1D-Convolutional Neural Networks can Quantify Therapy Content of Children and Adolescents Walking in a Robot-Assisted Gait Trainer.

Therapy content, consisting of device parameter settings and therapy instructions, is crucial for an effective robot-assisted gait therapy program. Settings and instructions depend on the therapy goals of the individual patient. While device parameters can be recorded by the robot, therapeutic instructions and associated patient responses are currently difficult to capture.

Label noise and self-learning label correction in cardiac abnormalities classification.

. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise.