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Differentiating Sitting and Lying Using a Thigh-Worn Accelerometer.

Kate Lyden¹, Dinesh John, Philippa Dall

¹1Division of Endocrinology, Metabolism, and Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO; 2School of Health Sciences, Northeastern University, Boston, MA; 3Department of Health Sciences, Glasgow Caledonian University, Glasgow, UNITED KINGDOM; 4School of Health and Life Sciences, University of Salford, Manchester, UNITED KINGDOM.

Medicine and Science in Sports and Exercise

|October 31, 2015

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A thigh-worn accelerometer accurately distinguishes between lying and sitting using thigh rotation angles. This automated method objectively measures daily lying time, crucial for understanding sedentary behavior

Area of Science:

Biomedical Engineering
Physical Activity Measurement
Wearable Technology

Background:

Sedentary behavior assessment often relies on self-report, which can be inaccurate.
Distinguishing between lying and sitting is important for understanding health impacts of sedentary time.

Purpose of the Study:

To develop and validate a classification algorithm for differentiating lying from sitting events.
Utilize data from a thigh-worn triaxial accelerometer for objective sedentary behavior classification.

Main Methods:

Collected 7-day activity data from 14 sedentary workers using activPAL3™ monitors.
Developed an algorithm based on thigh's y-axis angle of rotation to classify sedentary events.
Defined lying as events occurring "in-bed" and sitting as events occurring "not-in-bed" based on participant diaries.

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Last Updated: Mar 31, 2026

Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

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Published on: April 21, 2017

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Main Results:

The algorithm achieved 96.7% sensitivity in classifying "in-bed" sedentary time as lying.
The algorithm demonstrated 92.9% specificity in classifying "not-in-bed" sedentary time as not lying.

Conclusions:

Thigh-worn triaxial accelerometers can reliably classify sedentary events into lying and sitting.
This automated method enables objective measurement of diurnal lying duration, including sleep time.
Objective data on lying and sitting can advance research on sedentary behavior's health outcomes.