This is an important development for clinical trials. This means that the detection of disease progression could be delayed, and in clinical trials, the types of statistical analysis that can be performed on the data might be restricted.' Wearable sensors provide early detection of progression in Parkinson’s DiseaseProfessor Antoniades' NeuroMetrology Lab have been carrying out experiments to assess whether sensor devices worn by patients on their trunk, wrists, and feet, combined with machine learning, can track the progression of motor symptoms more accurately than traditional rating scales. It is already known that these new techniques can be used to discriminate between healthy older adults, individuals with different severity of Parkinson's disease, and individuals with other Parkinsonian-like disorders. Read the full article, 'Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning', can be read in Nature.