scholarly journals RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3855
Author(s):  
Mubashir Rehman ◽  
Raza Ali Shah ◽  
Muhammad Bilal Khan ◽  
Najah Abed AbuAli ◽  
Syed Aziz Shah ◽  
...  
Keyword(s):  
Software Defined Radio ◽  
Infected Individual ◽  
Control Period ◽  
Research Work ◽  
Disease Diagnosis ◽  
Breathing Patterns ◽  
Healthcare Applications ◽  
Training Time ◽  
Abnormal Breathing ◽  
Rf Sensing

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

Abnormal movements and diaphragmatic flutter in a case of suspected induced illness

2021 ◽  
Vol 14 (4) ◽  
pp. e236962
Author(s):  
Rebecca Arvier ◽  
Thomas Clayton ◽  
Monique Dade ◽  
Rahul S Joshi
Keyword(s):  
Breathing Pattern ◽  
Tertiary Hospital ◽  
Drug Screen ◽  
Urine Drug Screen ◽  
Breathing Patterns ◽  
Abnormal Movements ◽  
Urine Drug ◽  
Conscious Level ◽  
Abnormal Breathing ◽  
Initial Results

A 6-month-old girl presented to hospital via ambulance with a decreased conscious level (initial Glasgow Coma Scale of 3) and an abnormal breathing pattern described as diaphragmatic flutter. She then developed abnormal movements and continued to have episodes of fluctuating conscious levels so was transferred to a tertiary hospital paediatric intensive care unit for further investigation. During her 16-day stay in hospital, she continued to experience discrete episodes of drowsiness, bradycardia, unusual breathing patterns and abnormal movements which were associated with agitation, tachycardia, hypertension and insomnia. The patient underwent extensive investigation for her symptoms and, after some delay in waiting for initial results before considering a urine drug screen, she was ultimately found to have lisdexamfetamine and clonidine in her urine drug screen. Her symptoms subsequently resolved after her mother’s visits were restricted.

Facial Characteristics of Children Who Breathe Through the Mouth

PEDIATRICS ◽  
1984 ◽  
Vol 73 (5) ◽  
pp. 622-625
Author(s):  
Dante Bresolin ◽  
Gail G. Shapiro ◽  
Peter A. Shapiro ◽  
Steven W. Dassel ◽  
Clifton T. Furukawa ◽  
...  
Keyword(s):  
Physical Examination ◽  
Nasal Obstruction ◽  
Clinical Examination ◽  
Breathing Patterns ◽  
Facial Growth ◽  
Cephalometric Radiograph ◽  
Abnormal Breathing ◽  
Mucosal Edema

There are many claims that abnormal breathing patterns alter facial growth; however, there are limited controlled data to confirm these claims. Thirty children with allergy, aged 6 to 12 years, who had moderate-to-severe nasal mucosal edema on physical examination and who appeared to breathe predominantly through the mouth and 15 children without allergy who had normal findings from nasal examination and who appeared to breathe predominantly through the nose were evaluated. All subjects received an intraoral clinical examination and cephalometric radiograph analysis. In comparison with children who breathed through the nose, children who breathed through the mouth had longer faces with narrower maxillae and retruded jaws. This supports the hypothesis that children with nasal obstruction and who appear to breathe through the mouth have distinctive facial characteristics.

An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network

2022 ◽  
pp. 155-170
Author(s):  
Lap-Kei Lee ◽  
Kwok Tai Chui ◽  
Jingjing Wang ◽  
Yin-Chun Fung ◽  
Zhanhui Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Research Work ◽  
Latent Semantic Indexing ◽  
Future Research ◽  
Training Time ◽  
Cross Domain ◽  
Document Frequency

The dependence on Internet in our daily life is ever-growing, which provides opportunity to discover valuable and subjective information using advanced techniques such as natural language processing and artificial intelligence. In this chapter, the research focus is a convolutional neural network for three-class (positive, neutral, and negative) cross-domain sentiment analysis. The model is enhanced in two-fold. First, a similarity label method facilitates the management between the source and target domains to generate more labelled data. Second, term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) are employed to compute the similarity between source and target domains. Performance evaluation is conducted using three datasets, beauty reviews, toys reviews, and phone reviews. The proposed method enhances the accuracy by 4.3-7.6% and reduces the training time by 50%. The limitations of the research work have been discussed, which serve as the rationales of future research directions.

Abnormal breathing patterns during sleep in diabetes

1985 ◽  
Vol 17 (4) ◽  
pp. 391-395 ◽  
Author(s):  
Susanna Mondini ◽  
Christian Guilleminault
Keyword(s):  
Breathing Patterns ◽  
Abnormal Breathing

scholarly journals Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation

2020 ◽  
Author(s):  
Suchitra Saxena ◽  
Shikha Tripathi ◽  
Sudarshan Tsb
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Process Automation ◽  
Training Time ◽  
Robotic Process Automation ◽  
Gated Recurrent Units

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.

scholarly journals Telugu and Hindi Script Recognition using Deep learning Techniques

2019 ◽  
Vol 8 (11) ◽  
pp. 1758-1764 ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Research Work ◽  
Indian Languages ◽  
Digital Form ◽  
Training Time ◽  
Learning Techniques ◽  
Handwritten Document

The need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.

scholarly journals Kernel Convolutional Neural Network Deep Learning Algorithm To Classify LIVER Disease

2019 ◽  
Vol 8 (6) ◽  
pp. 2002-2007
Author(s):  
Pardeep Kaur ◽  
Harinder Kaur
Keyword(s):  
Liver Disease ◽  
Learning Algorithm ◽  
Eating Habits ◽  
Kernel Method ◽  
Early Stage ◽  
Research Work ◽  
Disease Diagnosis ◽  
The Body ◽  
Common Disease ◽  
Deep Learning Algorithm

Now a day, liver disease is common disease due to the bad eating habits among individuals. Some disturbance in the functioning of the liver may cause liver sickness. Liver is responsible for overall functioning of the body. Hence, it becomes necessary to diagnosis the liver disease at an early stage. In advanced world of technology, various methods has been been developed to diagnosis and detect the disease includes data mining. This is novel concept to determine the data by extracting features and recognize indications of liver disease by medical experts. The existing technique has implemented optimize the rules released from Boosted classification with a genetic algorithm, to enhance the LDD (Liver Disease Diagnosis) interval of time and accuracy level. Hence, GA is utilized for enhancing and enhancing directions of another method. In this research work, defines a novel method ECNN (Enhanced CNN) of LDD and enable medical specialists to recognize sign of disease and optimization is done for maximum period, decrease the death rate. Clustering and Feature extraction phase to extract the unique feature based on Kernel method and divide the data into a group or cluster-based using FCM algorithm. Implement CNN method to predict or detect the liver disease to improve the performance and classification of rules set. The proposed method has implemented to achieve better performance and compared with existing methods. The simulation tool used in this research works MATLAB 2016a and calculates the performance is Accuracy achieved 96 % ad existing GA accuracy rate 92.9 % achieved in our work

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