i-PROGNOSIS partners presented a poster “Towards unobtrusive Parkinson’s disease detection via motor symptoms severity inference from multimodal smartphone sensor data” at the International Congress of Parkinson’s Disease and Movement Disorders® in September in Nice, France.

The i-PROGNOSIS team showed with their poster that the methods proposed by i-PROGNOSIS for motor symptoms inference show promising PD diagnostic performance in the relatively small clinically evaluated cohorts. The results highlight the potential of evolving these methods into an objective PD screening/monitoring tool that could support clinical diagnosis, drug response assessment and decision making. Passive capturing of the required input data further fosters evaluation of individuals’ natural behavior, as well as long term adherence.

Researchers, physicians and people from the industry who attended the conference were interested in the novel approach of i-PROGNOSIS. Over twenty people showed interest in the passive data capturing from the iPrognosis app and the novel machine learning algorithms that extract Parkinson’s diagnostic indices from the user’s data.

Download a high-resolution version of the poster below: