Situation for elderly home care difficult Demographic Change will worsen the situation Leads to a immense number of elderly people that will be cared for at home

What it does

Provide an all in one solution to make caring for your loved ones at home easier

How we built it

Figma for development of app prototype statista for market sizing kinematic model detection with open-cv encoding of the kinematic model into a feature vector training a random forest classifier with our own dataset that we created during the makeathon deploying application on streamlit

Challenges we ran into

Very tight timeline Collection of proprietary data for posture classification Coming up with own challenge from the plethora of interesting topics

Accomplishments that we're proud of

Curating own data set & optimized pre-processing for ML pipeline Creating a ML pipeline from Slim pitch deck and personalized storyline

What we learned

Working and functioning as team - with a lot of fun Diving into a topic all of us have a personal connection to Fast prototyping fusing hardware, software, and analytics

What's next for

Finalize prototype and launch app beta Seek co-innovation partner Design hardware and select manufacturer

Built With

  • kinematic-model
  • kinematic-model->feature-vectors
  • open-cv
  • python
  • random-forest-classifier
  • sklearn
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