Over the last few decades, Smartphone technology has seen significant improvements. Enhancements specific to built-in Inertial Measurement Units (IMUs) and other dedicated sensors of the smartphones(which are often available as default) such as- Accelerometer, Gyroscope, Magnetometer, Fingerprint reader, Proximity and Ambient light sensors have made devices smarter and the interaction seamless. Gesture recognition using these smart phones have been experimented with many techniques. In this solution, a Recurrent Neural Network (RNN) approach, LSTM (Long-Short Term Memory Cells) has been used to classify ten different gestures based on data from Accelerometer and Gyroscope. Selection of sensor data (Accelerometer and Gyroscope) was based on the ones that provided maximum information regarding the movement and orientation of the phone. Various models were experimented in this project, the results of which are presented in the later sections. Furthermore, the properties and characteristics of the collected data were studied and a set of improvements have been suggested in the future work section.
@article{arxiv.2308.11642,
title = {Gesture Recognition based on Long-Short Term Memory Cells using Smartphone IMUs},
author = {Yuvaraj Govindarajulu and Raja Rajeshwari Raj Kumar},
journal= {arXiv preprint arXiv:2308.11642},
year = {2023}
}
Comments
Proceedings of Fachpraktikum Interaktive Systeme (FIS 18), FIS 18, Summer 2018, University of Stuttgart, Germany