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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.

Md Amran Hossen1, Pg Emeroylariffion Abas1

  • 1Faculty of Integrated Technologies, Universiti Brunei Darussalam, Bandar Seri Begawan BE 1410, Brunei.

Journal of Imaging
|March 26, 2025
PubMed
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A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Sensors (Basel, Switzerland)·2022
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This review covers human activity recognition (HAR) techniques, integrating sensor and vision data. It highlights deep learning advancements and challenges for real-world applications.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Human Activity Recognition (HAR) is crucial for applications like healthcare and security.
  • Diverse sensor modalities (wearables, vision, radar) are used for HAR.
  • Traditional and deep learning methods are applied to HAR.

Purpose of the Study:

  • To provide a comprehensive review of current HAR techniques.
  • To analyze the strengths and limitations of various sensor modalities and machine learning approaches.
  • To identify future research directions and ethical considerations in HAR.

Main Methods:

  • Review of sensor-based, vision-based, and hybrid HAR methodologies.
  • Analysis of traditional machine learning (supervised, unsupervised) and deep learning (CNNs, RNNs, attention) frameworks.
Keywords:
activity discoveryactivity recognitiondata fusiondeep learningemerging applicationsmachine learningsensor modalities

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  • Exploration of emerging technologies like radar and Wi-Fi CSI for HAR.

    • Main Results:

    • Deep learning, particularly CNNs and RNNs, shows significant promise in HAR.
    • Multimodal sensor fusion offers enhanced accuracy but faces integration challenges.
    • Environmental variability and model interpretability remain key challenges for HAR systems.

    Conclusions:

    • Future HAR research should focus on multimodal sensor fusion and adaptive models.
    • Edge computing integration is vital for real-time HAR analysis.
    • Addressing privacy and fairness is critical for the ethical deployment of HAR technologies.