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Over the last few decades, Smartphone technology has seen significantimprovements. Enhancements specific to built-in Inertial Measurement Units(IMUs) and other dedicated sensors of the smartphones(which are often availableas default) such as- Accelerometer, Gyroscope, Magnetometer, Fingerprintreader, Proximity and Ambient light sensors have made devices smarter and theinteraction seamless. Gesture recognition using these smart phones have beenexperimented with many techniques. In this solution, a Recurrent Neural Network(RNN) approach, LSTM (Long-Short Term Memory Cells) has been used to classifyten different gestures based on data from Accelerometer and Gyroscope.Selection of sensor data (Accelerometer and Gyroscope) was based on the onesthat provided maximum information regarding the movement and orientation of thephone. Various models were experimented in this project, the results of whichare presented in the later sections. Furthermore, the properties andcharacteristics of the collected data were studied and a set of improvementshave been suggested in the future work section.

Gesture Recognition based on Long-Short Term Memory Cells using Smartphone IMUs