A Survey Paper on Wearable Sensors based Fall Detection

Prajapati, Trupti; Bhatt, Nikita; Mistry, Darshana

doi:10.5120/20211-2475

Cited by 8 publications

(3 citation statements)

References 6 publications

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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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Section: Introductionmentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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“…Fall detection systems usually adopt a wearable sensor or use vision/ambient-based sensors for ambient sensing or implement hybrid solutions. Each approach shows different advantages and disadvantages related to accuracy, response time, and complexity [11].…”

Section: Fall Detectionmentioning

confidence: 99%

An IoT Smart Environment in Support of Disease Diagnosis Decentralization

Andreadis

Zambon

2020

Electronics

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The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm.

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“…The same digital value is sent to of the caregiver of the fallen person using GSM (Prajapati et al , 2015).…”

Section: Fall Detectionmentioning

confidence: 99%

Human health monitoring using wearable sensor

Evangeline

Lenin

2019

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Purpose The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier stage and monitoring after recoup. Design/methodology/approach The methodology involves a combination of three subsystems which monitors the human parameters such as temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters measured are communicated to the caregiver and doctor. Findings Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and LabVIEW simulators. Practical implications The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health monitoring, elderly monitoring and so on. Originality/value First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer product for use as monitoring device with high accuracy.

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A Survey Paper on Wearable Sensors based Fall Detection

Cited by 8 publications

References 6 publications

Deep Learning Based Systems Developed for Fall Detection: A Review

Deep Learning Based Systems Developed for Fall Detection: A Review

An IoT Smart Environment in Support of Disease Diagnosis Decentralization

Human health monitoring using wearable sensor

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