A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam, Md. Milon; Neom, Nieb; Imtiaz, Md. Samir; Nooruddin, Sheikh; Islam, Md. Repon; Islam, Md. Rabiul

doi:10.18280/isi.240602

Cited by 28 publications

(15 citation statements)

References 30 publications

(45 reference statements)

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“…These connected components can be used on devices for health monitoring. The used sensors then forward the information to distant locations like M2M, which are machinery for computers, machines for people, handheld devices, or smartphones [11]. It is a simple, energy-efficient, much smarter, scalable, and interoperable way of tracking and optimizing care to any health problem.…”

Section: Introductionmentioning

confidence: 99%

Development of Smart Healthcare Monitoring System in IoT Environment

2020

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Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient's basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO 2 sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.

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Section: Introductionmentioning

confidence: 99%

Development of Smart Healthcare Monitoring System in IoT Environment

2020

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“…It has become a concern to develop intelligent detection and prevention system. Many developed fall detection and monitoring systems have been reviewed in [12]- [23] with various categories and perspectives. The challenges, issues and, advances related to fall detection and fall prevention are discussed in [24]- [27].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This paper provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

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“…Python programming language was used for data mining predictive tasks. Python is a well-known general purpose and dynamic programming language that is being used for different fields such as data mining [30], machine learning [31,32], and internet of things [33,34]. Data mining algorithms are being implemented using python with the help of the special purpose libraries.…”

Section: Resultsmentioning

confidence: 99%

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

et al. 2020

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Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

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A Review on Fall Detection Systems Using Data from Smartphone Sensors

Cited by 28 publications

References 30 publications

Development of Smart Healthcare Monitoring System in IoT Environment

Development of Smart Healthcare Monitoring System in IoT Environment

Deep Learning Based Systems Developed for Fall Detection: A Review

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

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