{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:16:12Z","timestamp":1785413772302,"version":"3.56.0"},"reference-count":24,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2024YFB3309900"],"award-info":[{"award-number":["2024YFB3309900"]}]},{"name":"Science and Technology Innovation Key R&D Program of Chongqing","award":["CSTB2024TIAD-STX0023"],"award-info":[{"award-number":["CSTB2024TIAD-STX0023"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202401318"],"award-info":[{"award-number":["KJQN202401318"]}]},{"award":["KJQN202401318"],"award-info":[{"award-number":["KJQN202401318"]}],"id":[{"id":"https:\/\/ror.org\/031nm5713","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reliable wearable activity recognition requires not only a class label but also an auditable indication of whether that label is supported by historical sensor evidence. We present CC-NSIEA, a label-preserving neural-plus-rule-based class-contrast evidence audit for cross-subject wearable activity recognition. A Temporal Residual Perception Network supplies the sole activity label, posterior probabilities, and a normalized temporal embedding. A read-only Training-Subject Evidence Memory retrieves global, predicted-class, and competing-class records. A rule-based Evidence Consistency Audit combines data validity, dynamic\/static motion coherence, retrieval support, and class separation. When first-round evidence is insufficient, Class-Contrast Evidence Refinement performs one deterministic contrast between the predicted class and the strongest posterior competitor; the audit cannot change the neural label. The term neuro-symbolic is used only in this restricted architectural sense: a neural predictor is coupled to explicitly represent deterministic predicates and a finite rule-based controller; the method does not perform symbolic inference, theorem proving, or knowledge-graph reasoning. On five subject-disjoint outer folds of the UCI HAR official training partition, the shared perception model achieved 90.13% accuracy and 90.55% macro-F1 across 7352 out-of-fold windows from 21 subjects. Relative to a matched dynamic deterministic controller, CC-NSIEA increased Error AUPRC from 0.423802 to 0.433057 and reduced AURC from 0.035941 to 0.035913. The 10,000-resample subject-cluster bootstrap interval for the AUPRC difference was [0.001595, 0.019547]. CC-NSIEA provides an evidence-centered complement to confidence-based reliability estimation.<\/jats:p>","DOI":"10.3390\/s26144390","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:16:28Z","timestamp":1783689388000},"page":"4390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neuro-Symbolic Class-Contrast Evidence Audit for Reliable Cross-Subject Wearable Activity Recognition"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2750-2130","authenticated-orcid":false,"given":"Qiang","family":"Li","sequence":"first","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China"},{"name":"School of Mathematics and Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing 402160, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhirong","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing 402160, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"ref_1","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. 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