Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

Autor: Shenoy VN; Cupertino High School, Cupertino, California., Aalami OO; Stanford University, Palo Alto, California.
Jazyk: angličtina
Zdroj: AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2018 Apr 16; Vol. 2017, pp. 1564-1570. Date of Electronic Publication: 2018 Apr 16 (Print Publication: 2017).
Abstrakt: Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient's records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 2022 1 , health monitoring platforms, such as Apple's HealthKit 2 , can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%.
Databáze: MEDLINE