The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Physical computing has spun a true global revolution in the way that the digital world now interfaces with the physical one. From Twitter-controlled Christmas trees to bicycle jackets with turn signal lights, the do-it-yourself movement is driving endless innovations and stimulating an age of creative engineering. The authors present a novel development platform that extends the principles of physical computing to the physiological domain, changing the way in which projects and applications involving physiological data can be made.

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system’s effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.

The electrocardiogram (ECG) is a non‐invasive and widely used technique for cardiac electrophysiological assessment. Although the ECG has traditionally only been used for functional diagnostic and evaluation, several advances in electrophysiological sensing have made available robust signal acquisition devices, particularly suited for ambulatory conditions, widening its range of applications. In particular, recent work has shown the potential of the ECG as a biometric trait, both for human identification and authentication. This study sets the ground for an ECG‐based real‐time biometric system. The authors describe an experimental setup and the evaluation of new fiducial and non‐fiducial approaches, including data acquisition, signal processing, feature extraction and analysis and classification methodologies, showing the applicability of the ECG as a real‐time biometric. Performance evaluation was done in clinical‐grade ECG recording from 51 healthy control individuals (of a publicly available benchmark dataset) as well as on data collected from 26 healthy volunteers performing computer activities without any posture or motion limitations, thus simulating a regular computer usage scenario.

Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.