Elderly individuals are increasingly living independently- that means that they are at greater risk of suffering from accidents (such as falls) in an environment where help may not be readily accessible. Akshaya- My grandparents recently came to live with us for a few months from India. Since both my parents work and I am at classes, there usually is no one at home in the event of an emergency. Since my grandparents are not used to the systems here, it would also be difficult for them to find/get help if they need it. This is a cause of worry, and CareFall has been a great opportunity to use what I have learned in school for a practical application that improves the lives of those I love- and has positive implications in helping the greater community.

What it does

App uses sensors on FitBit wearable device to alert designated caregivers that an elderly patient has suffered from a fall. Offers real-time vital statistic (such as heart rate) updates for caregivers to monitor before they even reach the patient- thus allowing for faster and more effective assessment of the situation and appropriate first response.

How we built it

We integrated FitBit device sensor data with a companion app and full stack web service.

Challenges we ran into

Integrating the flow of information from a physical FitBit device to a companion app and then to the web interface was difficult.

Accomplishments that we're proud of

We are realy proud to have a working project that helps us improve the lives of elderly individuals in our community.

What we learned

Working as a team, medical applications for computer science. How to detect falls using information from accelerometer and gyrometer.

What's next for CareFall

The best part of MedHacks is that it challenges you to dream of a better world for fellow humans. We hope to offer more vitals information such as body temperature, geolocation, consciousness) to the web application. While our initial project was heavily focused on addressing a need of elderly people safety, the applications for our device extent beyond this specific demographic. For example, we hope to build upon what we leared about identifying falls to being able to identify crash situations with the FitBit. This would allow us to send real-time patient updates to a hospital about the conditions of drivers/passengers in a car crash. Since this is a very high-risk situation, any time wasted directly decreases the chances of a good recovery. Remote monitoring of patient vital information as the ambulance is on the way allows for effective action once they reach the site.

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