scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

scholarly journals A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

2022 ◽  
Author(s):  
Ping Wang ◽  
Qimeng Li ◽  
Peng Yin ◽  
Zhonghao Wang ◽  
Yu Ling ◽  
...  
Keyword(s):  
Neural Network ◽  
Fall Detection ◽  
The Elderly ◽  
Channel Selection ◽  
Ultra Wideband ◽  
World Health ◽  
Indoor Environments ◽  
Neural Network Approach ◽  
High Recognition Accuracy ◽  
Health Organization

AbstractAccording to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

Solar Powered Multi-Controlled Smart Wheelchair for Disabled: Development and Features

2019 ◽  
Vol 16 (11) ◽  
pp. 4889-4900 ◽  
Author(s):  
A. Sharmila ◽  
Ankur Saini ◽  
Shubham Choudhary ◽  
T. Yuvaraja ◽  
S. G. Rahul
Keyword(s):  
Health Care Services ◽  
Detection System ◽  
Fall Detection ◽  
Hospital Staff ◽  
Health Condition ◽  
World Health ◽  
Level Indicator ◽  
Solar Powered ◽  
Health Organization ◽  
Smart Wheelchair

As per the report presented by the World Health Organization, it is well aware that 15% of the total world’s population is physically challenged. Accessibility of health care services is limited to people with physical disabilities. The utilization of battery powered wheelchairs with excellent navigational capabilities is one of the extraordinary strides towards the incorporation of severely physically and mentally challenged people. Motion, movement and localization are significant issues for the blind, paraplegic and handicapped people who are accompanied by eminent tiresome work. There exist different systems to override the problems described, allowing the end-user to perform safe movements and complete certain daily life tasks. Considering the said issues as a motivation, this work presents the design and development of Solar Powered Multi-Controller Smart Wheelchair. The developed smart wheelchair uses eye blink sensor to steer the wheelchair for quadriplegia patient along with Joystick and Keypad module for several kinds of disabilities. In addition, more liberty is provided to the disabled person by using additional sensors such as heartbeat sensor and a temperature sensor which continuously monitors the health condition of the patient. Additionally, a urine level indicator is also used to avoid inconvenience to the patient. If the patient falls down along with a wheelchair, a fall detection system in the wheelchair detects the same. All the detail can be shared with hospital staff and the patient’s guardian during a contingency condition, so that the staffs and guardians can take immediate actions. The safety of the patient and the wheelchair with respect to the incorporation of solar power is highly given priority during this system design.

scholarly journals A Survey on Recent Advances in Wearable Fall Detection Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Anita Ramachandran ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
World Health Organization ◽  
Fall Detection ◽  
Healthcare Systems ◽  
World Health ◽  
The European Union ◽  
Human Beings ◽  
Average Life Expectancy ◽  
Detection Systems ◽  
Health Organization

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

scholarly journals Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 884 ◽  
Author(s):  
Zizheng Zhang ◽  
Shigemi Ishida ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Method ◽  
Detection System ◽  
Wearable Sensors ◽  
Low Frequency ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Features ◽  
Pose Detection

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

scholarly journals Fall Detection System-Based Posture-Recognition for Indoor Environments

2021 ◽  
Vol 7 (3) ◽  
pp. 42
Author(s):  
Abderrazak Iazzi ◽  
Mohammed Rziza ◽  
Rachid Oulad Haj Thami
Keyword(s):  
Detection System ◽  
Head Injuries ◽  
Fall Detection ◽  
The Elderly ◽  
Support Vector ◽  
Svm Classifier ◽  
Indoor Environments ◽  
Risk Of Death ◽  
Senior Population ◽  
Public Datasets

The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

Machine Learning Based Coronary Artery Disease Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 3999-4002
Author(s):  
A. C. Bhavani ◽  
K. Aditya Shastry ◽  
K. Deepika ◽  
Nithya N. Shanbag ◽  
G. C. Akshatha
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Medical Diagnosis ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Health Organization

The world health organization (WHO) has assessed that the death of around 12 million people across the globe is observed each year because of diseases related to cardiovascular. The dangers associated with the cardiovascular disease can be identified effectively using machine learning techniques. As per survey, around 30% of the patient suffers no symptoms during heart attacks. But the bloodstream contains unique indications of the attack for days. The medical diagnosis of a patient remains a complex task due to several factors. The accurate medical diagnosis of a patient’s heart disease is critical as it significantly leads to the saving of millions of human lives. In this regard, the automation of the medical diagnosis is significant. The goal of this work is the development of a system for predicting the disease related to coronary artery in a patient with high accuracy utilizing machine learning (ML) techniques. Several algorithms like Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) classifiers were implemented for predicting the disease. Extensive experiments demonstrated that the naïve Bayes achieved higher accuracy than the DT and SVM with regards to accuracy, precision, F-Measure, Recall, and receiver operating characteristic (ROC) performance metrics.

scholarly journals Efficient Machine Learning Techniques to Detect Glaucoma using Structure and Texture based Features

2020 ◽  
Vol 9 (2) ◽  
pp. 193-201
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Texture Features ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
World Health ◽  
Fundus Image ◽  
Glaucoma Detection ◽  
Learning Techniques ◽  
Health Organization

Survey of world health organization has revealed that retinal eye disease Glaucoma is the second leading cause for the blindness worldwide. It is the disease which will steal the vision of the patient without any warning or symptoms. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the paper is to predict and detect Glaucoma efficiently using image processing and machine learning based classification techniques. Segmentation techniques such as unique template approach, Gray Level Coherence Matrix based feature extraction approach and wavelet transform based approach are used to extract these structure and texture based features. Combination of structure based and texture based techniques along with machine learning techniques improves the efficiency of the system. Developed efficient Computer aided Glaucoma detection system classifies a fundus image as either Normal or Glaucomatous image based on the structural features of the fundus image such as Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Superior and Inferior neuro-retinal rim thicknesses, Vessel structure based features and Distribution of texture features in the fundus images.

scholarly journals A Study on Self-Diagnosis Method to Prevent the Spread of COVID-19 Based on SVM

2021 ◽  
Vol 12 (5) ◽  
pp. 264-270
Author(s):  
Young-Sang Kwak, Seo-Won Song, Seong-Hee Yeo, Min-Soo Kang
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Support Vector Machine ◽  
World Health ◽  
Diffusion Method ◽  
Support Vector ◽  
Infected Person ◽  
Severe Problem ◽  
Diagnosis Method ◽  
Health Organization

In this paper, a study was conducted to find a self-diagnosis method to prevent the spread of COVID-19 based on machine learning. COVID-19 is an infectious disease caused by a newly discovered coronavirus. According to WHO(World Health Organization)’s situation report published on May 18th, 2020, COVID-19 has already affected 4,600,000 cases and 310,000 deaths globally and still increasing. The most severe problem of COVID-19 virus is that it spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, which occurs in everyday life. And also, at this time, there are no specific vaccines or treatments for COVID-19.Because of the secure diffusion method and the absence of a vaccine, it is essential to self-diagnose or do a self-diagnosis questionnaire whenever possible. But self-diagnosing has too many questions, and ambiguous standards also take time. Therefore, in this study, using SVM(Support Vector Machine), Decision Tree and correlation analysis found two vital factors to predict the infection of the COVID-19 virus with an accuracy of 80%. Applying the result proposed in this paper, people can self-diagnose quickly to prevent COVID-19 and further prevent the spread of COVID-19.

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