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Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial

Pasan Hettiarachchi¹, Katarina Aili^2,3, Andreas Holtermann^4,5

¹Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 751 85 Uppsala, Sweden.

Sensors (Basel, Switzerland)

|February 12, 2021

Summary

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A refined algorithm using thigh-worn accelerometers can accurately detect lying down time. This advancement allows for cross-brand compatibility in health and posture studies.

Area of Science:

Biomedical Engineering
Physical Activity Measurement

Background:

Assessing body posture, particularly lying down time, is crucial for health monitoring.
Thigh-worn accelerometers are effective for measuring daily physical activity.
Existing algorithms for detecting lying down are often proprietary and lack cross-brand compatibility.

Purpose of the Study:

To refine and validate an algorithm for detecting lying down using raw data from thigh-worn accelerometers.
To develop a universally applicable algorithm for diverse accelerometer brands.

Main Methods:

Adult participants (n=97) wore thigh and upper back accelerometers for 7 days.
An open-source algorithm (Acti4) provided sedentary bout data as input.
The refined algorithm incorporated thigh rotation, acceleration standard deviation, and sedentary bout duration criteria.

Keywords:

ProPASS accuracy bedtime daily activity objective measurement physical activity physical behaviour posture sedentary behaviour

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Main Results:

The refined algorithm demonstrated good agreement with reference measurements for lying down time.
Mean differences were +2.9 min (development) and +6.5 min (validation) per day.
The algorithm showed potential for use across different accelerometer brands.

Conclusions:

The refined algorithm provides a valid method for estimating lying down time using thigh-worn accelerometers.
This advancement enhances the utility of accelerometers in diverse research settings.
The algorithm's cross-brand compatibility facilitates broader application in health and activity studies.