Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information

Tasnim, Nusrat; Islam, Md. Mahbubul; Baek, Joong-Hwan

doi:10.3390/inventions5030049

Cited by 16 publications

(5 citation statements)

References 33 publications

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“…Regardless of the skeleton type, it can be used for shape classifications, comparisons, and recognition. Various skeleton applications have been reported [28][29][30][31][32][33].…”

Section: Characterisation Methods and Skeletonsmentioning

confidence: 99%

Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm

et al. 2023

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The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape’s points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape’s characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes.

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“…Regardless of the skeleton type, it can be used for shape classifications, comparisons, and recognition. Various skeleton applications have been reported [28][29][30][31][32][33].…”

Section: Characterisation Methods and Skeletonsmentioning

confidence: 99%

Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm

et al. 2023

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“…Tasnim et al [42] proposed a DCNN model to train the feature vector transformed from the coordinates of the joints along the X, Y, and Z axes. The joints of each frame ith are represented by F i (X ij , Y ij , Z ij ), where j is the joint number.…”

Section: Cnn-basedmentioning

confidence: 99%

Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study

Nguyen¹,

Nguyen²,

Scherer

et al. 2023

Sensors

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Human activity recognition (HAR) is an important research problem in computer vision. This problem is widely applied to building applications in human–machine interactions, monitoring, etc. Especially, HAR based on the human skeleton creates intuitive applications. Therefore, determining the current results of these studies is very important in selecting solutions and developing commercial products. In this paper, we perform a full survey on using deep learning to recognize human activity based on three-dimensional (3D) human skeleton data as input. Our research is based on four types of deep learning networks for activity recognition based on extracted feature vectors: Recurrent Neural Network (RNN) using extracted activity sequence features; Convolutional Neural Network (CNN) uses feature vectors extracted based on the projection of the skeleton into the image space; Graph Convolution Network (GCN) uses features extracted from the skeleton graph and the temporal–spatial function of the skeleton; Hybrid Deep Neural Network (Hybrid–DNN) uses many other types of features in combination. Our survey research is fully implemented from models, databases, metrics, and results from 2019 to March 2023, and they are presented in ascending order of time. In particular, we also carried out a comparative study on HAR based on a 3D human skeleton on the KLHA3D 102 and KLYOGA3D datasets. At the same time, we performed analysis and discussed the obtained results when applying CNN-based, GCN-based, and Hybrid–DNN-based deep learning networks.

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“…In comparison to prior systems, some researchers use depth cameras such as the Kinect sensor, a low-cost consumer-grade 3D camera, to extract key points on the human body [12]. This technology can contribute to the provision of several human action characteristics, such as depth, colour differentiation, and human skeletal structure [13]. The x and y coordinates, as well as the confidence value, were recorded for each body key point, and a 2-Dimensional (2D) human movement estimate can be performed using the OpenPose algorithm [14].…”

Section: A Human Body and Movement Trackingmentioning

confidence: 99%

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Hisham¹,

Hassan²,

Ibrahim³

et al. 2022

IJACSA

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Human action analysis is an enthralling area of research in artificial intelligence, as it may be used to improve a range of applications, including sports coaching, rehabilitation, and monitoring. By forecasting the body's vital position of posture, human action analysis may be performed. Human body tracking and action recognition are the two primary components of video-based human action analysis. We present an efficient human tracking model for squat exercises using the open-source MediaPipe technology. The human posture detection model is used to detect and track the vital body joints within the human topology. A series of critical body joint motions are being observed and analysed for aberrant body movement patterns while conducting squat workouts. The model is validated using a squat dataset collected from ten healthy people of varying genders and physiques. The incoming data from the model is filtered using the double exponential smoothing method;the Mean Squared Error between the measured and smoothed angles is determined to classify the movement as normal or abnormal. Level smoothing and trend control have parameters of 0.8928 and 0.77256, respectively. Six out of ten subjects in the trial were precisely predicted by the model. The mean square error of the signals obtained under normal and abnormal squat settings is 56.3197 and 29.7857, respectively. Thus, by utilising a simple threshold method, the low-cost camera-based squat movement condition detection model was able to detect the abnormality of the workout movement.

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Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information

Cited by 16 publications

References 33 publications

Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm

Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm

Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

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