A mobile proposal for frailty monitoring by rehabilitation and physical daily activity

Bravo, José; Fontecha, Jesús; Navarro, Fco Javier; Hervás, Ramón

doi:10.1109/icce-berlin.2011.6031839

Cited by 12 publications

(9 citation statements)

References 10 publications

(10 reference statements)

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“…Bravo and colleagues [23] conducted a study of the walking patterns of aged people in their homes. These trials involved extensive walking exercises, where an accelerometer was used to measure disturbance of their balance.…”

Section: Analysis Of Gait Characteristics Using Inertial Sensorsmentioning

confidence: 99%

“…The analysis did not consider the relationship between the degrees of frailty with dual task gait performance. It is evident that gait analysis plays a crucial role in determining frailty [19,23,42] and cognition is one of the parameters used in various clinical frailty instruments [5,13,20]. Therefore, dual-task gait analysis using sensor (vision or inertial) technology appears to accurately predict the degree of frailty.…”

Section: Dual Task Gait Analysismentioning

confidence: 99%

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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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Section: Analysis Of Gait Characteristics Using Inertial Sensorsmentioning

confidence: 99%

Section: Dual Task Gait Analysismentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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“…Authors compared their system with the existing ones and obtained better detection performance and power efficiency. In [22], Bravo et al proposed a frailty monitoring by controlling daily activities. In their system, elderly people were continuously monitored using the data of the accelerometers enabled in their smartphones.…”

Section: Related Workmentioning

confidence: 99%

An architecture and protocol for smart continuous eHealth monitoring using 5G

et al. 2017

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Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth and lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to take measures from the body, and a smartphone in the patient side and a DataBase with an intelligent system which is able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data taken from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there is low number of users, like 100 or 200 users, we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G. The Packet Loss is almost null in 4G until 300 users while in 5G is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use 5G network because it offers low delays and guarantees the availability of bandwidth for all users.

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“…setting resistance, duration and frequency) and to record patient activity while they are carrying out their prescriptions. Bravo et al [32] proposed a mobile system to monitor frailty of elderly people during rehabilitation. Their solution computes a frailty index based on balance, mobility, endurance and physical activity of the patient and triggers an alarm if it reaches a defined threshold.…”

Section: Classification Of Clinical Applicationsmentioning

confidence: 99%

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Hadjidj¹,

Souil²,

Bouabdallah³

et al. 2013

Journal of Network and Computer Applications

139

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Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges.

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A mobile proposal for frailty monitoring by rehabilitation and physical daily activity

Cited by 12 publications

References 10 publications

Assessment of frailty: a survey of quantitative and clinical methods

Assessment of frailty: a survey of quantitative and clinical methods

An architecture and protocol for smart continuous eHealth monitoring using 5G

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

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