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Body Temperature Authentication for Secure Smartwatch to Smart Device Communication
Enamamu T, Clarke NL, Haskell-Dowland PS (Dowland PS), Li F
Proceedings of the IEEE International Conference on Computing, Networking and Informatics (ICCNI 2017) , ISBN: 978-1-5090-4643-0, pp1-7, 2017
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The advancement of smart devices has led to a steep rise in wearable devices of which smart watches are increasingly gaining popularity in the wearable technology market. Most smart watches have evolved from their first generation to their present generation with increased functionality and capacity. This has led to smart watches gaining popularity and acceptability within the mainstream digital device usage. The first generation of smart watches were fitted with fewer sensors compared to the present day smart watches. The present day smart watch can be used for various activities much more than its tradition usage for health and fitness. These activities includes accepting and declining calls, reading Short Message Service (SMS), listening to music, navigation etc. while smart watches are still advancing technologically, some can function independently while most can be synchronized with smart phones through Bluetooth or Near-Field Communication (NFC). This brings about their easy communication with smart phones. To access the smart watch applications and information, it will be ideal to authenticate the user. Therefore this paper proposed a novel body temperature authentication system, BT-Authen, to authenticate the user by using the body temperature information extracted via a smart watch for continuous and non-intrusive user authentication. The authentication credentials are compared on the smartphone it is paired with before access is granted. To actualise this, the galvanic skins response (GSR) and skin temperature information are extracted for user authentication. The dataset for the evaluation of the body temperature signals are extracted from 30 subjects over three days. Six features are extracted from each of the two body temperature signals. The classification achieved an EER of 3.4 % using a Neural Network Feedforward (NN-FF) classifier. The performance increased to EER of 0.54% after applying a best performance scoring algorithm.

Enamamu T, Clarke NL, Haskell-Dowland PS (Dowland PS), Li F