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Related Experiment Video

Updated: May 1, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities.

Jungpil Shin1, Najmul Hassan1, Abu Saleh Musa Miah1

  • 1School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This survey reviews Machine Learning and Deep Learning for Human Activity Recognition (HAR) using diverse data types from 2014-2025. It details single and multi-modality techniques, fusion methods, and future research directions.

Keywords:
classificationdeep learning (DL)diverse modalityhuman activity recognition (HAR)machine learning (ML)vision and sensor based HAR

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Human Activity Recognition (HAR) systems are crucial for understanding human behavior.
  • HAR utilizes diverse data modalities like RGB, skeleton, audio, and radar signals.
  • Significant research exists on various HAR approaches across different modalities.

Purpose of the Study:

  • To provide a comprehensive survey of recent advancements in HAR from 2014 to 2025.
  • To focus on Machine Learning (ML) and Deep Learning (DL) techniques.
  • To categorize approaches based on input data modalities.

Main Methods:

  • Review of peer-reviewed research papers in English.
  • Categorization of HAR methods by single-modality and multi-modality techniques.
  • Analysis of fusion-based and co-learning frameworks, hand-crafted features, human-object interaction recognition, and activity detection.

Main Results:

  • Detailed dataset descriptions for each modality.
  • Summary of the latest HAR systems and their comparative results on benchmark datasets.
  • Mathematical derivations for evaluating deep learning models for each modality.

Conclusions:

  • Identification of trends and challenges in current HAR research.
  • Proposing effective future research directions in the field of HAR.
  • Highlighting the importance of multi-modality and advanced DL techniques for improved HAR performance.