A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

Uddin, Md. Zia; Yousuf, Mohammad Abu

doi:10.3329/jsr.v7i3.19527

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…VIF exceeding 10 are signs of serious multicollinearity. Systolic BP ('sysBP') and Diabolic BP ('diaBP') are found highly correlated; hence feature extraction using PCA [19] is done for these, which captures 93 % variance in the data. Though Prevalent Hypertension ('preHyp') was also correlated with 'sysBP' and 'diaBP', this was retained as advised by a medical practitioner.…”

Section: Feature Selection and Extractionmentioning

confidence: 99%

Consensus of Feature Selection Methods and Reduced Generalization Gap Model to Improve Diagnosis of Heart Disease

Gupta¹,

Sedamkar²

2021

J. Sci. Res.

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Enhancing the diagnostic ability of Machine Learning models for acceptable prediction in the healthcare community is still a concern. There are critical care disease datasets available online on which researchers have experimented with a different number of instances and features for similar disease prediction. Further, different Machine Learning (ML) models have different preprocessing requirements. Framingham heart disease data is multicollinear and has missing values. Thus, the proposed model aims to explore the differential preprocessing needs of ML models followed by feature selection in consensus with domain experts and feature extraction to resolve multicollinearity issues. Missing values have been imputed differently for each feature. The work also identifies optimal train set size by plotting a learning curve that provides a minimum generalization gap. When testing is done on this hyperparameter tuned model, performance is enhanced with respect to the F score weighted by support and stratification since the data is imbalanced. Experimental results demonstrate improvement in performance metrics, i.e., weighted F score, precision, recall, accuracy up to 3 %, and F1 score by 8 % for Logistic Regression Classifier with the proposed model. Further, the time required for hyperparameter tuning is reduced by 50% for tree-based models, particularly Classification and Regression Tree (CART).

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Section: Feature Selection and Extractionmentioning

confidence: 99%

Consensus of Feature Selection Methods and Reduced Generalization Gap Model to Improve Diagnosis of Heart Disease

Gupta¹,

Sedamkar²

2021

J. Sci. Res.

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“…The multilayer perceptron technique, presented in [17], become a popular algorithm which is widely documented as a new architecture for ANNs. It is famously used for the classification purposes that can approximate any regularity between the input and output.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues

Rueangsirarak

Uttama

Kaewkaen

et al. 2018

2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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This research presents a feasibility to adopt a decision support system framework as a rehabilitation and assessment tool for supporting the physiotherapist in identifying the abnormal gaits of older people. The walking movement was captured by the Microsoft Kinect cameras in order to collect the human motion during 4-meters clinical walk test. 28 older adults participated in this research and perform their gait in front of the affordable cameras. To distinguish an abnormal gait with balance impairment from those of healthy older adults, two machine learning algorithms; ANN and SVM, were selected to classify the data. Experimental results show that SVM achieves the best performance of classification with 82.14% of accuracy, in single-task and double-task conditions, when compared with the standard clinical results. However, SVM cannot achieve an acceptable performance when classifying triple-task condition, achieving only 71.42% of accuracy. As a comparison, ANN delivers only 75.00% of accuracy, which is inferior to SVM. This study show that SVM can be considered as a rehabilitation measuring tool for assisting the physiotherapist in assessing the gait of older people.

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“…In addition to that feature, we added features of the orientation of the object using the Principal Components Analysis (PCA) [13]. To get the angle value object orientation, calculation as in [16].…”

Section: A Vision Modulementioning

confidence: 99%

A Hierarchical Description-based Video Monitoring System for Elderly

Nari

Setiawan

Adiprawita

2017

EECSI

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The increase in the number of elderly motivates academic researchers to develop technologies that can ensure selfsufficiency in their lives. In this research, prototype of an inexpensive video monitoring system for the elderly using a single RGB camera proposed. In the process is divided into two, namely vision and event recognition module. For event recognition, we use a hierarchical description-based approach with three attributes, namely posture (e.g., stand, sit and lie), location (e.g., walking zone, relaxing zone and toilet zone) and duration (e.g., short and long). Output this system is description activity recognized in the text. The experiment result shows our system can provide the effectiveness of the context description.

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A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

Cited by 6 publications

References 14 publications

Consensus of Feature Selection Methods and Reduced Generalization Gap Model to Improve Diagnosis of Heart Disease

Consensus of Feature Selection Methods and Reduced Generalization Gap Model to Improve Diagnosis of Heart Disease

Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues

A Hierarchical Description-based Video Monitoring System for Elderly

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