Probabilistic model checking for human activity recognition in medical serious games

L’Yvonnet, Thibaud; Maria, Elisabetta De; Moisan, Sabine; Rigault, Jean-Paul

doi:10.1016/j.scico.2021.102629

Cited by 5 publications

(7 citation statements)

References 39 publications

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“…In 2021, Thibaud et al 27 have developed a HAR model by considering feasible differences in human behavior. This work deployed DTMCs and PRISM technique was used for expressing and checking motivating temporal logic features regarding the dynamic progression of actions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DCNN was exploited in Reference 26 that incur minimal bias and reduced count of arrays; anyhow, it requires examination of kernel selection. At last, DTMCs was suggested in Reference 27 that offer better prediction accuracy and take minimal time duration. However, it has to focus more on temporal logic characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In 2021, Thibaud et al 27 have developed a HAR model by considering feasible differences in human behavior.…”

Section: Related Workmentioning

confidence: 99%

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Proposed spatio‐temporal features for human activity classification using ensemble classification model

Tyagi

Singh

Dev

2023

Concurrency and Computation

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Classifying human actions from still images or video sequences is a demanding task owing to issues, like lighting, backdrop clutter, variations in scale, partial occlusion, viewpoint, and appearance. A lot of appliances, together with video systems, human-computer interfaces, and surveillance necessitate a compound action recognition system. Here, the proposed system develops a novel scheme for HAR. Initially, filtering as well as background subtraction is done during preprocessing. Then, the features including local binary pattern (LBP), bag of the virtual word (BOW), and the proposed local spatio-temporal features are extracted. Then, in the recognition phase, an ensemble classification model is introduced that includes Recurrent Neural networks (RNN 1 and RNN 2) and Multi-Layer Perceptron (MLP 1 and MLP 2). The features are classified using RNN 1 and RNN 2, and the outputs from RNN 1 and RNN 2 are further classified using MLP 1 and MLP 2, respectively. Finally, the outputs attained from MLP 1 and MLP 2 are averaged and the final classified output is obtained. At last, the superiority of the developed approach is proved on varied measures. K E Y W O R D Saccuracy, human action, multi-layer perceptron, proposed spatio-temporal features, RNN INTRODUCTION"HAR is defined as a classification task where the movement of a person is categorized based on data received from different sources such as sensors, cameras, etc.". It has different appliances in healthcare, particularly in constant action monitoring and fall recognition of aged persons. [1][2][3] In addition, it has vast prospective to support emerging appliances namely, augmented reality, IoT, and indoor localization in smart construction controlling systems to offer a secure indoor surrounding with higher EE.Diverse studies were carried out on HAR techniques in the state of the works using computer vision, ambient devices, sensors, and smartphones. These approaches are usually classified into two classes, that is, passive and active monitoring schemes. [4][5][6] In an AMS, exterior devices like wearable sensors and cameras were deployed. Sensor-based HAR is extensively deployed in analysis, in which a person has to continually carry sensors such as gyroscope, pedometer, and accelerometer that can turn out to be difficult for the person in numerous cases, particularly for elder ones. [7][8][9]

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Proposed spatio‐temporal features for human activity classification using ensemble classification model

Tyagi

Singh

Dev

2023

Concurrency and Computation

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“…Jung et al [29], Sena et al [30] and L'Yvonnet et al [31] presented a sound recognition-oriented HAR method using recurrent neural networks (RNNs). This study collected sound data by analyzing ten groups of people who performed daily concerts in the internal environment.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Based Feature Extraction and Deep Learning Classification Model with Depth Vision

Sinha,

Kumar,

Ghosh

2023

cai

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It remains a challenging task to identify human activities from a video sequence or still image due to factors such as backdrop clutter, fractional occlusion, and changes in scale, point of view, appearance, and lighting. Different appliances, as well as video surveillance systems, human-computer interfaces, and robots used to study human behavior, require different activity classification systems. A four-stage framework for recognizing human activities is proposed in the paper. As part of the initial stages of pre-processing, video-to-frame conversion and adaptive histogram equalization (AHE) are performed. Additionally, watershed segmentation is performed and, from the segmented images, local texton XOR patterns (LTXOR), motion boundary scale-invariant feature transforms (MoBSIFT) and bag of visual words (BoW) based features are extracted. The Bidirectional gated recurrent unit (Bi-GRU) and the Bidirectional long short-term memory (Bi-LSTM) classifiers are used to detect human activity. In addition, the combined decisions of the Bi-GRU and Bi-LSTM classifiers are further fused, and their accuracy levels are determined. With this Dempster-Shafer theory (DST)

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“…Besides, linear time properties are also covered in the specification step of the underlying system. Examples of the application of MDPs in this field includes the verification of hardware systems [2,3], computer networks [4,5], cyberphysical and cyber-security systems [6][7][8], medical sciences [9] robotics and software systems [10][11][12][13]. In reinforcement learning, the main focus is to approximate the optimal expected reward (or cost) before reaching a final state [14].…”

Section: Introductionmentioning

confidence: 99%

State ordering and classification for analyzing non-sparse large Markov Models

Mohagheghi

2023

Preprint

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Markov chains and Markov decision processes have been widely used to model the behavior of computer systems with probabilistic aspects. Numerical and iterative methods are commonly used to analyze these models. Many efforts have been made in recent decades to improve the efficiency of these numerical methods. In this paper, focusing on Markov models with non-sparse structure, a new set of heuristics is proposed for prioritizing model states with the aim of reducing the total computation time. In these heuristics, a set of simulation runs are used for statistical analysis of the effect of each state on the required values of the other states. Under this criterion, the priority of each state in updating its values is determined. The proposed heuristics provide a state ordering that improves the value propagation among the states. The proposed methods are also extended for very large models where disk-based techniques are required to analyze the models. Experimental results show that our proposed methods in this paper reduce the running times of the iterative methods for most cases of non-sparse models.

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Probabilistic model checking for human activity recognition in medical serious games

Cited by 5 publications

References 39 publications

Proposed spatio‐temporal features for human activity classification using ensemble classification model

Proposed spatio‐temporal features for human activity classification using ensemble classification model

Ensemble Based Feature Extraction and Deep Learning Classification Model with Depth Vision

State ordering and classification for analyzing non-sparse large Markov Models

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