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ESP (Example-based Sensor Predictions)

This project aims to help novices make sophisticated use of sensors in interactive projects through the application of machine learning.

Pre-requisites

At the moment, this project runs only on OS X. You'll need Xcode and git (to clone this repository and its submodules).

Installation

To install, first clone this repository, then run the setup script:

git clone https://github.com/damellis/sensors.git
cd sensors
./setup

This will clone the relevant git submodules and create some symbolic links.

Running

The main application is the SmartSensors Xcode project. To select an example to run, uncomment the corresponding line in user.h. Many of these examples expect an Arduino board to be connected to the computer and running an appropriate sketch. Example include:

  • user_audio_beat.h: recognizes periodic sounds (e.g. dialtones, bells ringing, whistling) using an FFT and support vector machines algorithm. Works with your computer's built-in microphone.

  • user_color_sensor.h: detects objects by color using a naive Bayes classifier. Works with either the Adafruit TCS34725 breakout (using the sketch in Arduino/ColorSensor) or the SparkFun ISL29125 breakout (using the sketch in Arduino/ColorSensor_SparkFun_ISL29125). See documentation for the sensors for hookup information.

  • user_accelerometer_calibration.h: recognizes gestures using a dynamic time warping algorith. Works with either an ADXL335 accelerometer (using the Arduino/ADXL335 sketch) or the built-in accelerometer on an Arduino 101 (using the Arduino/Arduino101_Accelerometer sketch).

  • user_accelerometer_poses.h: recognizes the orientations of an object using a naive Bayes classifier. Works with accelerometers as for the user_accelerometer_calibration.h example.

Dependencies

These should be automatically installed by the setup script:

License

TODO add license (BSD?)

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