Data analytics for wearable IoT-based telemedicine

Abstract

Parkinson’s disease (PD, Parkinson’s) is a common neurodegenerative disease affecting over 10 million individuals worldwide. Its main marker is the loss of dopamineproducing neurons in the substantia nigra, an area of the midbrain. The root cause of PD is currently unknown. Besides, the disease is progressive, and the symptoms worsen as the ones affected grow older. Motor symptoms such as tremors, slowness of movement, and muscular rigidity, along with other non-motor ones, such as trouble with sleep, may occur. The current solutions for PD are medication and, in cases when the disease does not respond to it as much as one would like, a surgical procedure called Deep Brain Stimulation (DBS) as an alternative. Although they don’t suppress or reverse the neurological damage, these solutions do help alleviate the symptoms. For proper dosage of medication and/or calibration of DBS, PD patients go through a screening process during which the progression of the disease is assessed. This process comes, unfortunately, with hurdles. These include the need for doctor visits for a person dealing with several symptoms, and the suboptimal screening frequency given the progressive nature of Parkinson’s. The rise of IoT and the field of Analytics has unlocked new and technology-inclusive means of managing healthcare. With the vast amounts of data spawning from countless sources, along with the advances in communication technologies, it might not come so much as a surprise that Data is at the center of many sectors today. From everyday devices such as watches or smartphones, sensor have become increasingly common due to their smaller size over the years, as well as becoming less expensive. It naturally comes from this fact, then, that many opportunities to make improvements centered around these technological advancements are arising. One of those being in biomedical engineering, where the ubiquity of sensors has improved many facets of how we are able to understand the human body. Parkinson’s Disease management is an area that could greatly benefit from it, and this section will present some possible solutions in the specific applications of PD monitoring and diagnosis. Using physiological sensors and remote-management architectures, can we improve the management of the disease? This thesis was written based on a study in which we recruited 2 healthy participants, and 4 PD patients. Data from UPDRS-III movements was collected with electronic textiles (e-textiles), then processed using time, frequency, and time-frequency domain methods to obtain relevant features, as hallmarks of Parkinson’s. These features were then used in MATLAB’s Classification Learner to build a binary-classification model for each UPDRS task to distinguish between PD and non-PD. These models yielded accuracies ranging from 81.0% to 99.3%.