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Nowadays, technology of data transmission is growing fast and has been well applied to different fields depending on practical demands. In which, internet-of-things (IoT)-related applications are the most popular mechanism with respective application scenarios. This study integrates ECG-based biometric authentication to create a sophisticated application for extreme demand of access security to the IOT system. This mechanism not only increases the users' convenience, but greatly protects privacy of the users.

An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each person's cardiac signal possesses unique characteristics. Thus, researchers have attempted to use ECG signals for personal identification. However, most studies verify results using ECG signals taken from databases which are obtained from subjects under the condition of rest. Therefore, the extraction and analysis of a subject's ECG typically occurs in the resting state. This study presents experiments that involve recording ECG information after the heart rate of the subjects was increased through exercise. This study adopts the root mean square value, nonlinear Lyapunov exponent, and correlation dimension to analyse ECG data, and uses a support vector machine (SVM) to classify and identify the best combination and the most appropriate kernel function of a SVM. Results show that the successful recognition rate exceeds 80% when using the nonlinear SVM with a polynomial kernel function. This study confirms the existence of unique ECG features in each person. Even in the condition of exercise, chaotic theory can be used to extract specific biological characteristics, confirming the feasibility of using ECG signals for biometric verification.

Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control. Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc. Still, there are novel identification technologies based on the individual's biometric features under development [1-4].

ECG signal vary from person to person, making it difficult to be imitated and duplicated. Biometric identification based on ECG is therefore a useful application based on this feature. Synchronization of chaotic systems provides a rich mechanism which is noise-like and virtually impossible to guess or predict. This study intends to combine our previously proposed information encryption/decryption system with chaotic synchronization circuits to create private key masking. To implement the proposed secure communication system, a pair of Lorenz-based synchronized circuits is developed by using operational amplifiers, resistors, capacitors and multipliers. The verification presented involves numerical simulation and hardware implementation to demonstrate feasibility of the proposed method. High quality randomness in ECG signals results in a widely expanded key space, making it an ideal key generator for personalized data encryption. The experiments demonstrate the use of this approach in encrypting texts and images via secure communications.

In the measurement of physiological signals, the electro-cardiogram (ECG) is essential to most medical diagnoses related to the heart behavior, which is closely associated with human's health. In this study, we have developed a health care platform (HCP) which is able to solve the baseline drift problem, detect the heart-rate status, and record the ECG signals. The HCP provides a convenient handheld device to collect ECG signals in moving state from only two conductive electrodes. The device transmits the data to computers through wireless transmission and shows real-time user's status including spectrum analysis and Fourier transform. In this paper, the HRV analysis is divided into two parts, one is time-domain analysis in HCP and frequency-domain analysis in PC. The experimental results analyze the life-behavior patterns of tested subjects that may affect HRV parameters (HF, LF and HF/LF) so that one may achieve the task of disease prevention and health management.