From controlled benchmarks to unconstrained environments: a pipeline-oriented survey of models, domains, and emerging trends on human activity recognition

Abstract

Human Activity Recognition (HAR) has become a central research topic in computer vision, machine learning, and pervasive computing, driven by its wide range of applications in intelligent surveillance, healthcare monitoring, human–computer interaction, and smart environments. Over the past two decades, the field has evolved from handcrafted feature-based approaches and classical statistical models to deep learning architectures and hybrid paradigms that combine representation learning with robust classifiers. This paper presents a comprehensive survey of HAR research from 2004 to February 2026, systematically organizing the literature according to a canonical processing pipeline that includes sensing modalities/domains, feature representations, temporal modeling strategies, learning paradigms, datasets, and evaluation protocols. Beyond a descriptive review, the survey provides a cross-analysis of the relationships between techniques, classifiers, datasets, and application domains, highlighting recurring design patterns, trade-offs, and performance implications. Particular attention is given to emerging trends such as hybrid deep–classical models, privacy-preserving HAR, device-free sensing, and edge-oriented deployments. By synthesizing methodological advances and practical considerations, this survey aims to serve both as an entry point for new researchers and as a decision-oriented reference for practitioners seeking to design robust, generalizable, and ethically responsible HAR systems.