Fall-risk screening system framework for physiotherapy care of elderly

Rueangsirarak, Worasak; Atkins, Anthony; Sharp, Bernadette; Chakpitak, Nopasit; Meksamoot, Komsak

doi:10.1016/j.eswa.2012.02.023

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…In the work of Bongue et al (2011), falls were tried to be predicted by a screening tool with only five risk factors (gender, living alone, psychoactive drug use, osteoarthritis, and previous falls) and one clinical test. Rueangsirarak et. al.…”

Section: Discussionmentioning

confidence: 99%

A decision support system for fall risk through biomechanical characteristics: A strikingapplication

Gülbandılar¹,

Sarı²,

Cimbiz³

2018

AKU-J. Sci. Eng.

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Düşme ve düşmeden kaynaklı yaralanmaları önlemek için aktif insanların düşme riskini değerlendirecek yeni araçların geliştirilmesi gereklidir. Bu makale, hangi parametrelerin düşme riskinde ve risk düzeyinde etkili olduğunu incelemeyi ve böylece de bir algoritmayı geliştirmeyi amaçlamaktadır. Bu amaçlara ulaşmak için, çok sayıda değişkeniirdeleyerek, yalınbir algoritma üretilmiştir. Bu algoritma karar ağacı ve entropi üzerine kurulmuştur. Bu algoritmayı üretmek için, 24 gönüllü ve 46 adet düşme riskinin değişkeni kullanılmıştır. Kikare analizi sonuçlarına göre; fizyoterapistin muayene teşhisi sonuçları ile algoritma sonuçları arasındaistatistiksel olarak anlamlı ilişki bulunmuştur(p<0.001 ve kappa=0.852).Geliştirilen sistem,kısa süreli bir eğitim ile klinisyen/klinisyen olmayan kişiler tarafından kullanılmak üzere tasarlanmıştır. Sonuç olarak, var olan verilerimiz sınırlı olmasından dolayı, bu algoritmayı yaygın klinik/klinik dışı kullanım için önermeden önce farklı özelliklere sahip daha geniş bir popülasyonda test edilmelidir.

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Section: Discussionmentioning

confidence: 99%

A decision support system for fall risk through biomechanical characteristics: A strikingapplication

Gülbandılar¹,

Sarı²,

Cimbiz³

2018

AKU-J. Sci. Eng.

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“…Many older adults prefer aging in place within their own homes, which has been associated with increased well-being [41]. Moreover, as the ratio between the aging population and caregivers continually increases [40] and families live farther apart, this circumstance often becomes a necessity.…”

Section: Introductionmentioning

confidence: 99%

Levels of Automation for a Mobile Robot Teleoperated by a Caregiver

Olatunji

Potenza

Kiselev

et al. 2022

J. Hum.-Robot Interact.

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Caregivers in eldercare can benefit from telepresence robots that allow them to perform a variety of tasks remotely. In order for such robots to be operated effectively and efficiently by non-technical users, it is important to examine if and how the robotic system’s level of automation (LOA) impacts their performance. The objective of this work was to develop suitable LOA modes for a mobile robotic telepresence (MRP) system for eldercare and assess their influence on users’ performance, workload, awareness of the environment, and usability at two different levels of task complexity. For this purpose, two LOA modes were implemented on the MRP platform: assisted teleoperation (low LOA mode) and autonomous navigation (high LOA mode). The system was evaluated in a user study with 20 participants, who, in the role of the caregiver, navigated the robot through a home-like environment to perform control and perception tasks. Results revealed that performance improved in the high LOA when task complexity was low. However, when task complexity increased, lower LOA improved performance. This opposite trend was also observed in the results for workload and situation awareness. We discuss the results in terms of the LOAs’ impact on users’ attitude towards automation and implications on usability.

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“…Elderly population growth is bringing significant challenges requiring more healthcare services and facilities [ 1 , 2 , 3 , 4 , 5 ]. Most elderly people have various health problems, such as memory problems, decreased ability to help themselves, fainting, or falling easily [ 6 ]. These problems cause the elderly to need care or monitoring by a caregiver [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

Muangprathub

Sriwichian

Wanichsombat

et al. 2021

IJERPH

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A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.

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Fall-risk screening system framework for physiotherapy care of elderly

Cited by 11 publications

References 5 publications

A decision support system for fall risk through biomechanical characteristics: A strikingapplication

A decision support system for fall risk through biomechanical characteristics: A strikingapplication

Levels of Automation for a Mobile Robot Teleoperated by a Caregiver

A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

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