eFurniture for home-based frailty detection using artificial neural networks and wireless sensors

Chang, Yu‐Chuan; Lin, Chung‐Chih; Lin, Pei-Hsin; Chen, Chun-Chang; Lee, Ren-Guey; Huang, Jing-Siang; Tsai, Tsai-Hsuan

doi:10.1016/j.medengphy.2011.09.010

Cited by 32 publications

(36 citation statements)

References 14 publications

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“…Chang et al, 2013 [32] conducted research, the purpose of which was to integrate wireless sensors and artificial neural networks in order to develop a system capable of gathering data and administering the information required to assess frailty as automatically as possible. To do so, they used a measuring device based on household goods in daily use, with a view to providing home-based means of measurement and thus ensuring that health controls would not be confined to healthcare establishments.…”

Section: Resultsmentioning

confidence: 99%

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review

Mugueta-Aguinaga

Garcia‐Zapirain

2017

Aging and disease

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This study analyzes the technologies used in dealing with frailty within the following areas: prevention, care, diagnosis and treatment. The aim of this paper is, on the one hand, to analyze the extent to which technology is present in terms of its relationship with frailty and what technological resources are used to treat it. Its other purpose is to define new challenges and contributions made by physiotherapy using technology. Eighty documents related to research, validation and/or the ascertaining of different types of hardware, software or both were reviewed in prominent areas. The authors used the following scales: in the area of diagnosis, Fried’s phenotype model of frailty and a model based on trials for the design of devices. The technologies developed that are based on these models accounted for 55% and 45% of cases respectively. In the area of prevention, the results proved similar regarding the use of wireless sensors with cameras (35.71%), and Kinect™ sensors (28.57%) to analyze movements and postures that indicate a risk of falling. In the area of care, results were found referring to the use of different motion, physiological and environmental wireless sensors (46,15%), i.e. so-called smart homes. In the area of treatment, the results show with a percentage of 37.5% that the Nintendo® Wii™ console is the most used tool for treating frailty in elderly persons. Further work needs to be carried out to reduce the gap existing between technology, frail elderly persons, healthcare professionals and carers to bring together the different views about technology. This need raises the challenge of developing and implementing technology in physiotherapy via serious games that may via play and connectivity help to improve the functional capacity, general health and quality of life of frail individuals.

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Section: Resultsmentioning

confidence: 99%

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review

Mugueta-Aguinaga

Garcia‐Zapirain

2017

Aging and disease

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“…Alternatively, Chang and colleagues [21] have employed five frailty markers in a neural network model that was used in conjunction with four developed sensor units referred to as eScale, eChair, ePad and eReach. The ePad unit was developed by integrating membrane sensors within a carpet to measure balance performance by step detection.…”

Section: Balance Analysis Using Sensormentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

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“…Data from the savanah of data (Table 1) should be reads as described promptly: results output with a neural network [10,13,15] on equal 1.9119 and 0.9326 for girl children; regarding this outcome data savannah Girls has a degree 3 of obesity and the child would have an obesity grade 2; dependent on the age, BMI, ICC and food intake [11,12,13]. The following figures show numerical results of the prediction system used.…”

Section: Resultsmentioning

confidence: 99%

“…The cooperative school meets the hygenic-Nutrient, where the food consumed within the school are a determining factor in the nutritional status of children, and applying neural network techniques can predict the behavior of infant obesity [9,10,13,15].…”

Section: Hypothesismentioning

confidence: 99%

Neural Backpropagation System for the Study of Obesity in Childhood

Medina-Santiago¹,

Melgar-Paniagua²,

Campos-Reyes³

et al. 2016

ijarai

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Abstract-This paper presents the development of a nutritional system using Backpropagation neural network, that is able to provide a clear and simple prediction problems of obesity in children up to twelve years, based on your eating habits during the day. For the development of this project has taken into account various factors, which are vital for the proper development of infants. A prediction system can offer a solution to several factors, which are not easily determined by convectional means.

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eFurniture for home-based frailty detection using artificial neural networks and wireless sensors

Cited by 32 publications

References 14 publications

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review

Assessment of frailty: a survey of quantitative and clinical methods

Neural Backpropagation System for the Study of Obesity in Childhood

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