Modeling Multiple Sclerosis using Mobile and Wearable Sensor Data

This dataset contains several input modalities to understand symptoms of patients diagnosed with multiple sclerosis. This represents one of the very few datasets available to investigate this problem. The dataset consists of data from 74 people with multiple sclerosis (PwMS) and 30 healthy controls in natural settings. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS.