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Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor

With the great expansion of computer technology, Human-Computer Interaction (HCI) is required to be more effective. Hand gestures are more natural compared with actions associated with the traditional devices such as the keyboard, mouse, etc. This thesis proposes a wearable data gloved-based hand ge...

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Bibliographic Details
Main Author: Ong, Jing Hao
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2017
Subjects:
T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Online Access:http://eprints.usm.my/52929/1/Data-Glove-Based%20Hand%20Gesture%20Recognition%20System%20Using%20Flex%20Sensors%20And%20An%20Imu%20Sensor_Ong%20Jing%20Hao_E3_2017.pdf
http://eprints.usm.my/52929/
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spelling my.usm.eprints.52929 http://eprints.usm.my/52929/ Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor Ong, Jing Hao T Technology TK Electrical Engineering. Electronics. Nuclear Engineering With the great expansion of computer technology, Human-Computer Interaction (HCI) is required to be more effective. Hand gestures are more natural compared with actions associated with the traditional devices such as the keyboard, mouse, etc. This thesis proposes a wearable data gloved-based hand gesture recognition system that is able to recognize static hand and dynamic gestures to allow human interacts with the computer in more natural manner. This system recognizes the hand gestures based on the information captured by the flex sensors and an IMU sensor. The fingers’ bending angles are measured by using the flex sensors while the pitch and roll of the hand are detected by using the IMU sensors. The acquired data is then processed in Raspberry Pi board. The Complementary filter is used to fuse the data from the accelerometer data and gyroscope packed in IMU sensor to obtain an accurate measurement. This system is based on k-Nearest Neighbors (k-NN) classifier algorithm, Dynamic Time Warping (DTW) and Euclidean distance metric algorithms. The proposed system was tested in recognizing 38 static and 12 dynamic hand gestures. The meaning of the hand gesture is displayed on GUI created in Raspberry Pi by using Python’s Tkinter programming. An accuracy of 98.97 % is achieved by this system in recognizing 12 dynamic and 38 static hand gestures without the user’s noise. Universiti Sains Malaysia 2017-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/52929/1/Data-Glove-Based%20Hand%20Gesture%20Recognition%20System%20Using%20Flex%20Sensors%20And%20An%20Imu%20Sensor_Ong%20Jing%20Hao_E3_2017.pdf Ong, Jing Hao (2017) Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik & Elektronik. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
spellingShingle T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Ong, Jing Hao
Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
description With the great expansion of computer technology, Human-Computer Interaction (HCI) is required to be more effective. Hand gestures are more natural compared with actions associated with the traditional devices such as the keyboard, mouse, etc. This thesis proposes a wearable data gloved-based hand gesture recognition system that is able to recognize static hand and dynamic gestures to allow human interacts with the computer in more natural manner. This system recognizes the hand gestures based on the information captured by the flex sensors and an IMU sensor. The fingers’ bending angles are measured by using the flex sensors while the pitch and roll of the hand are detected by using the IMU sensors. The acquired data is then processed in Raspberry Pi board. The Complementary filter is used to fuse the data from the accelerometer data and gyroscope packed in IMU sensor to obtain an accurate measurement. This system is based on k-Nearest Neighbors (k-NN) classifier algorithm, Dynamic Time Warping (DTW) and Euclidean distance metric algorithms. The proposed system was tested in recognizing 38 static and 12 dynamic hand gestures. The meaning of the hand gesture is displayed on GUI created in Raspberry Pi by using Python’s Tkinter programming. An accuracy of 98.97 % is achieved by this system in recognizing 12 dynamic and 38 static hand gestures without the user’s noise.
format Monograph
author Ong, Jing Hao
author_facet Ong, Jing Hao
author_sort Ong, Jing Hao
title Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
title_short Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
title_full Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
title_fullStr Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
title_full_unstemmed Data-Glove-Based Hand Gesture Recognition System Using Flex Sensors And An Imu Sensor
title_sort data-glove-based hand gesture recognition system using flex sensors and an imu sensor
publisher Universiti Sains Malaysia
publishDate 2017
url http://eprints.usm.my/52929/1/Data-Glove-Based%20Hand%20Gesture%20Recognition%20System%20Using%20Flex%20Sensors%20And%20An%20Imu%20Sensor_Ong%20Jing%20Hao_E3_2017.pdf
http://eprints.usm.my/52929/
_version_ 1736834778225704960
score 13.211869

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