Date of Award
2023
Degree Type
Thesis
Degree Name
Master of Science in Interdisciplinary Neurosciences
Department
Interdisciplinary Neuroscience
First Advisor
Kunal Mankodiya
Second Advisor
Susan D'Andrea
Abstract
Parkinson’s Disease (PD) is a neurodegenerative disease characterized by non-motor and motor symptoms due to the death of dopamine-producing cells in the Substantia Nigra (STN). Approximately 1 million people are diagnosed with PD in the US, with a projected growth of 60% by 2037. Some medications try to balance dopamine levels by increasing their bioavailability, others act as dopamine agonists, and others act as acetylcholine antagonists (a neurotransmitter that takes over movement control in the Basal Ganglia). There is no known cure for PD, but medications can help patients control and improve their symptoms. Due to the disease's progressive nature, it is important to track the changes in the patient’s symptomatology. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is the gold-standard PD symptom assessment tool. It consists of a series of evaluations on different aspects of PD symptomatology, including motor and non-motor daily living experiences.
Periodic visits to the clinic can be challenging for patients who often face barriers to mobility, transportation, and health management and maintenance. Additionally, the PD population projection growth will increase the demand for clinical professionals, straining the healthcare system. There is a critical demand to offer accessible telemedicine solutions for PD assessment in remote settings.
This thesis work presents the development, validation, and feasibility testing of an in-home smart data collection system, ReBoot, for monitoring PD symptoms, consisting of a wearable Smart Shoe with embedded pressure and IMU sensors, and a computer for cloud communication. The computer guides patients step by step on specific motor tasks to perform (UPDRS-based). The system was validated in-lab with ten healthy participants. These participants wore the Smart Shoe and an inertial suit (XSens MVN Link inertial suit) while performing the motor tasks. Both systems were compared by obtaining the cross-correlation coefficient for each assessment task and each participant. All the cross-correlation coefficients were above 0.4, being a moderate to high correlation. With this validation method, the ReBoot system can be considered a reliable movement analysis tool. Data obtained from pressure sensors were analyzed against reference videos, showing no significant difference between methods. Once the ReBoot system was validated against a gold standard movement analysis system, three PD participants were recruited to perform a 10-day feasibility study, where OFF and ON medication periods were compared by using a Welch two-sample t-test, where the system showed a statistical difference in one of the motor tasks but not in the others. This might be attributed to the mild to moderate symptoms of the participants and the saturation of the maximum acceleration measured by the Smart Shoe.
The results obtained in this thesis work indicate that the ReBoot System is a promising movement analysis tool. However, iterative improvements in the current design are required to address challenges; to avoid saturating the accelerometer component, local data logging for recovery in case of disconnection, and the inclusion of debugging log files. Likewise, a study should be performed where PD participants perform UPDRS-III tasks on OFF and ON medication states while wearing the ReBoot system and the XSens inertial suit.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Cantón Leal, José Miguel, "ReBoot: A wearable sensing system for in-home Parkinon's motor assessments" (2023). Open Access Master's Theses. Paper 2429.
https://digitalcommons.uri.edu/theses/2429