Biometric recognition through gait analysis.

The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users' privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people's gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.

A PNU-Based Methodology to Improve the Reliability of Biometric Systems.

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation. Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus. Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated. Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay. Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19.

The novel coronavirus disease 2019 (COVID-19) global pandemic has shifted how many patients receive outpatient care. Telehealth and remote monitoring have become more prevalent, and measurements taken in a patient's home using biometric monitoring technologies (BioMeTs) offer convenient opportunities to collect vital sign data. Healthcare providers may lack prior experience using BioMeTs in remote patient care, and, therefore, may be unfamiliar with the many versions of BioMeTs, novel data collection protocols, and context of the values collected. To make informed patient care decisions based on the biometric data collected remotely, it is important to understand the engineering solutions embedded in the products, data collection protocols, form factors (physical size and shape), data quality considerations, and availability of validation information. This article provides an overview of BioMeTs available for collecting vital signs (temperature, heart rate, blood pressure, oxygen saturation, and respiratory rate) and discusses the strengths and limitations of continuous monitoring. We provide considerations for remote data collection and sources of validation information to guide BioMeT use in the era of COVID-19 and beyond.