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
How to Cite?
Ramya Ravibarathi, Tamilarasu Viswanathan, "Assimilation of Gesture using 9 Axis Acclerometer Sensor," SSRG International Journal of Electrical and Electronics Engineering, vol. 5, no. 2, pp. 1-4, 2018. Crossref, https://doi.org/10.14445/23488379/IJEEE-V5I2P101
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.
Keywords:
Eclipse, Arduino Due , Machine Learning, sigmoid function, activation function, output weights, filtering, feed forward neural network.
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.
[3]. Smart device based gesture control for industrial application, 2017,IEEE.
[4]. Development of Hand Gesture Recognition Sensor Based on Accelerometer and Gyroscope for Controlling Arm of Underwater Remotely Operated Robot Ronny Mardiyanto, Mochamad Fajar Rinaldi Utomo, Djoko Purwanto, Heri Suryoatmojo Department of Electrical Engineering Institute Teknologi Sepuluh Nopember Surabaya, Indonesia,2017 IEEE.
[5]. A Hand Gesture Recognition Model Based on Semi-supervised Learning ,Meiping Tao, Li Ma Xi’an University of Posts and Telecommunications Xi’an, China, 2015 IEEE