IOT based Sensor System for 24x7 monitoring movement disorder symptoms using machine learning

Parkinson's Disease (PD) owing to its highly heterogeneous symptoms and progression is a tough problem for automated monitoring. The current method of monitoring the disease involves physical records made by the patient or the care giver about the response to orally administered dopamine precursor for disease management. Since this is indispensable data required by the medical experts for titration of medication it puts the burden of frequent record keeping on them. We aim to use a combination of Non-Contact sleep monitoring system and wearable inertial measurement units to monitor Parkinson's disease progression and response to medication. Through monitoring changes in vitals like heart rate, breath rate and sleep disturbances at night and by keeping track of body movement in the day, we aim to create a 24 hour monitoring system which can provide reliable data for medical experts for better management of the disease with minimal intrusion into the patient's everyday life.

Automated summary

Parkinson's Disease (PD) is a challenge for automated monitoring due to its heterogeneous symptoms and progression. The current method involves physical records made by the patient or care giver about the response to medication, which adds burden to medical experts. Our aim is to use a combination of Non-Contact sleep monitoring system and wearable inertial measurement units to create a 24-hour monitoring system that can provide reliable data for better disease management with minimal intrusion.