Human Activity Recognition

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  CV decision tree

Inspiration

Elderly people can fall down and there might be no help in remote access and/or no notification happening to send help.

What it does

This model can be a true super hero / life saver. It perfectly predicts whether a person is falling / walking using sensory data from a smartphone which could be used as a trigger to notify emergency services.

How I built it

Decision-tree based and cross-validated, feature-rich self-collected sensory data using Python for pre-processing and R for model training.

Challenges I ran into

• time inconsistent measurements of sensory data
• falling on the ground to collect data hurt sometimes

Accomplishments that I'm proud of

• identified most relevant features that distinguish between two classes
• showed that simple model can better predict and train much faster than a complex, deep learning model (LSTM)

What I learned

• coding
• feature extraction of time dimension

What's next for Human Activity Recognition

• collect more data to train more complex models
• incorporate more different activities
• integrate it to a server application that monitors live sensory input data and triggers a mechanism in case of dangerous situation

Built With

• android
• python
• r