PhD student in Engineering
Published in: Nature
As the world’s population ages, the number of people suffering from age-related neurological movement disorders such as Parkinson’s disease has been on the rise. Pathological hand tremor (PHT) is a common symptom that has a considerable impact on patients’ quality of life, and accurate estimations of PHTs are therefore essential to develop rehabilitation and support technologies. Led by Soroosh Shahtalebi, the PHTNet project tackles the problem with a real-time recurrent model for PHTs that integrates computational modeling and machine learning techniques to maximize the resolution of estimation and improve early diagnosis.