"Displays"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Patruno, C., Alvear Gallon, M.V., Bono, A., and Cicirelli, G. (2026, January 13\u201316). Evaluating Input Data Structures for CNN-Based Assembly Action Recognition Using Projected Distance Features. Proceedings of the 2026 12th International Conference on Control, Decision and Information Technologies (CoDIT), Bari, Italy.","DOI":"10.1109\/CoDIT70676.2026.11630963"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104831","DOI":"10.1016\/j.apergo.2026.104831","article-title":"Vision-language models for occupational physical exposure assessment: Classification and temporal segmentation of manual material handling tasks","volume":"138","author":"Rajabi","year":"2026","journal-title":"Appl. Ergon."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20129","DOI":"10.1109\/JSEN.2026.3694967","article-title":"Fully Distance-Guided Refinement Graph Convolutional Networks for Skeleton-Based Human Action Recognition","volume":"26","author":"Ding","year":"2026","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"37387","DOI":"10.1007\/s11042-023-15022-8","article-title":"A deep learning model based on sequential object feature accumulation for sport activity recognition","volume":"82","author":"Ryu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, H., Feng, R., and Zhang, W. (2023). C-DTW for Human Action Recognition Based on Nanogenerator. Sensors, 23.","DOI":"10.3390\/s23167230"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mistry, J., and Inden, B. (2018, January 19\u201321). An Approach to Sign Language Translation using the Intel RealSense Camera. Proceedings of the 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, UK.","DOI":"10.1109\/CEEC.2018.8674227"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Hussain, F., Garcia, N.M., Lameski, P., and Zdravevski, E. (2020). Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification. Future Internet, 12.","DOI":"10.3390\/fi12110194"},{"key":"ref_15","first-page":"3238","article-title":"Seglearn: A python package for learning sequences and time series","volume":"19","author":"Burns","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Um, T.T., Babakeshizadeh, V., and Kuli\u0107, D. (2017, January 24\u201328). Exercise motion classification from large-scale wearable sensor data using convolutional neural networks. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206051"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5835\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:06:05Z","timestamp":1790067965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":16,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185835"],"URL":"https:\/\/doi.org\/10.3390\/s26185835","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}