Human activity recognition by using convolutional neural network

Kim, Hankil; Lee, Sungock; Jung, Hoe-Kyung

doi:10.11591/ijece.v9i6.pp5270-5276

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…Since activity recognition learns and classifies images or patterns, it is important to consider the method of collecting images or data. Sometimes the data were collected with a depth camera consisting of RGB-D sensors [18]; another study employed a thermal camera capable of recognizing actions regardless of day or night [35]; in a study that implemented gesture recognition for smart home automation [41], they used Channel State Information (CSI) time-series data generated by performing gestures in front of a Wi-Fi router instead of a camera. The techniques for activity recognition are mainly based on supervised learning that matches and classifies data and designates labels of activities.…”

Section: Data Extractionmentioning

confidence: 99%

Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Jiménez

Villalba-Mora

2022

Computers

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In recent years, research on convolutional neural networks (CNN) and recurrent neural networks (RNN) in deep learning has been actively conducted. In order to provide more personalized and advanced functions in smart home services, studies on deep learning applications are becoming more frequent, and deep learning is acknowledged as an efficient method for recognizing the voices and activities of users. In this context, this study aims to systematically review the smart home studies that apply CNN and RNN/LSTM as their main solution. Of the 632 studies retrieved from the Web of Science, Scopus, IEEE Explore, and PubMed databases, 43 studies were selected and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. In this paper, we examine which smart home applications CNN and RNN/LSTM are applied to and compare how they were implemented and evaluated. The selected studies dealt with a total of 15 application areas for smart homes, where activity recognition was covered the most. This study provides essential data for all researchers who want to apply deep learning for smart homes, identifies the main trends, and can help to guide design and evaluation decisions for particular smart home services.

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Section: Data Extractionmentioning

confidence: 99%

Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Jiménez

Villalba-Mora

2022

Computers

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“…Problems between smart farms and data include: It is difficult to obtain enough data to be used for learning, and even the provided data has an adverse effect on learning due to unbalanced data per class [20][21][22][23]. Based on these problems, this paper proposes a method of amplifying the amount of data through a generative antagonistic neural network and generating data of various classes to solve the data imbalance between classes.…”

Section: Problems and Solutionsmentioning

confidence: 99%

Fruit tree disease classification system using generative adversarial networks

Kim

Lee

Jung

2021

IJECE

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Smart farm refers to a farm that can remotely and automatically maintain proper growth and management of crops and livestock by integrating technology with agriculture. Currently, smart farms are concentrated in the field of smart horticulture, and although spreading research is being conducted in limited spaces. In addition, it is difficult to obtain a sufficient amount of data to be used for learning, and there is a problem that data imbalance occurs because it is difficult to obtain a similar amount for each class. In this paper, we propose a method to amplify a small amount of data and to solve the problems of imbalance data by using a feature that can learn to mimic the data of a generative adversarial network. The proposed method can create dataset of various crops and also show high hit rate. Dataset generated from crops would be used to solve problems of data imbalance by learning.

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“…Thereafter, the upsampling process is performed to obtain the divided image. In this paper, U-Net is designed by adopting the above structure [23][24][25].…”

Section: U-net Designmentioning

confidence: 99%

Pine wilt disease spreading prevention system using semantic segmentation

Hwang

Jeong²,

Jung

2021

IJECE

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Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There is no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learning-based semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease.

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Human activity recognition by using convolutional neural network

Cited by 14 publications

References 14 publications

Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Fruit tree disease classification system using generative adversarial networks

Pine wilt disease spreading prevention system using semantic segmentation

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