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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 21; s. 6705
Hlavní autoři:	Bassani, Giulia, Avizzano, Carlo Alberto, Filippeschi, Alessandro
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 02.11.2025
Témata:	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
On-line přístup:	Získat plný text
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