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35/100

✓ Peer-reviewed alzhub usefulness score 35 out of 100. Clinical safety

Topic: Risk, prevention and care in population studies

Falls are a leading cause of morbidity and mortality in older adults, especially among nursing home residents. Falls occur more commonly among older adults with dementia than among those without dementia. Moreover, half of nursing home residents have moderate to severe cognitive impairment. While less than 5% of older adults live in nursing homes, they account for 20% of deaths from falls in this age group. In addition, 78% of older adults who fall need help in getting up from the floor. The consequences of falling, such as prolonged lying on the floor, can produce severe and prolonged health effects. The acute shortage of staff in nursing homes, especially during evening, night and weekend shifts, can delay the detection and response to falls. There are various systems designed to detect falls and alert staff, including those utilizing wearable devices, ambience sensors and cameras (vision) as well as fusion systems. They each have their advantages and drawbacks. In this NIA-funded SBIR grant, we are developing and testing the feasibility of a fall detection and prevention system that addresses the drawbacks of previous systems. We anchor our approach on the deployment of an autonomously navigating robot equipped with a mounted infrared camera and machine learning software designed to detect the risk of falls and falls themselves. The robot will patrol resident rooms during evening and night shifts and alert the staff, allowing them to evaluate the fall risk or fall alert presented by the robot video camera and determine whether indeed a resident has fallen or is at risk of falling, and take appropriate action.

Key findings

There are various systems designed to detect falls and alert staff, including those utilizing wearable devices, ambience sensors and cameras (vision) as well as fusion systems.
They each have their advantages and drawbacks.
In this NIA-funded SBIR grant, we are developing and testing the feasibility of a fall detection and prevention system that addresses the drawbacks of previous systems.
We anchor our approach on the deployment of an autonomously navigating robot equipped with a mounted infrared camera and machine learning software designed to detect the risk of falls and falls themselves.
The robot will patrol resident rooms during evening and night shifts and alert the staff, allowing them to evaluate the fall risk or fall alert presented by the robot video camera and determine whether indeed a resident has fallen or is at risk of falling, and take appropriate action.

Why it matters

This paper describes an early-stage project to develop and test the feasibility of a robot with an infrared camera and machine learning software to detect falls and fall risk in nursing homes. It is about fall detection technology, not about treating, curing or preventing Alzheimer's disease.

Study details

Evidence strengthLow

How this study scored 35/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Advances in Geriatric Medicine and Research)
Journal's citation record	0 / 4	Advances in Geriatric Medicine and Research: journal h-index 13 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.3 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 2.2× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Stefan Gravenstein: h-index 53 (OpenAlex, whole career)
Institution's track record	1 / 3	Brown University: institution h-index 1,120 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 5 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 39949787)
Free to read	2 / 2	Free to read
Total	35 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Malinsky, Y., McNicoll, L., & Gravenstein, S. (2025). Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls. Advances in Geriatric Medicine and Research. https://doi.org/10.20900/agmr20250001

Vancouver

Malinsky Y, McNicoll L, Gravenstein S. Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls. Advances in Geriatric Medicine and Research. 2025. doi:10.20900/agmr20250001. PMID: 39949787.

BibTeX

@article{malinsky2025-39949787,
  author  = {Malinsky, Yuval and McNicoll, Lynn and Gravenstein, Stefan},
  title   = {{Robots in Nursing Homes: Helping Nurses Detect and Prevent Falls}},
  journal = {Advances in Geriatric Medicine and Research},
  year    = {2025},
  doi     = {10.20900/agmr20250001},
  pmid    = {39949787},
  url     = {https://doi.org/10.20900/agmr20250001}
}

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