Action recognition using kinematics posture feature on 3D skeleton joint locations

Ahad, Md. Atiqur Rahman; Ahmed, Masud; Antar, Anindya Das; Makihara, Yasushi; Yagi, Yasushi

doi:10.1016/j.patrec.2021.02.013

Cited by 47 publications

(16 citation statements)

References 23 publications

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“…As can be seen from Figure 1 , the deep learning-based sport recognition model for mental health status is composed of a data layer, a logic layer, and a display layer. The data layer is used to obtain people’s sport data, real-time sport data, and health identification data and transmit the above data into the logic layer ( Ahad et al, 2021 ). After using the logic layer to perform mutual information feature extraction, exercise health pattern recognition, and exercise intensity logic calculation on people’s exercise data and health data, the relevant calculation results are inputted into the display layer to provide users with exercise project management, exercise intensity viewing, and exercise health model and other information, so that users can fully understand the effect of current exercise on their mental health.…”

Section: Recognition and Analysis Of Sports On Mental Healthmentioning

confidence: 99%

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Li²

2022

Front. Psychol.

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This paper presents the purpose of sport recognition of mental health for users and analyzes and studies the recognition of mental health by sports based on deep learning. The recognition model of sport mental health state composed of data layer, logic layer and display layer is built. After fusing human health data with deep learning algorithm, the feature of human health mutual information is extracted, the feature into the recognition model of mental health state is inputted, and the recognition results of sport mental health mode after forward and reverse operation are outputted. The recognition data of sports on mental health status are obtained, which correspond to the link flowing through during multi-level transmission, calibrate the multi-level transmission point, and fuse and process the recognition information of sports on mental health status. The experimental results show that the loss value of the research method when analyzing the effect of sports on mental health enhancement is the smallest, the output result is reliable, can effectively improve the body mass index (BMI) of the human body, has the most controllable amount of data, and has good performance.

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Section: Recognition and Analysis Of Sports On Mental Healthmentioning

confidence: 99%

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Li²

2022

Front. Psychol.

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“…Early Feature Fusion. Although neural networks can autonomously learn data features, many studies have indicated that early feature processing can improve the performances of models, so it is necessary to select distinctive features [44][45][46][47][48]. For example, inspired by the Lie group-based skeleton descriptor [44], Jiang et al [16] proposed a spatiotemporal skeleton transformation descriptor (ST-STD) to define the relative transformations of skeleton gestures, including rotation and translation during skeleton movement.…”

Section: Point Levelmentioning

confidence: 99%

“…For example, inspired by the Lie group-based skeleton descriptor [44], Jiang et al [16] proposed a spatiotemporal skeleton transformation descriptor (ST-STD) to define the relative transformations of skeleton gestures, including rotation and translation during skeleton movement. Ahad et al [45] used the linear joint position feature (LJPF) and angular joint position feature (AJPF) obtained based on the three-dimensional linear joint positions and angles between skeleton segments as distinctive features. Nie et al [9] proposed two new viewpointinvariant motion features: the Euler angle of joints (JEAs) and the Euclidean distance matrix between joints (JEDM).…”

Section: Point Levelmentioning

confidence: 99%

A Lightweight Hierarchical Model with Frame-Level Joints Adaptive Graph Convolution for Skeleton-Based Action Recognition

Jiang

Yang

Liu

et al. 2021

Security and Communication Networks

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In skeleton-based human action recognition methods, human behaviours can be analysed through temporal and spatial changes in the human skeleton. Skeletons are not limited by clothing changes, lighting conditions, or complex backgrounds. This recognition method is robust and has aroused great interest; however, many existing studies used deep-layer networks with large numbers of required parameters to improve the model performance and thus lost the advantage of less computation of skeleton data. It is difficult to deploy previously established models to real-life applications based on low-cost embedded devices. To obtain a model with fewer parameters and a higher accuracy, this study designed a lightweight frame-level joints adaptive graph convolutional network (FLAGCN) model to solve skeleton-based action recognition tasks. Compared with the classical 2s-AGCN model, the new model obtained a higher precision with 1/8 of the parameters and 1/9 of the floating-point operations (FLOPs). Our proposed network characterises three main improvements. First, a previous feature-fusion method replaces the multistream network and reduces the number of required parameters. Second, at the spatial level, two kinds of graph convolution methods capture different aspects of human action information. A frame-level graph convolution constructs a human topological structure for each data frame, whereas an adjacency graph convolution captures the characteristics of the adjacent joints. Third, the model proposed in this study hierarchically extracts different levels of action sequence features, making the model clear and easy to understand; further, it reduces the depth of the model and the number of parameters. A large number of experiments on the NTU RGB + D 60 and 120 data sets show that this method has the advantages of few required parameters, low computational costs, and fast speeds. It also has a simple structure and training process that make it easy to deploy in real-time recognition systems based on low-cost embedded devices.

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“…The number of classes defined varied across 9 to 18, depending on the dataset used. The SVM classifier was trained with a linear kernel function obtaining, for each dataset, the following results in terms of accuracy and precision: 93.91%, 97.51%, 74.78%, 71.58%, and 94.92%, respectively [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…The first deep model was composed of two LSTM layers, the second one was arranged with one CNN layer followed by an LSTM network (CNNRNN), and the last model was organized with two CNN networks and an LSTM network for the last layer (ConvRNN). The best model for all the datasets used was the ConvRNN architecture, which obtained accuracies ranging 94.7% and 98.1% [ 20 ]. Zhu et al proposed a new spatial model with end-to-end bidirectional LSTM-CNN (BLSTM-CNN).…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living

Guerra

Schmid

Beltrami

et al. 2022

Sensors

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Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.

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Action recognition using kinematics posture feature on 3D skeleton joint locations

Cited by 47 publications

References 23 publications

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

A Lightweight Hierarchical Model with Frame-Level Joints Adaptive Graph Convolution for Skeleton-Based Action Recognition

Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living

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