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Quantification of postural stability in older adults using mobile technology

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Abstract

Traditional biomechanical systems used to capture kinematic data have shown that declines in postural stability are frequently present in older adults and neurological populations. Recent advances in processor speed and measuring capabilities of on-board electronics within mobile devices present an opportunity to gather kinematic data and apply biomechanical analyses to potentially quantify postural stability. The aim of this project was to determine if the kinematic data gathered using a mobile device were of sufficient quantity and quality to characterize postural stability in older adults. Twelve healthy older adults completed six different balance conditions under altered surface, stance and vision. Simultaneous kinematic measurements were gathered from a three-dimensional motion analysis system and iPad during balance conditions. Correlation between the two systems was significant across balance conditions and outcome measures: peak-to-peak (r = 0.70–0.99), normalized path length (r = 0.64–0.98), root mean square (r = 0.73–0.99) of linear acceleration, 95 % volume (r = 0.96–0.99) of linear and angular acceleration and total power across different frequencies (r = 0.79–0.92). The mean absolute percentage error metric, used to evaluate time-series measurements point-by-point, indicated that when measuring linear and angular acceleration, the iPad tracked the motion analysis system with average error between 6 and 10 % of motion analysis measurements across all balance conditions. Collectively, similar accuracy with the iPad compared to motion capture suggests the sensors provide sufficient accuracy and quality for the quantification of postural stability in older adults. The objectivity, portability, and ease of use of this device make it ideal for use in clinical environments, which often lack biomechanical systems.

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Acknowledgments

The authors wish to thank Mike Buss, who led the development of the Cleveland Clinic Balance App, Mark Gustetic, Joshua Hirsch, and David Schindler for assistance in data processing along with Matt Streicher who contributed to data collection with the motion analysis system. This study was supported by R01NS073717-01 and the Parkinson's Disease Foundation.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, USA

    Sarah J. Ozinga & Jay L. Alberts

  2. Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, USA

    Sarah J. Ozinga

  3. Center for Neurological Restoration, Cleveland Clinic, Cleveland, OH, USA

    Jay L. Alberts

  4. Cleveland FES Center, L. Stokes Cleveland VA Medical Center, Cleveland, OH, USA

    Jay L. Alberts

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  1. Sarah J. Ozinga
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Correspondence to Jay L. Alberts.

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Ozinga, S.J., Alberts, J.L. Quantification of postural stability in older adults using mobile technology. Exp Brain Res 232, 3861–3872 (2014). https://doi.org/10.1007/s00221-014-4069-8

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