BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or ne...
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Gespeichert in:

Autor*in:	Heilym Ramirez [verfasserIn] Sergio A. Velastin [verfasserIn] Sara Cuellar [verfasserIn] Ernesto Fabregas [verfasserIn] Gonzalo Farias [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	activity recognition human skeleton pose estimation BERT computer vision

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 3, p 1400
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:3, p 1400

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23031400

Katalog-ID:	DOAJ080592678

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language	English
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callnumber-first	T - Technology
author	Heilym Ramirez
spellingShingle	Heilym Ramirez misc TP1-1185 misc activity recognition misc human skeleton misc pose estimation misc BERT misc computer vision misc Chemical technology BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
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topic_title	TP1-1185 BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN activity recognition human skeleton pose estimation BERT computer vision
topic	misc TP1-1185 misc activity recognition misc human skeleton misc pose estimation misc BERT misc computer vision misc Chemical technology
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title	BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
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author_browse	Heilym Ramirez Sergio A. Velastin Sara Cuellar Ernesto Fabregas Gonzalo Farias
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title_sort	bert for activity recognition using sequences of skeleton features and data augmentation with gan
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title_auth	BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
abstract	Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
abstractGer	Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
abstract_unstemmed	Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
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title_short	BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
url	https://doi.org/10.3390/s23031400 https://doaj.org/article/90f2396ec305492d8c2a528df6425a22 https://www.mdpi.com/1424-8220/23/3/1400 https://doaj.org/toc/1424-8220
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author2	Sergio A. Velastin Sara Cuellar Ernesto Fabregas Gonzalo Farias
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up_date	2024-07-03T15:28:46.380Z
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BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

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