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This team focuses on instrumentation with wearable sensors, including IMU, goniometers, pressure sensors and a novel epidermal flexible EMG. We are interested in analyzing the information carried by these sensors to develop intent recognition algorithms and gait state estimation using machine learning techniques. We implemented a full data collection system including motion capture and force plates in a configurable experimental area that includes ramps, stairs and ground level walking. With this study, we can evaluate the biomechanics of ambulation at different conditions and get a better background for the development of controllers for assistive devices.

Lab members:

Jonathan Camargo

Related work:

Jonathan Camargo Leyva, Will Flanagan, Noel Csomay-Shanklin, Bharat Kanwar, Aaron Young, “A New Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors,” IEEE Transactions on Biomedical Engineering, accepted, March 2021. (DOI)

Jonathan Camargo Leyva, Aditya Ramanathan, Will Flanagan, Aaron Young, “A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions,” Journal of Biomechanics, accepted, February 2021. (DOI)

Jonathan Camargo, Aditya Ramanathan, Aaron Young, “Automated Gap-Filling for Marker-Based Biomechanical Motion Capture Data,” Computer Methods in Biomechanics and Biomedical Engineering, accepted for publication, 2020. (DOI) (Code)