Neuro-Symbolic Class-Contrast Evidence Audit for Reliable Cross-Subject Wearable Activity Recognition.

Overview

1. School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China; (Q.L.); (X.Z.)
2. School of Mathematics and Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing 402160, China

Abstract

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.

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Data

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Data Availability Statement

The UCI Human Activity Recognition Using Smartphones dataset is publicly available from the UCI Machine Learning Repository. The CC-NSIEA source code, configuration files, predeclared experimental protocol, unit tests, and execution instructions are available at: https://github.com/qzr011005-web/CC-NSIEA (accessed on 6 July 2026). The generated OOF predictions, KPC records, subject-cluster bootstrap outputs, fold-level summaries, paper tables, and execution logs are publicly available in GitHub Release v1.0.0 at: https://github.com/qzr011005-web/CC-NSIEA/releases/tag/v1.0.0 (accessed on 6 July 2026). The repository and release do not distribute the original UCI HAR data, trained checkpoints, cached outputs, or API credentials.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 4 MeSH terms, 3 funders, 12 references.

Cite

This paper

Li, Q., Qu, Z., Yan, M., & Zhang, X. (2026). Neuro-Symbolic Class-Contrast Evidence Audit for Reliable Cross-Subject Wearable Activity Recognition. Sensors (Basel, Switzerland), 26(14), 4390. https://doi.org/10.3390/s26144390

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