Designing a Sensor-Based Wearable Computing System for Custom Hand Gesture Recognition Using Machine Learning

Ayoub, Hadeel. 2022. Designing a Sensor-Based Wearable Computing System for Custom Hand Gesture Recognition Using Machine Learning. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

This thesis investigates how assistive technology can be made to facilitate communication for people that are unable to or have difficulty communicating via vocal speech, and how this technology can be made more universal and compatible with the many different types of sign language that they use. Through this research, a fully customisable and stand-alone wearable device was developed, that employs machine learning techniques to recognise individual hand gestures and translate them into text, images and speech. The device can recognise and translate custom hand gestures by training a personal classifier for each user, relying on a small training sample size, that works online on an embedded system or mobile device, with a classification accuracy rate of up to 99%. This was achieved through a series of iterative case studies, with user testing carried out by real users in their every day environments and in public spaces.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):

https://doi.org/10.25602/GOLD.00031706

Keywords:

Gesture Recognition, Machine Learning, Wearable Technology, Sensors

Departments, Centres and Research Units:

Computing

Date:

31 March 2022

Item ID:

31706

Date Deposited:

11 Apr 2022 08:47

Last Modified:

11 Apr 2022 08:48

URI:

https://research.gold.ac.uk/id/eprint/31706

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