Activity Analysis Based on Mobile Sensors<br/>

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Activity Analysis Based on Mobile Sensors

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 51617 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Gholampour, Iman
Abstract:
Smartphone sensors like accelerometer, gyroscope and magnetometer are very common nowadays. This gives us the opportunity for sensor-based activity recognition. This thesis's goal is to collect data from different smartphone sensors and then extract hand-crafted features and classify them using machine learning algorithms. Metro, bus, taxi, bicycle, running, upstairs, walking and standing are studied activities in this thesis. All above steps are covered in this research, later we want to present an activity recognition model and then test it through a web server, after that, we modify the model by proposing to change learning coefficient to gain better accuracy. Finally, an Android app was developed to train a model for recognition that has 88 percent test accuracy. In the last section, we explain some application of activity recognition and how to use them
Keywords:
Smart Phones Sensors ; Feature Extraction ; Activity Recognition ; Metro Activity ; Accelerometer

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