Human posture recognition based on multiple features and rule learning

Abstract The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.

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A Knowledge Integrated Case-Based Classifier

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194019500293 ◽

2019 ◽

Vol 29 (06) ◽

pp. 849-871

Author(s):

Jirapond Muangprathub ◽

Siriwan Kajornkasirat ◽

Apirat Wanichsombat ◽

Veera Boonjing ◽

Jarunee Saelee ◽

...

Keyword(s):

Knowledge Base ◽

Learning Algorithm ◽

Concept Lattice ◽

Rule Learning ◽

Classification Performance ◽

Training Data ◽

Vector Model ◽

Case Based Reasoning ◽

Rule Based ◽

Case Based

This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.

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Human posture recognition based on kinect depth images

Advanced Computer Control ◽

10.2495/icacc130331 ◽

2014 ◽

Cited By ~ 1

Author(s):

Qing Zhu ◽

Yayi Wang

Keyword(s):

Human Posture ◽

Depth Images ◽

Posture Recognition

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Piano Playing Simulation System Based on Human Posture Recognition

2020 International Conference on Culture-oriented Science & Technology (ICCST) ◽

10.1109/iccst50977.2020.00013 ◽

2020 ◽

Author(s):

Jucheng Qiu ◽

Xiaoyu Wu ◽

Shuyu Chen ◽

Yifan Zhang

Keyword(s):

Simulation System ◽

Human Posture ◽

Posture Recognition ◽

Piano Playing

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Integrated Internet of Things (IoT) technology device on smart home system with human posture recognition using kNN method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1098/4/042065 ◽

2021 ◽

Vol 1098 (4) ◽

pp. 042065

Author(s):

M I Siddiq ◽

I P D Wibawa ◽

M Kallista

Keyword(s):

Internet Of Things ◽

Smart Home ◽

Human Posture ◽

Posture Recognition

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Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

10.26434/chemrxiv.14397140 ◽

2021 ◽

Author(s):

Jeremy Feinstein ◽

ganesh sivaraman ◽

Kurt Picel ◽

Brian Peters ◽

Alvaro Vazquez-Mayagoitia ◽

...

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

High Throughput ◽

High Throughput Screening ◽

Organic Chemical ◽

Learning Method ◽

Toxicity Data ◽

Chemical Structures ◽

Machine Learning Methods ◽

Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

BioMed Research International ◽

10.1155/2016/4248026 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Abbas Akkasi ◽

Ekrem Varoğlu ◽

Nazife Dimililer

Keyword(s):

Conditional Random Fields ◽

Named Entity Recognition ◽

Classification Performance ◽

Entity Recognition ◽

Support Vector ◽

Learning Approaches ◽

Data Set ◽

Rule Based ◽

Named Entity ◽

Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

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