On the Impact of the Data Acquisition Protocol on ECG Biometric Identification.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)-this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously-one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system's performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that "off-the-person" data acquisition led to a better performance over time, when compared to "on-the-person" places.

Nanofiber-Reinforced Silver Nanowires Network as a Robust, Ultrathin, and Conformable Epidermal Electrode for Ambulatory Monitoring of Physiological Signals.

Extremely soft and thin electrodes with high skin conformability have potential applications in wearable devices for personal healthcare. Here, a submicrometer thick, highly robust, and conformable nanonetwork epidermal electrode (NEE) is reported. Electrospinning of polyamide nanofibers and electrospraying of silver nanowires are simultaneously performed to form a homogeneously convoluted network in a nonwoven way. For a 125 nm thick NEE, a low sheet resistance of ≈4 Ω sq-1 with an optical transmittance of ≈82% is achieved. Due to the nanofiber-based scaffold that undertakes most of the stress during deformation, the electric resistance of the NEE shows very little variation; less than 1.2% after 50 000 bending cycles. The NEE can form a fully conformal contact to human skin without additional adhesives, and the NEE shows a contact impedance that is over 50% lower than what is found in commercial gel electrodes. Due to conformal contact even under deformation, the NEE proves to be a stable, robust, and comfortable approach for measuring electrocardiogram signals, especially when a subject is in motion. These features make the NEE promising for use in the ambulatory measurement of physiological signals for healthcare applications.

Carbon Nanotube Embedded Nanostructure for Biometrics.

Low electric energy loss is a very important problem to minimize the decay of transferred energy intensity due to impedance mismatch. This issue has been dealt with by adding an impedance matching layer at the interface between two media. A strategy was proposed to improve the charge transfer from the human body to a biometric device by using an impedance matching nanostructure. Nanocomposite pattern arrays were fabricated with shape memory polymer and carbon nanotubes. The shape recovery ability of the nanopatterns enhanced durability and sustainability of the structure. It was found that the composite nanopatterns improved the current transfer by two times compared with the nonpatterned composite sample. The underlying mechanism of the enhanced charge transport was understood by carrying out a numerical simulation. We anticipate that this study can provide a new pathway for developing advanced biometric devices with high sensitivity to biological information.

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links.

This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method.