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Simulated sensor data for data-driven biomechanical estimations

Halldór Kárason

  • 30.8.2022, 12:00 - 13:00

The Institute of Biomedical Engineering is hosting an open lecture "Simulated sensor data for data-driven biomechanical estimations" by Halldór Kárason, a PhD student at the University of Salerno, Tuesday August 30th at 12:00 in room M208.


Tendon injuries are caused by abnormal gait patterns at the ankle joint. Consequently, an effective strategy to prevent such injuries and monitor the after-injury rehabilitation performance can only be devised if a proper gait pattern measurement/estimation is in place. By measuring biomechanical variables of interest, that are, in the case of the Achilles tendon, the ankle kinematics (joint rotational positions and velocities) and kinetics (joint loads, moment, and tendon forces), tendinopathies can be detected in advance, and corrective actions can be planned. Likewise, the same variables are of interest in monitoring the rehabilitation progress. Data-driven, deep neural networks can directly learn the relationship between sensor data and target biomechanical variables based on lab measurements. Robust data-driven models require a relatively large and representative dataset to learn these relationships while avoiding model generalization. Assessments are performed inside specialized motion laboratories, making large datasets impractical to acquire. Here, we want to see if simulated inertial sensor data and data augmentation of measured data could alleviate data limitation of deep neural networks, estimating biomechanical variables.

P4 FIT (Perspectives For Future Innovation in Tendon Repair)

This project has received funding from the European Union‘s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955685.
Part of H2020-MSCA-ITN-EJD programme.

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