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

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse...

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Vydané v:Sensors (Basel, Switzerland) Ročník 25; číslo 21; s. 6705
Hlavní autori: Bassani, Giulia, Avizzano, Carlo Alberto, Filippeschi, Alessandro
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 02.11.2025
Predmet:
Adult
Algorithms
autoencoder
Classification
Comparative analysis
convolutional neural network (CNN)
Datasets
Deep Learning
Embedded systems
Human Activities
human activity recognition (HAR)
Humans
Information management
manual material handling (MMH)
Materials handling
Neural networks
Neural Networks, Computer
recurrent neural network (RNN)
Sensors
Wearable Electronic Devices
wearable sensor network (WSN)
New York
United States
human activity recognition (HAR)
recurrent neural network (RNN)
manual material handling (MMH)
autoencoder
convolutional neural network (CNN)
wearable sensor network (WSN)
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires ×57.1 and ×31.3 more Multiply and ACcumulate (MAC) and ×100.8 and ×28.3 more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25216705