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The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. 

In this work, we propose a new approach to examine the joint effect of physical layer security (PhySec) and encryption. Our idea relies on the concept of rate-equivocation regions and can be used to study the tradeoff between encryption strength, allowed leakage, and transmission rate. By considering encryption, it is possible to achieve transmission rates beyond the secrecy capacity that is achievable by conventional physical layer security. Toward our goal, we exploit the fact that cryptography undermines the ability of the eavesdropper to access the plaintext. We then relax the design of physical layer security schemes without compromising the security of the system. To validate our new approach, we consider a multi-node Gaussian wiretap channel consisting of a legitimate transmitter, a legitimate receiver, an eavesdropper and multiple trusted relays assisting transmission from the transmitter to the receiver. Under this wireless network, we illustrate that encryption awareness not only complements traditional PhySec methods but also achieves superior secrecy performance. An encryption-aware secrecy capacity was also obtained from the rate-equivocation regions under different channel state information conditions.