Assessment of postural sway with accelerometers: A comparison with force plate measures
Authors: Slunecko Michelle , Csapo Robert 
Published: September 2024

Introduction & Purpose
Postural control is essential for maintaining balance during static and dynamic tasks. It is typically measured by centre of pressure (CoP) deviations using a ground reaction force plate (FP). Alternatively, more affordable and accessible accelerometer-based measures have been proposed. However, no consensus on the best protocol exists (Pinho et al., 2019). This study investigates the feasibility of assessing postural sway using linear acceleration data, considering sensor placement, task difficulty and analysed parameters.
Methods
The University of Vienna Ethics Committee approved this study (Ref. Nr. 01108). Ten healthy adults (6 M/4 F; 37.9 ± 14.8 years; BMI = 23.3 ± 2.3) performed eleven balance tasks, seven easy tasks from the Berg Balance Scale and four harder tasks with eyes closed, each lasting 20 seconds. Postural sway was assessed using a FP and five inertial measurement units (IMUs) attached to the upper spine (C7), lower spine (L1), sacrum and both thighs. Data were synchronously collected at 1500 Hz, zeroed, band pass filtered (3rd order Butterworth, 0.3-10 Hz; Martinez-Mendez et al., 2012), and cropped to 19 s. Outcomes included CoP path length, root mean square of sway amplitude in the anteroposterior (AP) and mediolateral (ML) directions for the FP, and RMS of the linear acceleration in AP and ML directions, along with their sum, for the ACC. Combinations of the two AP, two ML and two overall parameters were analysed by winsorised Pearson correlations and Bland Altman limits of agreement of z-scores for each task and for each sensor site. Fisher's Z-transformed correlation coefficients and limits of agreement were compared across task difficulty, parameter combinations, and sensor placement sites using a 3-way ANOVA.
Results
Pearson coefficients correlating the ACC parameters with the FP parameters averaged 0.60 (95% CI [0.54, 0.66]) for the easy tasks and 0.78 (95% CI [0.726, 0.84]) for the hard tasks, with no significant differences between sensor sites. A significant interaction (p < 0.001) was found between task difficulty and parameter combination. Simple main effect analysis showed no difference between correlations of AP, ML and overall parameters for hard tasks but for easy tasks, where overall parameters showed the highest correlation, followed by ML and then AP parameters, all differing significantly from each other. The Bland Altman mean difference of z-scores between FP and ACC parameters was on average -0.002 (95% CI [-0.027, 0.024]). The lower and upper limits of agreement were on average -0.72 (95% CI [-0.84, -0.62]) and 0.73 (95% CI [0.62, 0.83]) respectively.
Discussion
The correlations between FP and ACC were higher during more challenging tasks, while the limits of agreement were broader and less precise in these tasks (data not shown). No optimal sensor placement site was identified as correlation and agreement between the two methods did not differ significantly between sites. The broad limits of agreement suggest relatively low precision of ACC measures compared to FP. Overall parameters often outperformed ML and AP parameters in terms of correlation and precision.
Conclusion
Accelerometer-based measures of postural sway showed high correlations with force plate measures but low precision when tasks where difficult enough to challenge participants’ balance. Sensor placement site did not influence correlation and precision.
References
Pinho, A. S., Salazar, A. P., Hennig, E. M., Spessato, B. C., Domingo, A., & Pagnussat, A. S. (2019). Can we rely on mobile devices and other gadgets to assess the postural balance of healthy individuals? A systematic review. Sensors, 19(13), Article 13. https://doi.org/10.3390/s19132972
Martinez-Mendez, R., Sekine, M., & Tamura, T. (2012). Postural sway parameters using a triaxial accelerometer: Comparing elderly and young healthy adults. Computer Methods in Biomechanics and Biomedical Engineering, 15(9), 899–910. https://doi.org/10.1080/10255842.2011.565753