Our approach leverages a convolutional neural network (CNN) architecture to automatically learn high-level representations from raw IMU signals, minimizing reliance on manual feature engineering. Central to our method is a two-stage training strategy that incorporates domain adversarial learning, enabling knowledge transfer between two IMU-based assessment tools: the Ataxia Instrumented Measures cup (AIM-C) and spoon (AIM-S). This strategy enhances learning from each device by exploiting shared underlying representations.
Wednesday, July 8, 2026
Domain Adaptation for IMU Data to Enhance Objective Assessment of Friedreich Ataxia
Tran, M., Ranaweera, K., Ngo, T., Pathirana, P., Milne, S., Horne, M., Delatycki, M., & Corben, L. (2026). Domain Adaptation for IMU Data to Enhance Objective Assessment of Friedreich Ataxia. IEEE Journal of Biomedical and Health Informatics, PP. doi:10.1109/JBHI.2026.3702417
