Assimilation of Gesture using 9 Axis Acclerometer Sensor

International Journal of Electrical and Electronics Engineering
© 2018 by SSRG - IJEEE Journal
Volume 5 Issue 2
Year of Publication : 2018
Authors : Ramya Ravibarathi, Tamilarasu Viswanathan
: 10.14445/23488379/IJEEE-V5I2P101
pdf
Citation:
MLA Style:

Ramya Ravibarathi, Tamilarasu Viswanathan, "Assimilation of Gesture using 9 Axis Acclerometer Sensor" SSRG International Journal of Electrical and Electronics Engineering 5.2 (2018): 1-4.

APA Style:

Ramya Ravibarathi, Tamilarasu Viswanathan,(2018). Assimilation of Gesture using 9 Axis Acclerometer Sensor. SSRG International Journal of Electrical and Electronics Engineering 5(2), 1-4.

Abstract:

Motion based interaction which could be a natural way of human machine interaction, encompasses a wide extend of applications in a computing environment. The accelerometer sensor is utilized for information procurement. The motions utilized are W, and 8. The speeding up flag of these signals are collected from 15 individuals counting both male and female by keeping the gadget at diverse positions with diverse pace. The motion acknowledgment primarily comprises of two stages: preparing organize and testing arrange. The preparing arrange is performed offline and it comprises of collection of increasing speed signals from the accelerometer sensor and the highlight extraction of the increasing speed signals. The highlights extricated from the signals are mean and variance. The testing arrange is done online. All the two signals are prepared employing a single arrange. The calculation utilized to recognize the motions is Extraordinary Learning Machines (ELM) which could be a sort of neural organize. Eclipse shows the simulated result and arduino gives the real time results.

References:

[1]. Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms, Paweł Pławiak, Tomasz So´snicki, Michał Nied´zwiecki, Zbisław Tabor and Krzysztof Rzecki, 2015 IEEE. 
[2]. Smart Wearable Hand Device for Sign Language Interpretation System with Sensors Fusion ,B. G. Lee, Member, IEEE, and S. M. Lee, 2017 IEEE. 
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Key Words:

 Eclipse, Arduino Due , Machine Learning, sigmoid function, activation function, output weights, filtering, feed forward neural network.