Hot Pink

Hot Pink

Video
Project / product name: Heart rate detection through a smartphone camera
Team leader: Anastasia Surikova
Challenge: no. 1: Heart rate detection using mobile phone
Problem: The challenge was to detect the heart pulse from the finger on the hand through a smartphone camera. The user should have an option to share the result with the doctor.
Solution: We developed a web/mobile app with the possibility to measure, evaluate and share heart pulse detected by a smartphone camera. Other functionalities are exporting data in csv format, viewing patients' history, or sending invitation codes to connect patients with a doctor. The app is available for both android and ios.
Impact: The application is available free of charge for users, enabling improvement and monitoring of health care without any additional cost for a patient or a hospital. Thanks to the modular open-source, it is possible to change and add functionalities as needed. The application allows both patient and doctor to collect recorded data over time and work with them both on a scale for the doctor taking care of the patient and in an anonymized form on a mass scale.
Feasibility & financials: We have created a fully functional application that allows both doctors and patients to monitor and detect abnormalities in the heart rate. Distribution of the application is possible through Google Play and App Store. The application is available free of charge without financial impediment on the part of the patient. Long-term storage of data on the hospital side is possible after integrating the application with the given hospital system, for example, through FHIR.
What you have built at the hackathon - text explanation + code (e.g. GitHub link): https://github.com/mild-blue/hackhealth2021 ikem test lite -> mobile app https://hotpink.z6.web.core.windows.net/login -> web We created our frontend in Ionic. Thanks to that, we support all major mobile platforms and the web. First, we upload the recorded video to our backend, which is written in .NET 6. Then the Python video processing service analyzes the video using OpenCV and SciPy. The results are stored in FHIR and can be accessed later along with other patients’ data.
What you had before the hackathon, please mention open source as well: empty GitHub repository, empty web app in Azure, empty mobile app
What comes next and what you wish to achieve: We have achieved all the goals we set at the beginning of the hackathon. Future versions of the application could offer the possibility of real-time video processing while the patient takes a heartbeat through the camera. Based on the collected data, then train the ML model to indicate the probability of specific heart diseases in real-time.