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

Abstract

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.

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

“…We showed that impaired participants tend to exhibit more unstable forces when standing, which could be used as indicators of potential falls and trigger a robot to take preventative measures. Building upon previous datasets found in literature [23][24][25], this dataset combines full-body biomechanics with force data from multiple sensors, allowing for more thorough analysis and comparison between sensors. Further exploration of this dataset could involve analysing participant velocities and accelerations to determine the maximum forces that a STSTS assistive robot would need to be able to apply to operate safely for a wide variety of users.…”

“…Moreover, the data from these studies are usually difficult to retrieve and proprietary, and to the authors' knowledge, no open-source database containing full biomechanical data for sit-to-stand-to-sit (STSTS) is currently available. Some datasets do exist but are quite limited, such as only using one accelerometer to measure trajectories [23], solely measuring force data from a balance board [24], or using unnatural or 'perturbed' motions [25]. Also uncommon are datasets containing data from users with pathological conditions [26].…”

An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

“…For this study, we used the single sit-to-stand test, starting with a 10 min general warm-up consisting of light walking and mobility exercises, as described in Marques et al [ 51 ]. The participants were equipped with a smartphone placed inside a waistband (Sports Waistband Universal Phone Holder), which was in turn attached to the waist.…”

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

“…The kinematics of the STS transitions have been measured in many, typically laboratory-bound, studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. For example, a smartphone acceleration sensor has been used to quantify STS transition and was found to be valid [ 6 , 7 ].…”

“…The kinematics of the STS transitions have been measured in many, typically laboratory-bound, studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. For example, a smartphone acceleration sensor has been used to quantify STS transition and was found to be valid [ 6 , 7 ]. The widely used 5x STS test [ 8 , 9 , 10 ], 10x STS test [ 11 ] and sit-to-walk [ 12 ] movement kinematics of the different phases have also been interpreted successfully with body-fixed gyroscope and/or accelerometer sensors.…”