Inspiration:

Heart diseases and suicide are the first and the tenth leading cause of death in the United States respectively. Heath care is getting more and more expensive and seeing a doctor is getting harder. According to research, it was found that there is a great correlation between a persons’ heart rate (more precisely resting heart rate) and depression. It is unfeasible for physicians and psychiatrists to continuously monitor heart rate and mental state. It’s not practical (or objectively possible) for patients to keep logs of their internal states throughout the day.

What it does:

For patients suffering from depression and heart problems, we remove the systematic need of inpatient doctor visits by providing a means to capture their daily mental and physical health progression autonomously. What the service does is real-time and historic data analysis and visualization, cognitive services API. Real time heart rate is streamed from Microsoft band through an android app we built. The data is analyzed and presented on our website. The website can be accessed by the doctor to monitor his patients’ heart rate in between visits. Patients are asked to take daily pictures that are analyzed by Microsoft's emotion API. The website alerts the doctor in case of the patient is having any heart discrepancy or emotional breakdown.

How we built it

An Android app that uses Microsoft's emotion and face APIs, takes a picture of the user and analyze his/her emotions; which are then sent to the dashboard to plot in real time user's emotions as well as heart rate which is being monitored by Microsoft's band. The dashboard is hosted on heroku, it uses node.js and google sheets API to plot the data and alert the Doctor when the heart rate/emotions spikes over a critical value.

Challenges we ran into:

Uploading our data to the cloud took a lot of time. And we got stuck on pulling our data from the cloud to the dashboard.

Accomplishments that we're proud of:

Deploying the dashboard to a cloud and being able to store data in a cloud.

What we learned:

We learned a lot.

What's next for HeartGrid:

Integrate machine learning where our service will be able to make sense of the data and find the person’s average physiological heart rate. This could eventually help in generating custom mental health care plans. In addition, expanding the service to help athletes perform better and understand their physiological capacities. Finally, we are hoping that there would be a Microsoft Band HeartGrid which helps detects Heart Rate Variability (HRV). HRV can help in detecting precisely many heart conditions and diseases.

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