Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition
This work addresses the delay in healthcare monitoring for patients with chronic conditions like diabetes and cardiovascular issues, though it is incremental as it applies existing digit recognition methods to a new medical data context.
This research tackled the problem of manually recording medical monitor data by developing a mobile application that captures and transmits this data to doctors quickly, achieving 98.2% accuracy in digit recognition from medical monitors.
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 data 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, health monitoring platforms, such as Apple's HealthKit, 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%.