Wearable in situ multi-axis motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size adjustable inductive sensor for kinematic motion tracking are introduced. The sensor was highly stable and able to track high frequency (>15Hz) and high strain rates (>450%/s). Four sensors were used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm was used to predict the sagittal, transverse, and frontal hip joint angle using the raw signals from the sport shorts strain sensors during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R2 = 0.98 and an RMSE of 2° in all three planes. This present study provides an alternative strain sensor to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that could find applications in smart wearable devices, robotics, or direct integration into textiles.
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Thesis advisor: Menon, Carlo
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