A deep genetic algorithm for human activity recognition leveraging fog computing frameworks

Subramanian, R. Raja; Vasudevan, V.

doi:10.1016/j.jvcir.2021.103132

Cited by 31 publications

(9 citation statements)

References 33 publications

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“…In view of this, a method combining data-driven and knowledge-driven was proposed in reference [35]. Indeed, some scholars used deep genetic algorithms or transfer learning for HAR to reduce model performance loss and improve recognition accuracy [36], [37].…”

Section: Other Related Methodsmentioning

confidence: 99%

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

2022

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In view of the excellent portability and privacy protection of wearable sensor devices, human activity recognition (HAR) of wearable devices has increased applications in human-computer interaction, health care, etc. Therefore, it is necessary to recognize various human activities accurately and efficiently. In this paper, we propose a multi-channel convolutional neural network with data augmentation for HAR, denoted AMC-CNN. First, the sliding windows in time series are used to construct the feature window, and then the feature window is augmented by data transformation and data addition. Through the horizontal connection of the augmented feature windows, the reconstructed feature samples are obtained. Second, a relatively lightweight multi-channel convolutional neural network is designed. In order to mine deep correlations between sensor data more efficiently, we use reasonable convolution kernel settings to compute multi-channel convolutions and extract parallel multi-scale features, and then train the model to recognize human activities. Finally, experiments are carried out on WISDM and MHEALTH datasets to verify the effectiveness of the data augmentation. Moreover, the other three models are adopted for comparative experiments. The evaluation indexes show that the method proposed in this paper has a better recognition effect, which verifies that the AMC-CNN model is effective in both single-sensor and multi-sensor HAR.INDEX TERMS Multi-channel convolutional neural network, data augmentation, human activity recognition, wearable devices.

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Section: Other Related Methodsmentioning

confidence: 99%

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

2022

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“…The authors in [100] proposed a methodology for identifying human activity on a real-time basis. The suggested activity recognition model includes a one-of-a-kind change SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams, was introduced by the authors of [98].…”

Section: Use Case 2: Intelligent Multimedia Processing On Edge For Hu...mentioning

confidence: 99%

“…The authors in [100] proposed a methodology for identifying human activity on a real-time basis. The suggested activity recognition model includes a one-of-a-kind change detection module.…”

Section: Use Case 2: Intelligent Multimedia Processing On Edge For Hu...mentioning

confidence: 99%

“…This can serve as a comprehensive reference and point researchers and readers towards further and future works. Use Case 2: Intelligent Multimedia Processing on the Edge for Human Computer Interaction (HCI) and Health [99][100][101][102][103][104][105][106][107][108][109][110][111] The remainder of the paper is structured as follows: Section 2 provides initial discussions and background information for the edge paradigm, multimedia information processing and artificial intelligence. The next five sections provide in-depth discussions on five areas of interest.…”

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confidence: 99%

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Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

et al. 2022

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

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“…Several human behaviors, including walking, sitting, riding a bike, jogging, eating, reading, and washing, are considered static or dynamic [3] . Training data for HAR are primarily derived from nonvisual wearable sensors and ambient assisted cameras (visual sensors) and a combination of both [4] , [5] .…”

Section: Introductionmentioning

confidence: 99%

AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design

Ismail¹,

Alsalamah²,

Hassan³

et al. 2023

Heliyon

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A deep genetic algorithm for human activity recognition leveraging fog computing frameworks

Cited by 31 publications

References 33 publications

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design

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