Remarkable Skeleton Based Human Action Recognition

Jaiswal, Sushma; Jaiswal, Tarun

doi:10.37256/aie.122020562

Cited by 2 publications

(2 citation statements)

References 59 publications

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“…The proposed model is superior in the performance-wise on the contrast to the classic CNN model. From the experimental results, it is evident that, enhanced CNN models as claimed by (Krishnaswamy Rangarajan, 2022) perform better rather than the classic CNN approach as considered by (Sushma Jaiswal, 2023). There are certain problems that occur in forecasting the Covid-19 from the CT scan.…”

Section: Literature Reviewmentioning

confidence: 95%

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Yamathi

Rani

Krishna

2023

JAETS

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with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method

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Section: Literature Reviewmentioning

confidence: 95%

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Yamathi

Rani

Krishna

2023

JAETS

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“…In particular, Convolutional Neural Networks (CNN) have achieved great success for image-based tasks, while Recurrent Neural Networks (RNN) outperformed other approaches on time series. For instance, Long Short-Term Memory (LSTM) networks are frequently used to solve sequence-based problems thanks to their strengths in modeling the dependencies and dynamics in sequential data [ 1 , 2 , 3 , 6 , 21 , 22 ]. Ahad et al trained three different deep learning models using temporal statistical features computed through a sliding time window on 3D skeletal joints data from five public datasets and compared their performances with that of the SVM classifier.…”

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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Remarkable Skeleton Based Human Action Recognition

Cited by 2 publications

References 59 publications

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living

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