A Device-Agnostic Framework for Personalized Anomaly Detection in Wearable and Mobile Ecosystems: Applications in Personal Safety and Respiratory Health Surveillance
Abstract—This paper presents a reformulated, device-agnostic architectural framework for two interrelated applications: (i) personalized audio-based personal safety threat detection, and (ii) respiratory pattern anomaly detection for early-stage epidemiological screening. Originating from an ideation dialogue, the proposed concepts are critically re-examined against fundamental constraints in machine learning, embedded systems, privacy engineering, and sensor heterogeneity. We propose a generalized abstraction layer that decouples sensing modality from inference logic, enabling deployment across heterogeneous smart devices (wearables, smartphones, IoT nodes) without hardware-specific dependencies. We address the zero-positive-example training problem through a reformulation as one-class classification and temporal anomaly detection. Privacy is preserved via on-device federated feature extraction with no raw data transmission. We further analyze the practical limitations identified by domain experts and propose mitigations grounded in current literature. The framework is positioned as a foundational architecture rather than a deployable product, inviting interdisciplinary validation. Impact Statement—By introducing a sensing abstraction layer and reformulating personal safety and respiratory monitoring as one-class anomaly detection problems, this work provides a portable, privacy-preserving architecture that can operate across heterogeneous consumer devices without hardware-specific redesign. The framework addresses the zero-positive-example constraint inherent to rare-event detection and offers a structured research agenda for empirical validation, potentially accelerating the development of reliable, edge-deployed health and safety monitoring systems. Index Terms—Anomaly detection, one-class classification, device-agnostic computing, respiratory pattern analysis, personal safety systems, federated learning, wearable computing, edge inference.