Few-Shot User-Adaptable Radar-Based Breath Signal Sensing.

Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.

RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation.

This paper presents and implements a novel remote attestation method to ensure the integrity of a device applicable to decentralized infrastructures, such as those found in common edge computing scenarios. Edge computing can be considered as a framework where multiple unsupervised devices communicate with each other with lack of hierarchy, requesting and offering services without a central server to orchestrate them. Because of these characteristics, there are many security threats, and detecting attacks is essential. Many remote attestation systems have been developed to alleviate this problem, but none of them can satisfy the requirements of edge computing: accepting dynamic enrollment and removal of devices to the system, respecting the interrupted activity of devices, and last but not least, providing a decentralized architecture for not trusting in just one Verifier. This security flaw has a negative impact on the development and implementation of edge computing-based technologies because of the impossibility of secure implementation. In this work, we propose a remote attestation system that, through using a Trusted Platform Module (TPM), enables the dynamic enrollment and an efficient and decentralized attestation. We demonstrate and evaluate our work in two use cases, attaining acceptance of intermittent activity by IoT devices, deletion of the dependency of centralized verifiers, and the probation of continuous integrity between unknown devices just by one signature verification.

Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module.

The increasing integration of technology in our daily lives demands the development of more convenient human-computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.

Air-Writing Character Recognition with Ultrasonic Transceivers.

The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.