A unified semantic framework for incident representation in heterogeneous wearable systems
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Abstract
Wearable computing systems generate heterogeneous fault reports originating from diverse hardware platforms, operating systems, and software stacks, which makes large-scale reliability analysis difficult. This study proposes a unified semantic framework for representing incident events in heterogeneous wearable systems. The framework introduces a hierarchical incident ontology that standardizes error descriptions across device firmware logs, operating system diagnostics, and application-level crash reports. A semantic alignment model based on contrastive representation learning is designed to map heterogeneous fault descriptions into a unified embedding space. The approach was evaluated using 312,457 incident records collected from four categories of wearable devices, including smartwatches, fitness trackers, AR glasses, and medical monitoring devices. The dataset covers 11 months of operational data from over 18,000 devices. Experimental results show that the proposed framework improves cross-device incident clustering purity from 72.1% to 90.6%, while reducing duplicate incident reports by 38.4%. In downstream failure analysis tasks, the normalized incident representation improves automated root cause identification accuracy by 21% compared with raw-text-based fault analytics. These results demonstrate that semantic standardization can significantly enhance reliability analytics and incident management in large-scale wearable ecosystems.
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