ADAPTIVE EVENT-SOURCED FRAMEWORKS FOR REAL-TIME HUMAN ACTIVITY RISK ANALYSIS: INTEGRATING INTERPRETABLE MACHINE LEARNING, CONTINUAL LEARNING, AND MEMS SENSING FOR ROBUST DEPLOYMENT

Abstract

This article advances a comprehensive, integrative framework for real-time human activity risk analysis by synthesizing event-sourced architectures, interpretable machine learning, continual learning approaches, and inertial sensing optimization. The work situates Kafka-style event sourcing as the architectural spine for low-latency, high-throughput telemetry ingestion and stateful stream processing and pairs it with MEMS accelerometer and inclinometer measurement techniques to maximize signal fidelity for human activity recognition. Drawing on prior empirical and methodological studies of human activity classification with smartphone and inertial sensors, on optimization methods for orientation and displacement measurements, and on modern operational practices in MLOps and drift detection, the article articulates a methodology that preserves interpretability while enabling production-scale adaptation to covariate drift and concept drift. The method emphasizes multiple-instance learning for sparse transactional contexts, drift-aware model lifecycle management via serverless and continual learning patterns, and uses explainable model families and post-hoc explanation methods to ensure traceability and regulatory compliance. Results presented are descriptive, synthesizing insights from the cited literature and projecting expected behaviors under various deployment regimes. The discussion probes limitations, trade-offs between latency and model complexity, operational risks, and proposes future research directions encompassing federated event-sourced learning, privacy-preserving telemetry, and tighter sensor–algorithm co-design. This article aims to be a reference for researchers and practitioners seeking to design production-ready risk analysis pipelines that are both rigorous and interpretable.