Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors

Silva, Joana; Sousa, Inês

doi:10.1109/memea.2016.7533778

Cited by 26 publications

(23 citation statements)

References 17 publications

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“…Instrumentation with inertial sensors during the execution of standard tests gives additional quantitative information, such as the duration of the standing phase on TUG, contributing to better assessment, and characterizing a person’s mobility and balance conditions. Another advantage of using inertial sensors is that they eliminate the bias introduced by observation of movements and subjective assessment and the output extracted are potentially more reliable and reproducible [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

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Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

et al. 2018

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BackgroundFalls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors.ObjectiveThe objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling.MethodsThis prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior’s universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test “modified,” and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise.ResultsThe enrollment began in June 2016 and we anticipate study completion by the end of 2018.ConclusionsThe FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate.Registered Report IdentifierRR1-10.2196/10304

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Section: Methodsmentioning

confidence: 99%

“…Recently, in addition to traditional methods, instrumentation has been integrated in some standard assessment tests, adding value to the existing methods because it gives additional quantitative information and eliminates the bias introduced by observation [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

et al. 2018

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“…1) Inertial Sensors: An analysis and segmentation of the TUG test involving inertial sensors were previously reported by Silva and Sousa [16]. Later on, Silva et al [13] presented an analysis of the TUG, STS, and 4Stage tests performed with inertial sensors and a pressure platform, reporting a feature extraction process for both types of sensors.…”

Section: B Feature Extractionmentioning

confidence: 96%

Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Silva

Sousa

Cardoso

2020

IEEE J. Biomed. Health Inform.

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Falls are among the frequent causes of the loss of mobility and independence in the elderly population. Given the global population aging, new strategies for predicting falls are required to reduce the number of their occurrences. In this study, a multifactorial screening protocol was applied to 281 community-dwelling adults aged over 65, and their 12-month prospective falls were annotated. Clinical and self-reported data, along with data from instrumented functional tests, involving inertial sensors and a pressure platform, were fused using early, late, and slow fusion approaches. For the early and late fusion, a classification pipeline was designed employing stratified sampling for the generation of the training and test sets. Grid search with cross-validation was used to optimize a set of feature selectors and classifiers. According to the slow fusion approach, each data source was mixed in the middle layers of a multilayer perceptron. The three studied fusion approaches yielded similar results for the majority of the metrics. However, if recall is considered to be more important than specificity, then the result of the late fusion approach providing a recall of 78.6% is better compared with the results achieved by the other two approaches. Index Terms-Fall risk assessment, inertial sensors, machine learning, pressure platform. I. INTRODUCTION T HE worldwide population aged over 65 is growing rapidly. The consequences of this phenomenon are not only social and health-related, but also economic. The process of aging affects the ability of a person to maintain balance, mobility, and muscle strength and to react properly to unexpected situations such as slipping or stumbling. There are also cross-related factors resulting from health conditions, including loss of auditory

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“…The authors of [58] extracted several features for the recognition of the different phases of the Instrumented TUG test, including RMS, standard deviation, median deviation, interquartile range (IQR), skewness, kurtosis, number of times the magnitude signal crosses the mean value, maximum and second maximum frequencies of the fast Fourier transform (FFT), maximum and second maximum amplitudes of the FFT, minimum, maximum, average of the peak height, energy, and entropy.…”

Section: Other Diseasesmentioning

confidence: 99%

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

et al. 2020

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The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

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Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors

Cited by 26 publications

References 17 publications

Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study

Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

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