A Data-Driven Approach for Estimating Postural Control Using an Inertial Measurement Unit

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In this paper, we propose a probabilistic multi-Gaussian parameter estimation technique which addresses the complex relationship between acceleration and ground force signals used to derive a human’s static center of pressure. The intent of this work is to develop an accurate accelerometer-based method for determining postural control and neuromuscular status which is more portable and cost-effective than force plate-based techniques. Acceleration data was collected using an inertial measurement unit while ground reaction forces were simultaneously measured using a force plate. Various metrics were calculated from both sensors and probabilistic data models were built to characterize the relationships between the two sensors. These models were used to predict force-based postural control metrics corresponding to observed acceleration metrics. Data collected from one participant was used as a training set to which the test data of two individuals were then applied. We conclude that converted acceleration-based metrics on average can accurately predict all the corresponding force-based metrics we studied here. Furthermore, the proposed multi-Gaussian parameter estimation approach outperforms a more basic linear transformation technique for 75% of the metrics studied, as evidenced by an increase in correlation coefficients between true and estimated force plate metrics.
Postural control, Neuromuscular status, Inertial measurement unit, Force plate, Center of pressure, Probabilistic data modeling, Gaussian mixture models
Giachin, A, Steckenrider, JJ, & Freisinger, G. "A Data-Driven Approach for Estimating Postural Control Using an Inertial Measurement Unit." Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition. Volume 5: Biomedical and Biotechnology. Virtual, Online. November 1–5, 2021. V005T05A042. ASME. https://doi.org/10.1115/IMECE2021-70518