Activity Prediction
Human Activity Prediction

This project is devoted to the problem of classification of time-series recorded by smartphone sensors (e.g. human activity prediction using the accelerometer data).

The Android application is available for download from here.

There are two parties: a server and a client, which communicate via the asynchronous TCP connection.

The client collects data using the smartphone sensors and sends it to the server for:

  • time-series classification,
  • training a classifier.

The back end (the server) receives the data and performs classification remotely thereby allowing for using complex classification algorithms.

The server works as follows. It saves the received time-series, performs computations, and responds to the client with a predicted class label, if necessary.

All the data received by the server is logged on the server in directories specified by clients (the 'account' field in the client app):


The server was implemented in Python, the client in Java.

The source code is open and is available from the project repository: https://github.com/karasikov/TimeSeriesClassification.