The development of intelligent medical and sports data analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning (DL). DL algorithms have enabled the development of highly accurate systems (with performance comparable to that of human experts, in some cases) and have become a standard choice for analyzing medical and sport data, especially images and videos. Dozens of commercial applications using deep learning to analyze, classify, segment and measure data from different modalities of sensors are currently available. Deep learning methods applied on medical and sports data are contributing to understanding the evolution of chronic diseases, predicting the risk of developing those diseases, and understanding the performance of athletes and their risk of overuse injuries. Researchers in industry, hospitals, sports institutes and academia have published.
The Analysis of Medical and Sports Sensor Data Using Machine Learning (AMSML) workshop aims to contribute to this area and focuses on using machine learning to analyze medical and sports data collected from different sensory devices. The workshop will provide an open forum for general MMM attendees where established researchers will share their experience working in the field of sports and medicine.