Input - Click on the “+” button in the video player and upload a single person featured video with good resolution video with less than 40mb.
Output - Infers the video with estimated heart rate in a JSON format.
Note: Upload a video with 25-30 FPS where the person should be in good light conditions, with no movements for optimal performance. Uploading any video that doesn't relate to the model might provide you unexpected results.
Using advanced non-invasive computer vision techniques to estimate the heart rate of the person by iterating over a sequence of frames is achieved through this modelIt allows us to visually see how the person's heart rate changes dynamically. Non-contact video-based physiological measurement has many applications in health care, in the wake of Covid-19 the significance of virtual doctor consultation has become very common as well as routine check of heart rate is also common amongst the general public, using non-contact video-based heart rate estimation can be very simple and affordable to many.
As our lives are becoming more mechanical to work it is necessary to have a continuous monitoring of heart rate, which provides the insides of the health over a period of time. It's a well known science that heart beats can carry a lot more information about the person than just the beats per minute. Such analysis can help a great deal in diagnosing and treating people with mental health issues including depression and dementia. Aspects related to overall patient care gets easier when combined with emotion analysis as well.
Remote Heart Rate Monitoring, Fitness Trainings, Medical Conditions etc.,
TensorGo Platform provides you with a one stop solution to customize the our API offerings as per your use case by mixing and matching the existing APIs or requesting for a new custom model. This accelerates the development of use cases with minimal or no code towards deep learning applications.