{
  "abstract": "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→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–target 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→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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia",
    "LR11TIC04, Communication Networks and Security Research Lab, and LR11TIC02, Green and Smart Communication Systems Research Lab, Higher School of Communication of Tunis (SUP’COM), University of Carthage, Ariana, Tunisia",
    "College of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia"
  ],
  "article_type": "Research Article",
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  "author_total": 5,
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      "name": "Sarra Ayouni"
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      "name": "Maha Sliti"
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    {
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        2
      ],
      "name": "Abeer Abdulaziz Alghanem"
    },
    {
      "name": "Fatimah Alhayan"
    },
    {
      "name": "Mohamed Maddeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dkhxkhzmrkp8t.bib",
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  "doi": "10.1371/journal.pone.0359300",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "1707|Context-Aware Activity Recognition Systems"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359300&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/dkhxkhzmrkp8t.ris",
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    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359300",
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  "subjects": [
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  "title": "Compact target-aware stability-based feature selection for cross-dataset wearable activity monitoring",
  "title_text": "Compact target-aware stability-based feature selection for cross-dataset wearable activity monitoring",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/dkhxkhzmrkp8t",
  "venue": "PLOS One",
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