Transforming the Objective Real-world measUrement of Symptoms (TORUS)

The holy grail of a cure for Parkinson's disease has been held back for decades by the extreme difficulty of measuring whether proposed new drugs actually improve the patient's symptoms and daily life. The TORUS research programme aims to solve that problem through a novel platform of sensing technologies for use in patients' own homes along with an advanced data fusion and machine learning pipeline that measures changes in specific mobility-related behaviours over weeks and months.

Neurological disorders are the single largest cause of disability - in the UK alone there are 150,000 people with Parkinson's disease, the fastest-growing neurological condition. Parkinson's disease is incurable, and symptoms worsen over time, severely reducing quality of life and creating heavy burdens on the patient's family. The cost to the NHS each year is £375M, with families and social services contributing a further £877M (Centre for Health & Social Care Research, 2017). The number of people with Parkinson's disease in the UK is expected to nearly double by 2040.

To get a new drug to market, pharmaceutical (pharma) companies need to evidence by a clinical trial whether the drug improves symptoms such as freezing when walking, tremor and the ability to undertake daily tasks such as standing up from sitting or moving between rooms. Currently, to gather this evidence, each patient in the trial must travel to hospital to be observed performing standardised tests by a clinician. However, these (at most) monthly "snapshot" samples of symptoms are a poor representation of the hour-by-hour variation of the patient's true symptoms.

The vision of TORUS is therefore to create the capability to autonomously, continuously and objectively measure symptoms of illness (mobility-related activities of daily living) many times every day during the clinical trial of a new drug, in the patient's own home and for months at a time

TORUS will achieve this goal by using a wrist-worn wearable integrated synergistically with AI-enabled cameras. The data from the wearable and cameras is fused to give metrics of the quality of mobility-related activities. The programme concluses with a clinical proof of concept.