A Fusion-Based Approach to Identify the Phases of the Sit-to-Stand Test in Older People

Shukla, Brajesh Kumar; Jain, Hiteshi; Vijay, Vivek; Yadav, Sandeep; Hewson, David

doi:10.1109/ncc48643.2020.9056092

Cited by 2 publications

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“… These data is useful to predict falls and other diseases in older adults [ 1 , 2 ]. These data may help to detect changes in muscle power before and after resistance training [3] , [4] , [5] , [6] . These data can be processed to identify the sit-to-stand movement, and the different effects related to training and detraining on neuromuscular function in older adults can be identified [ 7 , 8 ].…”

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confidence: 99%

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Accelerometer data from the performance of sit-to-stand test by elderly people

et al. 2020

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The sit-to-stand test is commonly used by clinicians and researchers to analyze the functional capacity of older adults. The test consists to stand up and sit down from a chair and can be applied either in function of a predetermined number of repetitions to be completed or according to a specific time. The most common tool used by the evaluators is the chronometer, due to its low cost and ease of use. However, this tool may miss some important data throughout the test, such as the stand-up time and the total time of each repetition, as well as other kinematic and kinetic variables. Therefore, it is necessary to develop new cheap and affordable tools to capture these data with reliability. In this perspective, the development of mobile applications can be a valid and reliable alternative for the automatic calculation of different variables with sensors’ data, including acceleration, velocity, force, power, and others. Thus, in this paper, we present a dataset related to the acquisition of the accelerometer data from a commodity smartphone for the measurement of different variables during the sit-to-stand test with institutionalized older adults. Forty participants (20 men and 20 women, 78.9 ± 8.6 years old, 71.7 ± 15.0 kg, 1.57 ± 0.1 m) from five community-dwelling centers (Centro de Dia e Apoio Domiciliário de Alcongosta, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia, and Lar da Aldeia de Joanes) from Fundão, in Portugal, volunteered to participate in the data acquisition. A mobile phone was attached to the waist of the participants to capture the data during the sit-to-stand test. Then, seated in an armless chair with the arms crossed over the chest, the participants stood up and sat down in a chair six times. The stand-up action was ordered by an acoustic signal emitted by the mobile application. All data were acquired with the mobile application, and the outcome measures were the reaction time, total time, stand-up time and movement time. This paper describes the procedures to acquire the data. These data can be reused for testing machine learning or other methods for the evaluation of neuromuscular function in older adults during the sit-to-stand test.

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