scholarly journals Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

2022 ◽  
Author(s):  
Umer Saeed ◽  
Syed Yaseen Shah ◽  
Jawad Ahmad ◽  
Muhammad Ali Imran ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Monitoring ◽  
Extensive Review ◽  
Contact Sensing

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models

2020 ◽  
Vol 17 (12) ◽  
pp. 5438-5446
Author(s):  
C. Suguna ◽  
S. P. Balamurugan
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Comparative Study ◽  
Cancer Diagnosis ◽  
Pap Smear ◽  
Classification Models ◽  
Extensive Review ◽  
Diagnosis And Classification ◽  
Automated Machine Learning

Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.

scholarly journals The Challenges of Telemedicine in Rheumatology

2021 ◽  
Vol 8 ◽  
Author(s):  
Yujie Song ◽  
Laurène Bernard ◽  
Christian Jorgensen ◽  
Gilles Dusfour ◽  
Yves-Marie Pers
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Devices ◽  
Rheumatic Diseases ◽  
Mobile Applications ◽  
Data Security ◽  
Patient Monitoring ◽  
Health Interventions ◽  
The Past ◽  
Legal Problems

During the past 20 years, the development of telemedicine has accelerated due to the rapid advancement and implementation of more sophisticated connected technologies. In rheumatology, e-health interventions in the diagnosis, monitoring and mentoring of rheumatic diseases are applied in different forms: teleconsultation and telecommunications, mobile applications, mobile devices, digital therapy, and artificial intelligence or machine learning. Telemedicine offers several advantages, in particular by facilitating access to healthcare and providing personalized and continuous patient monitoring. However, some limitations remain to be solved, such as data security, legal problems, reimbursement method, accessibility, as well as the application of recommendations in the development of the tools.

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

scholarly journals Smart Wheel Chair for Elderly People

2020 ◽  
Vol 9 (2) ◽  
pp. 268-275
Keyword(s):  
Machine Learning ◽  
Patient Monitoring ◽  
The Elderly ◽  
Healthcare Industry ◽  
Human Intervention ◽  
Constant Attention ◽  
Limited Mobility ◽  
External Assistance ◽  
Wheel Chair ◽  
Spectrum Of Patients

Healthcare is a labour intensive industry. A substantial amount of money and resources are spent on hiring caretakers and nurses for patients who need constant attention for their sustenance. The proposed system can be used for a broad spectrum of patients but specifically focuses on the elderly, the bedridden, and the ones with limited mobility. The proposed work provides a solution to get a full-fledged working system that automates every aspect of patient monitoring to reduce errors introduced by human intervention. It provides a framework of seamless interaction with the patient and, finally, to deliver external assistance for mobility. This proposed system relies on embedded computers, ECG, IoT, RTC, HMM, machine learning, and other sensors used in the healthcare industry.

scholarly journals Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dylan M. Richards ◽  
MacKenzie J. Tweardy ◽  
Steven R. Steinhubl ◽  
David W. Chestek ◽  
Terry L. Vanden Hoek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Monitoring ◽  
Standard Of Care ◽  
Sensor Data ◽  
Study Cohort ◽  
Remote Patient Monitoring ◽  
Health Technologies ◽  
Phase Study ◽  
Remote Patient ◽  
Continuous Sensor

AbstractThe COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

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