Accelerometer and GPS sensor combination based system for human activity recognition

Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. These devices open wide range of opportunities of using their potential in different branches like healthcare, financing and so on. Current paper introduces an approach which allows recognizing activity, performed by human, using smartphone acceleration and positioning sensors. We introduce an approach that retrieves signal data and stores it SQLite portable mobile database. It uses asynchronous model of signal retrieving and storing procedures. After the signals were collected we applied noise reduction, time and frequency domain feature extraction processes for stored information and acquired high-dimensional feature patterns. These patterns were later transferred on remote server instead of raw signals. The classification stage was based on “learning with teacher” method. Incoming signal sequences were collected from sensors of mobile device and were analyzed using support vector machines (SVM) learning method.