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In this paper, an integrated biometric-based security framework is proposed for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that require low computational power and less resources (e.g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification method is utilized for accurate authentication based on the non-Gaussian statistics of ECG (Electro-Cardio-Gram) signals.
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