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Updated: Oct 22, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

A Review on Computer Vision-Based Methods for Human Action Recognition.

Mahmoud Al-Faris¹, John Chiverton¹, David Ndzi²

¹School of Energy & Electronic Engineering, Faculty of Technology, University of Portsmouth, Portsmouth PO1 3DJ, UK.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This review explores human action recognition systems, detailing advances from hand-crafted methods to deep learning. It categorizes current research and datasets to guide future computer vision developments.

Keywords:

deep learning feature representation hand-crafted feature human action recognition

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Human action recognition is crucial for applications like surveillance and healthcare.
Accurate vision-based recognition remains a significant challenge in computer vision research.

Purpose of the Study:

To review recent advancements in human action recognition systems.
To categorize and analyze state-of-the-art methods and datasets.
To provide insights for future research directions.

Main Methods:

Categorization of methods from hand-crafted representations (holistic, local) to deep learning (discriminative, generative, multi-modality).
Presentation and analysis of common human action recognition datasets.
Comparative analysis of different approaches and data sources.

Main Results:

Identification of trends shifting from traditional feature engineering to deep learning models.
Overview of diverse datasets enabling robust model training and evaluation.
Synthesis of current research landscape and performance benchmarks.

Conclusions:

Deep learning and multi-modality approaches show significant promise for improved action recognition.
Standardized datasets and rigorous evaluation are key for progress.
Future research should focus on addressing complex actions and real-world variability.