Abstract
Mental health is vital to human well-being, and prevention strategies to address mental illness have a significant impact on the burden of disease and quality of life. With the recent developments in body-worn sensors, it is now possible to continuously collect data that can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment, thereby improving patient experiences and quality of life. However, access to high-quality medical data for research purposes is limited, especially regarding diagnosed psychiatric patients. To this extent, we present the OBF-Psychiatric dataset which comprises motor activity recordings of patients with bipolar and unipolar major depression, schizophrenia, and ADHD (attention deficit hyperactivity disorder). The dataset also contains data from a clinical sample diagnosed with various mood and anxiety disorders, as well as a healthy control group, making it suitable for building machine learning models and other analytical tools. It contains recordings from 162 individuals totalling 1565 days worth of motor activity data with a mean of 9.6 days per individual.