In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System

Carol Maher, Kylie A. Dankiw, Ben Singh (), Svetlana Bogomolova and Rachel G. Curtis
Additional contact information
Carol Maher: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia
Kylie A. Dankiw: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia
Ben Singh: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia
Svetlana Bogomolova: Centre for Social Impact, College of Business, Government and Law, Flinders University, Adelaide, SA 5001, Australia
Rachel G. Curtis: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia

Future Internet, 2024, vol. 16, issue 6, 1-18

Abstract: The Neo Care home monitoring system aims to detect falls and other events using artificial intelligence. This study evaluated Neo Care’s accuracy and explored user perceptions through a 12-week in-home trial with 18 households of adults aged 65+ years old at risk of falls (mean age: 75.3 years old; 67% female). Participants logged events that were cross-referenced with Neo Care logs to calculate sensitivity and specificity for fall detection and response. Qualitative interviews gathered in-depth user feedback. During the trial, 28 falls/events were documented, with 12 eligible for analysis as others occurred outside the home or when devices were offline. Neo Care was activated 4939 times—4930 by everyday household sounds and 9 by actual falls. Fall detection sensitivity was 75.00% and specificity 6.80%. For responding to falls, sensitivity was 62.50% and specificity 17.28%. Users felt more secure with Neo Care but identified needs for further calibration to improve accuracy. Advantages included avoiding wearables, while key challenges were misinterpreting noises and occasional technical issues like going offline. Suggested improvements were visual indicators, trigger words, and outdoor capability. The study demonstrated Neo Care’s potential with modifications. Users found it beneficial, but highlighted areas for improvement. Real-world evaluations and user-centered design are crucial for healthcare technology development.

Keywords: home monitoring system; technology; fall detection; user perceptions; real-world evaluation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/6/197/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/6/197/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:6:p:197-:d:1407479

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().