{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:02:16Z","timestamp":1791396136496,"version":"4.3.3"},"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PRFI- 2026"],"award-info":[{"award-number":["PRFI- 2026"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Wearable activity-recognition models may lose reliability when transferred across participants, sensor placements, sampling rates, acquisition protocols, and activity definitions. This study evaluates a compact unsupervised target-aware feature-selection strategy for cross-dataset wearable activity monitoring under a pre-specified MHEALTH\n \n \n \n \n \n \u2192<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n PAMAP2 transfer protocol. Five-second wearable-signal windows were represented using 270 handcrafted inertial and magnetic descriptors. Source-domain mutual-information relevance was then combined with unlabeled source\u2013target descriptor stability to select a compact set of 48 features, after which lightweight classifiers were trained using MHEALTH labels only. The original activities were harmonized into three coarse states: sedentary, active, and vigorous, while harmonized PAMAP2 target labels were withheld from model development and used only for final evaluation. Under leave-one-subject-out evaluation on MHEALTH, the Random Forest classifier achieved 0.9985 accuracy and 0.9984 macro-F1. In the primary cross-dataset experiment, the pre-specified stability-aware configuration achieved 0.9042 accuracy and 0.8753 macro-F1 on PAMAP2, compared with 0.8888 accuracy and 0.8436 macro-F1 for the full 270-feature representation and 0.8734 accuracy and 0.7940 macro-F1 for source-mutual-information-only selection. With all operating values frozen, reverse PAMAP2\n \n \n \n \n \n \u2192<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n MHEALTH transfer also ranked Stable-48 highest (accuracy 0.5811; macro-F1 0.4400), ahead of Source-MI-48 (0.5720; 0.4320) and Full-270 (0.5445; 0.4257), although Holm-adjusted participant-level contrasts were not significant. The compact configuration also achieved higher complete-window accuracy and macro-F1 than the tested shallow target-feature transformations and feature-level MLP adaptation baselines within the same handcrafted descriptor space. These findings indicate that jointly considering source-domain relevance and unlabeled cross-domain descriptor stability can improve compact cross-dataset wearable activity monitoring. The conclusions remain specific to the evaluated protocols and activity taxonomies and do not establish general superiority over raw-signal deep-learning methods or clinical readiness.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0359300","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:42:51Z","timestamp":1791394971000},"page":"e0359300","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Compact target-aware stability-based feature selection for cross-dataset wearable activity monitoring"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5662-6238","authenticated-orcid":true,"given":"Sarra","family":"Ayouni","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University","place":["Riyadh 11671, Saudi Arabia"],"department":["Department of Information 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