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Uses Random Forest Classifier and accelerometer data to predict user activity

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gabrielhgiraldo/Sensor_Data_Activity_Classifier

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Device Sensor User Activity Random Forest Classifier

Loading Data

Dataset taken from http://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition# Be aware that when unzipped, data is ~3.3gb! data not included in repository(just download it from link above)

Try your own data!

  • download the SensorUDP app from https://play.google.com/store/apps/details?id=com.ubccapstone.sensorUDP&hl=en_US
  • run The Live_Sensor_Streaming jupyter notebook
  • follow instructions and enjoy! (due to controlled conditions in the dataset, it only works if the back of the phone is facing downward, I try to do some coordinate system transformations using the rotation sensors, but haven't completely nailed it down)

Further Consideration

  • Get a more representative dataset
  • Different train test split (time split/leave one out?)
  • Include more activities
  • Include more sensors if accelerometer isn’t sufficient
  • Include more time and frequency features

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Uses Random Forest Classifier and accelerometer data to predict user activity

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