Fall Detection System by Machine Learning Framework for Public Health

Rodrigues, Thiago Braga; Salgado, Débora Pereira; Cordeiro, Mauricio C.; Osterwald, Katja Magdalena; Filho, Teodiano Freire Bastos; Lucena, Vicente Ferreira de; Naves, Eduardo Lázaro Martins; Murray, Niall

doi:10.1016/j.procs.2018.10.189

Cited by 28 publications

(9 citation statements)

References 7 publications

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“…Data analytics approaches in general and machine learning (ML) methods in particular have improved insights into single disease investigations and its use in public health has a promising future to revolutionize the evidence based policy making and designing intervention programs [7]- [9].In addition several examples in literature exist for using machine learning approaches in epidemiology context for public health [10], [11]. [12].…”

Section: Related Workmentioning

confidence: 99%

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Khalique

Khan

2021

IEEE Access

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Communicable diseases remain a significant challenge for public health management. In particular, for resource scarce settings, it is important to understand the linkages and dynamics of multiple diseases that share common resources, space or time. We develop a framework, called Multiple Disease Management Framework (MDMF) based on machine learning approach for managing multiple diseases occurring in close space and time to identify locations that experience high disease burden rates. We use 8 water related disease incidence data in Punjab, Pakistan from year 2013 to 2019 to investigate interactions among hotspots of different diseases. The hotspot analysis involves multi level clustering and tagging of individual disease incidence streams that generates a distance based graph over a geographical area and is then integrated into a single stream in the framework to identify final sensitive locations called cluster alarms. The initial individual disease clustering yielded number of clusters as high as 24 clusters for each disease with up to 16 neighboring clusters of other diseases of similar sizes. The cluster tagging and multi level clustering process was able to identify as low as 19 locations of cluster alarms across the whole province of 38 districts. The identification of high disease hotspots and their dynamics with the neighboring hotspots of multiple diseases allows identification of locations with higher need of related public health resources. This identification is very critical for national health agencies for optimal allocation of resources and devising an effective intervention programs.INDEX TERMS cluster analysis machine learning , multiple diseases , public health informatics, public health management , spatio temporal analysis

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Section: Related Workmentioning

confidence: 99%

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Khalique

Khan

2021

IEEE Access

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“…More specifically, it should be noted that there is one study about real-time fall detection practices originated from the United States of America (Serpen& Khan, 2018), and one study originated from Spain (Yacchirema et al, 2018). One study that has basis development by using a conventional device (Soewito et al, 2015) which originated from Indonesia and one study from Brazil (Rodrigues et al, 2018). A total of two studies conducted in Japan (Kong et al, 2018;Sumiya et al, 2015) practice image analysis in detecting fall events and one study originated from Taiwan (Lu & Chu, 2018).…”

Section: General Findings and Characteristics Of The Study Included In The Reviewmentioning

confidence: 99%

“…Another classification was the wearable device, which consists of four studies. One study from (Rodrigues et al, 2018) used the conventional device, the sensor calculated the coordinate and generated into the MATLAB. The data go through the developed model in deciding either the fall event occurs.…”

Section: Detection Techniquementioning

confidence: 99%

Classification of Fall Detection System for Elderly: Systematic Review

et.al¹

2021

TURCOMAT

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Elderly are the world’s largest growing population, categorized over the age of 60 to 65 years. They are the ones who prone to fall due to their old age and low self-efficacy, thus making them vulnerable to different accidents. Even doing daily activities can also expose the elderly to a fall incident. As a result, it has gained the attention of many researchers in conducting studies related to the elderly daily health care, especially in relation to the fall detection system. This paper aims to provide a systematic review on the classification of fall detection systems for the elderly. This systematic review is designed based on the existing and extensive literature review on fall detection systems guided by the prisma statement (preferred reporting items for systematic reviews and meta-analyses) review method. Based on this systematic review, four overarching themes that provide in-depth information on fall detection to detect fall events have been identified; classification of fall detection, basis development, type of sensor and detection technique. In a nutshell, the fall detection approach has successfully provided an alternative health care services for elderly who choose to live independently. Therefore, it is important to continue to develop a fall detection system that integrates with technology in order to provide a safe living environment for elderly, and for children, it can offer as an alternative for monitoring systems.

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“…One of the observed drawbacks of wearable sensors is that the accuracy of fall classification and detection is impacted by the placement of the sensors. In [36], the authors generated a dataset with accelerometer and gyroscope, worn around the waist, and applied SVM, boosted and bagged decision trees, kNN, k-mean, and hidden Markov model (HMM). It was observed that fine kNN produced an accuracy of 99.4%.…”

Section: Machine Learning-based Wearable Systems For Fallmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

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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.

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Fall Detection System by Machine Learning Framework for Public Health

Cited by 28 publications

References 7 publications

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Classification of Fall Detection System for Elderly: Systematic Review

A Survey on Recent Advances in Wearable Fall Detection Systems

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