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.

Graduate students:

• Jonathan Camargo