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.