Software for the Analysis of Data on Parkinson's Disease Symptoms Collected Using Wearable Technology
Computational Science 2017, 2017
Smartphone apps and wearable technology with built-in sensors allow people with Parkinson's disease (PD) to track motor symptoms such as tremor. The nature of this technology allows people with PD to track symptoms as frequently as several times a day. This generates large amounts of data that cannot be analyzed manually, so software capable of automated analysis is needed.
We plan to develop free software for processing data on Parkinson's motor symptoms collected using wearable technology. We believe this product can help researchers, clinicians, and people with Parkinson's analyze and interpret the data.
To engineer the software, we will select mathematical methods appropriate for its development; design, build and test the software; and verify its performance using data collected from smartphones and wearable technology in recent research studies. We will release the software as open source. This means any individual or organization will be able to download the software, change it according to their needs and use it in any way appropriate to achieve their goals. We also will establish a community of contributors who will continue to expand and improve the software long term.
Impact on Diagnosis/Treatment of Parkinson's disease:
The software will provide a standard way to diagnose Parkinson's, identify problems earlier in disease course (e.g., medication side effects), and track disease progression using smartphones and wearable technology.
Next Steps for Development:
Successful implementation of this project will help demonstrate the usefulness of smartphones and wearable technology in accurately determining the stage of Parkinson's and predicting disease progression.
Professor of Pervasive Computing at Birkbeck College, University of London
Location: London, United Kingdom