Characterising and minimising sources of error in inertial body sensor networks

Authors:
Shanshan Chen;Jeff S. Brantley;Taeyoung Kim;Samuel A. Ridenour;John Lach
Affiliations:
Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
Venue:
International Journal of Autonomous and Adaptive Communications Systems
Year:
2013

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Abstract

Body sensor network BSN applications depend on accurate and precise data from body-worn devices, but issues related to sensor variations, body mounting variations, integration drift and node-to-node synchronisation can dramatically impact the quality and reliability of collected data and, ultimately, application fidelity. Characterising and addressing these sources of error which are both static and dynamic e.g. sensors suffer from static manufacturing variability and dynamic environmental impacts - within the context of application requirements is therefore necessary for the viability of such applications. This work characterises and addresses errors related to node synchronisation, sensor and mounting calibration and integration drift on a case study application - knee joint angle as measured during walking by the TEMPO 3.1 inertial BSN platform. Using the Vicon® optical motion capture system to provide ground truth, synchronisation, calibration and drift error are quantied, and the efficacy of solutions for reducing such errors is evaluated.