INTEL wants to collect data from Parkinson’s patients using wearable devices to monitor symptoms.
The collaboration between Intel and the Michael J. Fox Foundation for Parkinson's Research (MJFF) will yield a multiphase research study using a new big data analytics platform that detects patterns in data collected from wearable technologies used to monitor symptoms.
The idea is to gather enough data and analysis to allow researchers and physicians to measure progression of the disease and accelerate the development of treatments.
According to Dr Todd Sherer, CEO of the MJFF, data science and wearable computing hold the potential to transform the ability to capture and objectively measure patients' actual experience of disease.
Because Parkinson’s symptoms are so variable, it can be hard for researchers to monitor progression of the disease. By capturing more data from thousands of patients, the scientific community could identify previously unidentified features of the disease.
Since wearables can unobtrusively gather and transmit objective, experiential data in real time, 24 hours a day, seven days a week, this allows researchers to collect and analyse hundreds of readings per second from thousands of patients and attaining a critical mass of data to detect patterns and make new discoveries.
Later this year, Intel and MJFF plan to launch a new mobile application that enables patients to report their medication intake as well as how they are feeling. The effort is part of the next phase of the study to enable medical researchers to study the effects of medication on motor symptoms via changes detected in sensor data from wearable devices.
To analyse the volume of data, more than 300 observations per second from each patient, Intel developed a big data analytics platform that integrates a number of software components including Cloudera CDH — an open-source software platform that collects, stores, and manages data.
In the near future, the platform could store other types of data such as patient, genome and clinical trial data. In addition, the platform could enable other advanced techniques such as machine learning and graph analytics to deliver more accurate predictive models that researchers could use to detect change in disease symptoms.