FitPlus

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  Collecting userinfo for machine learning predictions

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  Activity page on the given user info and the back end machine learning model based on upon demo data

Inspiration

We were inspired from the current decline in physical and mental health due to lack of understanding about the benefits people can attain from walking and other simple physical activities.

What it does

It collects data from their devices and then calculates their calories burned and then motivates them to do better by providing features such as calories burnt, footsteps

How we built it

We built it using Streamlit for UI, Matlab mobile for data collection, Python and Matlab for data manipulation and machine learning predictions, and Google Cloud for globally storing and deploying the project.

Challenges we ran into

There is not one challenge we can talk about. We ran into calibration of the sensors, understanding the data provided from the sensors, and the amount of outliers within the data

Accomplishments that we're proud of

We are proud of deploying this project globally on Google Cloud. We are also proud of collaborating and reaching the end of the hackathon with sheer determination and team work.

What we learned

We learned and utilized multiple libraries and modules that we would generally not use in our projects. We also learned to manage time and collaborate despite having differences in opinions

What's next for FitPlus

The next for FitPlus is adding more features and using better machine learning models. We also want to expand this across our domain and make it a popular app amongst people who lack motivation to put in the work.

Built With

• google-cloud
• google-compute-engine
• google-storage
• machine-learning
• matlab
• numpy
• pandas
• python
• scikit-learn
• streamlit