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1.
Sensors (Basel) ; 23(17)2023 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-37687875

RESUMO

Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient's body weight fluctuations occurring within a day. The patient's lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models' predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth.


Assuntos
Sistemas Computacionais , Sapatos , Humanos , Desidratação , Aprendizado de Máquina , Peso Corporal
2.
IEEE J Biomed Health Inform ; 25(6): 1852-1863, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33788696

RESUMO

The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 ± 6.83 and specificity of 77.72 ± 8.06 is observed for the COVIDGR dataset.


Assuntos
COVID-19/diagnóstico por imagem , Reconhecimento Automatizado de Padrão , Pneumonia Viral/diagnóstico por imagem , Automação , COVID-19/virologia , Simulação por Computador , Humanos , Aprendizado de Máquina , Pneumonia Viral/virologia , Radiografia Torácica , SARS-CoV-2/isolamento & purificação , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X
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