A systematic review on fall detection systems for elderly healthcare

Purwar, Archana; Chawla, Indu

doi:10.1007/s11042-023-17190-z

Cited by 1 publication

(2 citation statements)

References 82 publications

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“…There are a few systematic review studies about AI for falling in older people in the literature. 23 , 24 These studies showed wearable device‐based technology has shown to be important for subtle changes in biomarkers of gait and balance associated with fall risk and fall detection systems. 23 , 24 …”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence based on falling in older people: A bibliometric analysis

Yenişehir

2024

Aging Medicine

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ObjectivesThis study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective.MethodsThe Web of Science database was searched for titles of English‐language articles containing the words “artificial intelligence,” “deep learning,” “machine learning,” “natural language processing,”, “neural artificial network,” “fall,” “geriatric,” “elderly,” “aging,” “older,” and “old age.” An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis.ResultsThirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was “Engineering Electrical Electronic” with seven articles. The most productive country was the United States, followed by China. The most common words were “injuries,” “people,” and “risk factors.”ConclusionPublications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed.

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Section: Discussionmentioning

confidence: 99%

“… 23 , 24 These studies showed wearable device‐based technology has shown to be important for subtle changes in biomarkers of gait and balance associated with fall risk and fall detection systems. 23 , 24 …”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence based on falling in older people: A bibliometric analysis

Yenişehir

2024

Aging Medicine

View full text Add to dashboard Cite

show abstract

A systematic review on fall detection systems for elderly healthcare

Cited by 1 publication

References 82 publications

Artificial intelligence based on falling in older people: A bibliometric analysis

Artificial intelligence based on falling in older people: A bibliometric analysis

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