Deep Learning for Recognizing Human Activities Using Motions of Skeletal Joints

Phyo, Cho Nilar; Zin, Thi Thi; Tin, Pyke

doi:10.1109/tce.2019.2908986

Cited by 68 publications

(27 citation statements)

References 27 publications

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“…A HAR based on deep-learning technology using skeleton images of human actions as input data was proposed in [15]. In addition, the authors of [16] developed a system incorporating enhanced images for a skeleton motion history, as well as a HAR system based on images of the relative positions of joints which can work independently on the problem domain.…”

Section: Related Workmentioning

confidence: 99%

Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

2019

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Nowadays, with the emergence of sophisticated electronic devices, human daily activities are becoming more and more complex. On the other hand, research has begun on the use of reliable, cost-effective sensors, patient monitoring systems, and other systems that make daily life more comfortable for the elderly. Moreover, in the field of computer vision, human action recognition (HAR) has drawn much attention as a subject of research because of its potential for numerous cost-effective applications. Although much research has investigated the use of HAR, most has dealt with simple basic actions in a simplified environment; not much work has been done in more complex, real-world environments. Therefore, a need exists for a system that can recognize complex daily activities in a variety of realistic environments. In this paper, we propose a system for recognizing such activities, in which humans interact with various objects, taking into consideration object-oriented activity information, the use of deep convolutional neural networks, and a multi-class support vector machine (multi-class SVM). The experiments are performed on a publicly available cornell activity dataset: CAD-120 which is a dataset of human–object interactions featuring ten high-level daily activities. The outcome results show that the proposed system achieves an accuracy of 93.33%, which is higher than other state-of-the-art methods, and has great potential for applications recognizing complex daily activities.

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Section: Related Workmentioning

confidence: 99%

Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

2019

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“…Human activity recognition (HAR) can be referred to as the art of identifying and naming activities using Artificial Intelligence (AI) from the gathered activity raw data by utilizing various sources (so-called devices). Examples of such devices include wearable sensors (Pham et al 2020), electronic device sensors like smartphone inertial sensor (Qi et al 2018;Zhu et al 2019), camera devices like Kinect (Wang et al 2019a;Phyo et al 2019), closed-circuit television (CCTV) (Du et al 2019), and some commercial off-theshelf (COTS) equipment's (Ding et al 2015;Li et al 2016). The use of diverse sources makes HAR important for multifaceted applications domains, such as healthcare (Pham et al 2020;Zhu et al 2019;Wang et al 2018), surveillance (Thida et al 2013;Deep and Zheng 2019;Vaniya and Bharathi 2016;Shuaibu et al 2017;Beddiar et al 2020) remote care to elderly people living alone (Phyo et al 2019;Deep and Zheng 2019;, smart home/office/city (Zhu et al 2019;Deep and Zheng 2019;Fan et al 2017), and various monitoring application like sports, and exercise (Ding et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Human activity recognition in artificial intelligence framework: a narrative review

et al. 2022

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Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10116-x.

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“…Deep learning is a learning model that helps students develop advanced thinking based on comprehension learning, efficiently solve practical problems, and thus critically learn new knowledge and ideas and integrate them into the original knowledge structure [6,7]. is learning model places great emphasis on critical understanding, guiding students to scientifically integrate information and further reconstruct the body of knowledge [8]. is learning model incorporates learning objectives that allow students not only to know and understand but also to achieve advanced cognition, to reach advanced levels of cognition, to apply analysis, synthesis, and evaluation, to apply their knowledge flexibly and scientifically, and to develop advanced higher-order thinking [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Music Teaching and Creation Based on Deep Learning

Liu

2021

Mobile Information Systems

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Under the background of quality education, music learning is also changing, from the original shallow learning to deep learning gradually. In-depth learning is a new teaching concept, which pays full attention to students’ perception and exploration of music so that students can fully experience the charm of music. It can not only help students master more music knowledge and improve their music skills but also cultivate students’ music literacy and enhance their music ability (Świechowski, 2015). Therefore, in junior high school music teaching, teachers should actively apply the deep learning model and then improve the teaching level and comprehensively cultivate students’ music literacy (Whitenack and Swanson, 2003). In this paper, two convolution-based deep learning models, Breath1d and Breath2d, were designed and constructed, and a multilayer perceptron (MLP) was used as a benchmark method for performance evaluation, and a long short-term memory (LSTM) network is applied for the classification task. This paper discusses the value and application strategies of deep learning in junior high school music teaching and hopes to provide some reference for all educational colleagues (Zhang and Nauman 2020).

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Deep Learning for Recognizing Human Activities Using Motions of Skeletal Joints

Cited by 68 publications

References 27 publications

Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

Human activity recognition in artificial intelligence framework: a narrative review

Research on Music Teaching and Creation Based on Deep Learning

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