Validity of load rate estimation using accelerometers during physical activity on an anti-gravity treadmill

Susan Nazirizadeh; Maria Stokes; Nigel K Arden; Alexander Ij Forrester

doi:10.1177/2055668320929551

Validity of load rate estimation using accelerometers during physical activity on an anti-gravity treadmill

J Rehabil Assist Technol Eng. 2021 Jun 2:8:2055668320929551. doi: 10.1177/2055668320929551. eCollection 2021 Jan-Dec.

Authors

Susan Nazirizadeh^{1

2

3}, Maria Stokes^{2

3}, Nigel K Arden^{3

4}, Alexander Ij Forrester^{1

3}

Affiliations

¹ Faculty of Engineering & Physical Sciences, University of Southampton, Southampton, UK.
² Faculty of Health Sciences, University of Southampton, Southampton, UK.
³ Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, Southampton, UK.
⁴ MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.

Abstract

Introduction: A simple tool to estimate loading on the lower limb joints outside a laboratory may be useful for people who suffer from degenerative joint disease. Here, the accelerometers on board of wearables (smartwatch, smartphone) were used to estimate the load rate on the lower limbs and were compared to data from a treadmill force plate. The aim was to assess the validity of wearables to estimate load rate transmitted through the joints.

Methods: Twelve healthy participants (female n = 4, male n = 8; aged 26 ± 3 years; height: 175 ± 15 cm; body mass: 71 ± 9 kg) carried wearables, while performing locomotive activities on an anti-gravity treadmill with an integrated force plate. Acceleration data from the wearables and force plate data were used to estimate the load rate. The treadmill enabled 7680 data points to be obtained, allowing a good estimate of uncertainty to be examined. A linear regression model and cross-validation with 1000 bootstrap resamples were used to assess the validation.

Results: Significant correlation was found between load rate from the force plate and wearables (smartphone: $R^{2} = 0.71$ ; smartwatch: $R^{2} = 0.67$ ).

Conclusion: Wearables' accelerometers can estimate load rate, and the good correlation with force plate data supports their use as a surrogate when assessing lower limb joint loading in field environments.

Keywords: Physical activity monitoring; bootstrapping; linear mixed model; load rate monitoring; smartphone; smartwatch.