An Approach To Enhance Fall Detection Using Machine Learning Classifier

Soni, Mukesh; Chauhan, Sonali; Bajpai, Bhavna; Puri, Tanvi

doi:10.1109/cicn49253.2020.9242634

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…It comprises the following 3D volumes: MosMed-10 contains the following classifications: ct1, ct, ct and ct. The 254 total 3D scans are part of the production of the CT0 the normal scan result means the person is not a candidate for pneumonia (Soni et al , 2020a). CT2D involves 2,354 3D scans, all of which point to a CT infection in the lungs.…”

Section: Mosmed-1110 Data Setmentioning

confidence: 99%

Diagnosis of COVID-19 using 3D CT scans and vaccination for COVID-19

Gangadhar

Jana

Majji³

et al. 2021

WJE

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Purpose For the first time in a decade, a new form of pneumonia virus, coronavirus, COVID-19, appeared in Wuhan, China. To date, it has affected millions of people, killed thousands and resulted in thousands of deaths around the world. To stop the spread of this virus, isolate the infected people. Computed tomography (CT) imaging is very accurate in revealing the details of the lungs and allows oncologists to detect COVID. However, the analysis of CT scans, which can include hundreds of images, may cause delays in hospitals. The use of artificial intelligence (AI) in radiology could help to COVID-19-positive cancer in this manner is the main purpose of the work. Design/methodology/approach CT scans are a medical imaging procedure that gives a three-dimensional (3D) representation of the lungs for clinical purposes. The volumetric 3D data sets can be regarded as axial, coronal and transverse data sets. By using AI, we can diagnose the virus presence. Findings The paper discusses the use of an AI for COVID-19, and CT classification issue and vaccination details of COVID-19 have been detailed in this paper. Originality/value Originality of the work is, all the data can be collected genuinely and did research work doneown methodology.

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Section: Mosmed-1110 Data Setmentioning

confidence: 99%

Diagnosis of COVID-19 using 3D CT scans and vaccination for COVID-19

Gangadhar

Jana

Majji³

et al. 2021

WJE

View full text Add to dashboard Cite

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“…Predictive models are helpful in many applications such as weather forecasting, share market, farming, retailer, banking, health care, sentiment analysis, human fall detection, natural language processing, classification of images and software quality improvement, etc. (Niar et al , 2020; Soni et al , 2020; Soni and Kumar, 2020; Chandra et al , 2016; Soni et al , 2020). Currently, machine learning models are used to predict various diseases such as heart diseases, lung diseases, cancers, skin diseases, joint disease, etc.…”

Section: Introductionmentioning

confidence: 99%

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Chauhan

Modi²,

Shrivastava

2021

WJE

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Purpose The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making. Design/methodology/approach In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score. Findings The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset. Originality/value Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

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“…This simulated sample is like real fall recorded by the real accelerometer for using them as input for ML application. Soni et al [14] presented the fall detecting system with ML method that subsequently employs a fog computing method to transmit data to the caregivers in real-time. Smartphone accelerometers are employed to collect the information and one class technique-based support vector machine (SVM) is employed for building fall detection.…”

Section: Related Workmentioning

confidence: 99%

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Alluhaidan¹,

Alajmi²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

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An Approach To Enhance Fall Detection Using Machine Learning Classifier

Cited by 19 publications

References 14 publications

Diagnosis of COVID-19 using 3D CT scans and vaccination for COVID-19

Diagnosis of COVID-19 using 3D CT scans and vaccination for COVID-19

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

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