Embedded hardware implementation for real time hand gesture recognition / Farah Farhana Mod Ma'asum

Mod Ma'asum, Farah Farhana (2017) Embedded hardware implementation for real time hand gesture recognition / Farah Farhana Mod Ma'asum. Masters thesis, Universiti Teknologi MARA (UiTM).


This research is focused in designing a low-cost embedded system which can produce robust marker-less tracking system. The application will benefit medical, disabled person and factories, since it can perform as a replacement of mouse cursor by just gesturing motion in the air. The performance of the hand gesture image recognition, segmentation technique and feature classification technique is established as part of the processes. There are four main phases were set up in achieving the research objectives. Initially, image of hand which are being captured are being segmented using Canny and Otsu threshold technique. Then, the hand image is extracted using convex hull and convexity technique while angle of fingertips is obtained from feature vector representation. Three actions are classified: MOVE, RIGHT CLICK and LEFT CLICK cursor. All these actions are then demonstrated with the Arduino board to verify that all techniques are authenticated based on the signal sent by hand gesture. An experiment is set up for 10 users for validation. Also, the users are trained to familiarize with the gesture system. The results revealed that the users are better trained in controlling their fingertips after five-minute of training in the second trial. The findings show that an increase in the LEFT CLICK action is achieved from 33.3% to 52.6%. The RIGHT CLICK is improved from 46.7% to 61% while 56.7% to 77.3 % for MOVE cursor. The results indicate that the system is capable to replace the multi-touch modalities. In addition, there are three different LED colors: RED, YELLOW and BLACK are embedded to the system to represent the gesture - MOVE, RIGHT CLICK and LEFT CLICK respectively, using serial communication. For that reason, low-cost embedded system for marker-less tracking system has been verified to obtain good gesture recognition.


Item Type: Thesis (Masters)
Email / ID Num.
Mod Ma'asum, Farah Farhana
Email / ID Num.
Thesis advisor
Sulaiman, Suhana
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunication
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Scanning systems
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Master of Science (Electronics Engineering)
Keywords: Embedded hardware; real time hand gesture recognition
Date: 2017
URI: https://ir.uitm.edu.my/id/eprint/21629
Edit Item
Edit Item


[thumbnail of 21629.pdf] Text

Download (174kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:
On Shelf

ID Number




Statistic details