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

Area of Science:

• Gerontology
• Biomedical Engineering
• Data Science

Background:

• The sit-to-stand test is crucial for assessing older adults' functional capacity.
• Traditional methods using chronometers lack detailed kinematic and kinetic data.
• There's a need for affordable, reliable tools to capture comprehensive sit-to-stand test data.

Purpose of the Study:

• To present a dataset from smartphone accelerometer data during the sit-to-stand test.
• To enable reliable, automatic calculation of key performance variables.
• To facilitate research into machine learning applications for evaluating older adults' neuromuscular function.

Main Methods:

• Acquired accelerometer data from smartphones attached to the waist of 40 older adults.
• Participants performed six sit-to-stand repetitions, guided by a mobile application's acoustic signal.
• Measured outcome variables included reaction time, total time, stand-up time, and movement time.

Main Results:

• Successfully collected accelerometer data during the sit-to-stand test from a diverse group of older adults.
• The mobile application facilitated standardized data acquisition and calculation of key performance metrics.
• The dataset provides a valuable resource for developing and validating new assessment methods.

Conclusions:

• Smartphone-based accelerometer data offers a cheap and reliable alternative for sit-to-stand test analysis.
• The presented dataset supports the development of advanced algorithms for assessing neuromuscular function in older adults.
• This approach can enhance the clinical and research evaluation of functional capacity in aging populations.