Opioid Overdose Real-Time Wearable Monitor with Fuzzy Inference System
Open Access
- Author:
- Vo, Dat Tien
- Graduate Program:
- Electrical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 26, 2018
- Committee Members:
- Robert Anthony Gray, Thesis Advisor/Co-Advisor
Sedig Salem Agili, Committee Member
Seth Wolpert, Committee Member - Keywords:
- Opioid
overdose
fuzzy
inference
system
embedded
wearable - Abstract:
- Opioid-involved deaths may be easily avoided if an injection of Narcan can be delivered on time to a person suffering from an opioid overdose. However, these victims of opioid users often do not find their way to the hospitals in time to receive the Narcan shots, and thus risk unnecessarily losing their lives. Therefore, this project proposes a system that can detect when a person is overdosing so that necessary action such as hypothetically contacting emergency responder, Narcan injection, etc. can be taken in a timely manner. Specifically, the device is aimed to be a wearable technology that performs constant non-invasive physiological and pathophysiological tests to detect when a person becomes apneic, which is the primary overdose reaction as opioid receptors in the respiratory centers become saturated. The innovation in our approach introduced the feasibility of Fuzzy Inference System embedded on a microprocessor and integrated with multiple sensors to diagnose true positive opioid overdoses with high reliability. Fuzzy Inference System features such as Fuzzy sets, IF-THEN rules and Fuzzy logical operation were leveraged in this project to overcome component flaws and the lack of clearly defined physiological thresholds for opoid overdose indicators. In the end, the finding shows that Opioid Fuzzy Inference system can be embedded on open- source hardware with less than 0.2% difference, when compared against MATLAB simulation.