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Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks.

Giulia Bassani1,2, Carlo Alberto Avizzano1,2, Alessandro Filippeschi1,2

  • 1Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna, 56124 Pisa, Italy.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models like BiLSTM and RCNN show promise for recognizing manual material handling activities using wearable sensors. These computationally lighter algorithms offer comparable performance to complex models, aiding in ergonomic risk assessment.

Keywords:
autoencoderconvolutional neural network (CNN)human activity recognition (HAR)manual material handling (MMH)recurrent neural network (RNN)wearable sensor network (WSN)

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Area of Science:

  • Occupational Health and Safety
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Human Activity Recognition (HAR) is vital in healthcare but underutilized for Manual Material Handling (MMH).
  • MMH activities significantly impact worker health and safety.
  • Existing HAR research rarely addresses the specific challenges of MMH.

Purpose of the Study:

  • To develop and evaluate Deep Learning algorithms for HAR in MMH.
  • To compare the performance and computational complexity of different HAR models.
  • To identify efficient algorithms suitable for real-time ergonomic risk assessment.

Main Methods:

  • Proposed four Deep Learning algorithms: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN).
  • Utilized wearable sensor data from 14 subjects to train and test models.
  • Compared performance using F1-score, 70-30% split, and Leave-One-Subject-Out (LOSO) validation, alongside computational complexity metrics (MAC, MA).

Main Results:

  • BiLSTM achieved the highest classification performance (95.7% on 70-30% split, 90.3% on LOSO).
  • RCNN demonstrated similar performance (95.9%; 89.2%) with improved results as subject numbers increased.
  • DeepConvLSTM, while performing comparably, was significantly more computationally complex than BiLSTM and RCNN.

Conclusions:

  • BiLSTM and RCNN offer a strong balance of classification accuracy and computational efficiency for MMH.
  • These lighter models are suitable for embedded systems and automatic ergonomic risk assessment.
  • Facilitates personalized risk assessment and enhances safety in industrial MMH practices.