We are being increasingly surrounded by electronic devices that need reliable authentication before they can be accessed. While typed passwords, face and fingerprint-based authentication are popular today, we explore the possibility of using additional bio-origin signals, especially electrocardiogram (ECG) signals that can be collected without an active engagement and attention of the human user. Our proposed approach, abbreviated as FERST, uses a two-stage deep learning framework for signal processing and classification. The acquired and pre-equalized ECG signal is transferred from a wearable device through arm-wrist-palm galvanic coupled channel. The first part of the reception framework consists of a denoising autoencoder (DAE) that filters this noisy ECG signal. Then, the second stage includes a convolutional neural network (CNN) to classify and authenticate the denoised ECG signal as present/absent in a prior database used for training. The overall design goal of our approach is to achieve acceptable authentication performance with simple hardware, which further decreases network complexity and enables miniaturization of the end-to-end system. Results reveal that the FERST provides (i) 82% improvement in inference time relative to state-of-the-art adaptive filtration methods, (ii) outperforms the state-of-the-art with 99.7% classification accuracy on a standardized ECG library dataset and (iii) a relatively high classification accuracy of 99.2% for different arm-wrist-palm channel dimensions.

This paper proposes MAGIC: magnetic resonant (MR) coupling for intra-body communication between implants and wearables. MAGIC includes not only the hardware-software design of the coupled coils and methods of manipulating the magnetic field for relaying information, but also the ability to raise immediate emergency-related alerts with guaranteed delivery time. MR coupling makes the design of the transmission link robust to channel-related parameters, as the magnetic permeability of skin and muscle is close to that of air. Thus, changes in tissue moisture content and thickness does not impact the design, which is a persistent problem in other approaches for implant communications like RF, ultrasound and galvanic coupling (GC). The paper makes three main contributions: It develops the theory leading to the design of the information relaying coils in MAGIC. It proposes a systems-level design of a communication link that extends up to 50cm with a low expected BER of 10-4. Finally, the paper includes an experimental setup demonstrating how MAGIC operates in air and muscle tissue, as well as a comparison with alternative implant communication technologies, such as classical radio frequency and GC. Results reveal that MAGIC offers instantaneous alerts with up to 5 times lower power consumption compared to other forms of communication.

Increasing threats of malicious eavesdropping raise concerns in confidential data reporting by body-worn sensors. We propose a secure, body-guided transmission channel through the use of galvanic coupling (GC). This method involves injecting weak electrical current into the body, which propagates primarily through the skin. The proposed approach makes the transmission of biometric data impervious to sniffing attacks, enabling the body to serve as a waveguide. This paper makes the following contributions: (i) An analytical channel model using a tissue equivalent circuit of the human arm-wrist-palm GC-propagation path is formulated and empirically verified. (ii) A simulation study is conducted for a comparative analysis of various modulation schemes, leveraging the validated GC-channel behavior. (iii) A GC-transceiver with optimized communication parameters (modulation, frequency, power) is designed and implemented using a dielectrically equivalent tissue phantom, and (iv) through experimental trials, resilience to over-the-air susceptibility (i.e., likelihood of adversarial eavesdropping) of the GC-signal and similar body communication techniques are demonstrated. Performance results of the GC-transceiver prototype yield a bit error rate of 10-6with a transmit power of -2dBm, in addition to over 7x reduction of signal radiation outside the body compared to capacitive coupling.

In a world dominated by the wearable IoT devices, malicious security threats have become a common concern in system design. Guided by this notion, we propose a secure transmission system through body-guided channels using Galvanic Coupling (GC). The GC-method injects weak electrical current into human tissue, primarily propagating through the skin. The proposed design provides impermeability to malicious attacks, (e.g. side-channel sniffing) when sending biometric data, as the body behaves as a natural waveguide. The following contributions are: 1) the analytical formulation and empirical verification of a 3D tissue equivalent circuit model for GC-signal propagation of the human arm-wrist-palm channel; 2) the simulation study of numerous modulation schemes, drawn from the validated results of the GC-channel model; 3) the design and implementation of a transceiver prototype using optimal communication parameters (modulation, frequency, power) for transmission on a dielectrically equivalent tissue phantom; and 4) through the experimental trials, we demonstrate the eavesdropping susceptibility of the GCsignals, and similar body communication techniques, over-the-air and while in direct contact with the medium. The performance results of the GC-transceiver prototype yield a bit error rate of 10-6with a transmit power of -2 dBm, in addition to over 7x reduction of signal radiation outside the body compared to capacitive coupling.

Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.