HUMAN ACTIVITY RECOGNITION, REAL-TIME RISK ANALYSIS, AND EVENT-STREAM GOVERNANCE: INTEGRATIVE FRAMEWORKS FOR WEARABLE SENSING AND ENTERPRISE RISK MANAGEMENT

Abstract

Background: The proliferation of wearable sensors and advances in machine learning have enabled human activity recognition (HAR) systems to reach levels of granularity and reliability previously unattainable. Simultaneously, the rise of event-stream processing and event sourcing architectures has reshaped how organizations perform real-time risk analysis and governance. Despite parallel progress in these domains, cross-disciplinary frameworks that unify sensor-level HAR, edge-to-cloud event streaming, and enterprise governance, risk, and compliance (GRC) practices remain underdeveloped.

 Objective: This article synthesizes evidence from sensor-design studies, HAR datasets and algorithms, event-streaming technologies, and governance practice literature to propose an integrative conceptual and methodological framework that supports reliable, privacy-aware, and operationally actionable HAR-driven risk analytics.

 Methods: We conduct a theory-driven synthesis grounded in empirical studies of accelerometer placement and datasets (Logacjov et al., 2021; Cleland et al., 2013; Stewart et al., 2018; Bao & Intille, 2004; Olguín & Pentland, 2006), algorithmic comparisons of ensemble learning and deep models (Abid et al., 2021; Hoang & Pietrosanto, 2022), engineering design for wearable systems (Nachiar et al., 2020), and event-stream processing and governance resources including Kafka event-sourcing (Kesarpu & Dasari, 2025), RisingWave surveys (RisingWave, 2024), and GRC guidance (LeanIX; Pathlock, 2025). From these sources we derive architectural patterns, data-flow principles, and evaluation criteria.

 Results: We articulate a layered architecture that couples multi-sensor HAR pipelines with robust event-sourcing and policy-aware GRC modules. The architecture emphasizes (a) sensor placement and calibration best practices to maximize signal fidelity, (b) hybrid modeling strategies—ensemble and deep learning—to balance accuracy and interpretability, (c) stream-first engineering using Kafka-style event sourcing and modern event processors for low-latency analytics, and (d) governance mechanisms for schema management, privacy, and auditability. We describe evaluation protocols for operational deployment including latency–accuracy trade-off analyses, model drift detection, and risk-score validation.

 Conclusions: Integrating HAR systems with event-stream processing and formalized governance produces practical benefits for real-time risk detection and decision support in health monitoring, occupational safety, and context-aware services. However, careful attention to sensor economics, model generalizability, privacy regulation, and organizational adoption pathways is essential. We conclude with a research agenda that prioritizes longitudinal field evaluation, explainable hybrid-model development, and prescriptive governance tooling.