scholarly journals A Unique Framework for Contactless Estimation of Body Vital Signs using Facial Recognition

2021 ◽  
Vol 10 (12) ◽  
pp. 14-20
Author(s):  
M. Bhanu Sridhar ◽  
Sai Himaja Kinthada ◽  
Bhargavi Marni
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Image Processing ◽  
New Technologies ◽  
Heart Diseases ◽  
Vital Signs ◽  
Main Idea ◽  
Machine Learning Techniques ◽  
Clinical Practices ◽  
Novel Technologies

As one of the consequences of COVID-19 pandemic, a lot of new technologies are developing in fast-track pace in clinical practices. The main idea of our project is to design contactless technology for the support of patients who suffer from blood pressure disorders and coronary heart diseases using machine learning approach. This may intend people to monitor their heart rate, pulse rate, respiratory life and oxygen saturation levels at an ease. The orientation of this paper is to monitor the blood pressure considering the facial changes and movements in a video to get rid of cuff-based measurement of blood pressure. We analyzed whether blood pressure can be obtained in a contactless way utilizing a novel technologies like image processing and machine learning techniques. This innovation estimates vague facial blood stream changes from video recordings captured by camera with the help of machine learning and image processing techniques.

scholarly journals Heart rate estimation from speech signals using machine learning and cloud computing

2020 ◽  
Author(s):  
Mohammed Usman
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Heart Rate ◽  
Cloud Computing ◽  
Early Diagnosis ◽  
Vital Signs ◽  
Machine Learning Techniques ◽  
Speech Signals ◽  
Learning Techniques ◽  
Heart Rate Estimation

Speech signals of individuals contain informationrelated to their physical, mental as well as emotionalhealth. A first step in clinical diagnosis is to measure vitalsigns, which provide an indication of vital body functions.The four vital signs - heart rate, blood pressure, body temperatureand respiratory rate are useful in assessing the healthof an individual and early diagnosis of deteriorating healthconditions. The objective of this work is to measure heartrate of individuals based on their speech signal using signalprocessing and machine learning techniques.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals NON-INVASIVE MODELING OF INTRACRANIAL HYPERTENSION FROM PHYSIOLOGICAL CHANNELS

2021 ◽  
Author(s):  
Parisa Naraei ◽  
Alireza Sadeghian
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Intracranial Hypertension ◽  
Clinical Decision Making ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Arterial Blood ◽  
Secondary Damage ◽  
Invasive Techniques ◽  
Present Thesis

Intracranial pressure (ICP), the pressure within the cranium reflects three elements: cerebrospinal fluid, brain tissue and blood pressure. High ICP (above 20 mmHg) is called intracranial hypertension (ICH) which is due to the tumour, swelling or the internal bleeding of brain and may cause secondary damage to the brain. ICP is a crucial parameter in diagnosis of brain injuries. Two models which utilize machine learning techniques to anticipate ICH and assist in clinical decision making were developed in the present thesis. ICP can be monitored through the invasive techniques (i.e., inserting an intraventricular catheter through the skull). Despite the high accuracy, the episodes of ICH can also be manually identified only after placement of catheter which is accompanied by lots of technical difficulties. Furthermore, the ICP signal might not be available continuously or may include unwanted noise that could introduce more complication to the diagnosis and treatment procedure. Considering the difficulties of the invasive techniques, a non-invasive model, capable to predict the ICH helps to save time, estimate the missing ICPs, predict the ICP in advance and accelerate medical intervention. The present thesis introduces two machine learning models to resolve the current limitations: 1- Non-invasive prediction of ICP labels 10 minutes in advance where the status of ICP (normal / ICH) is predicted based on the two components extracted from the physiological signals such as mean arterial blood pressure and respiration rate. 2- Wavelet – clustering where a machine learning solution for ICP estimation using a hybrid wavelet clustering is proposed. The episodes of ICP and derived from ICP (such as cerebral perfusion pressure) are excluded from the second model.

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