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Michael Lapinski Thesis Defense: A Platform for High-Speed Biomechanical Data Analysis using Wearable Wireless Sensors

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Joseph Paradiso
Michael Lapinski

Humanity's desire to capture and understand motion started in 1878 and has continually evolved to this day. Today, the best-of-breed technologies for capturing motion are marker based optical systems that leverage high-speed cameras. While these systems are excellent at providing positional information, they suffer from an innate inability to accurately provide fundamental parameters such as velocity and acceleration. The problem is further compounded when the target of capture is high-speed human motion. When applied to biomechanical study, this inaccuracy is magnified when higher-level parameters, such as torque and force, are calculated using optical information.

This dissertation presents a a first-of-its-kind, wearable dual-range inertial sensor platform that allows end-to-end investigation of high-level biomechanical parameters. The platform takes a novel approach by providing these parameters more accurately and at a higher fidelity than the current state of the art. The dual-range sensing approach allows accurate capture of both slow-moving motion and that which pushes the limits of human ability. The platform addresses inherent problems with scaling clinical biomechanical analysis to tens of thousands of trials using the sensor platforms data. This end-to-end approach provides mechanisms for rapid player instrumentation, en masse data translation, and calculation of clinically relevant joint forces and torques.

Host/Chair: Joseph A. Paradiso

Participant(s)/Committee

Thomas M. Kepple, Dr. Eric Berkson

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