Extraction of a Pulse Wave Using a Piezoelectric Element Toward Energy Harvesting

Pulse waves are expected to be used as a power source for wearable devices. In this study, we examine whether a pulse wave can actually be extracted from a human body using commercially available piezoelectric elements. By improving the contact condition between the skin and the piezoelectric element, we confirmed that pulse waves could be extracted

Introduction of a Mutual Feature between Electrodes into Support Vector Machine Based Person Verification Using Evoked Electroencephalogram by Ultrasound

In user management, to realize continuous user authentication, we study the use of an electroencephalogram (EEG) evoked by ultrasound as biometrics. In previous studies, using a spectrum and four nonlinear quantities in EEG as individual features and a support vector machine (SVM) as a verification method achieved an equal error rate (EER) of 0 %. However, it required a large number of SVM models, wherein considerable amount of computation regarding learning was consumed. In this study, we introduce a mutual feature between electrodes and confirm its effectiveness in achieving EER = 0 % with a smaller number of SVM models

Introduction of Fractal Dimension Feature and Reduction of Calculation Amount in Person Authentication Using Evoked EEG by Ultrasound

The aim of this study is to authenticate individuals using an electroencephalogram (EEG) evoked by a stimulus. EEGs are highly confidential and enable continuous authentication during the use of or access to the given information or service. However, perceivable stimulation distracts the users from the activity they are carrying out while using the service. Therefore, ultrasound stimuli were chosen for EEG evocation. In our previous study, an Equal Error Rate (EER) of 0 % was achieved; however, there were some features which had not been evaluated. In this paper, we introduce a new type of feature, namely fractal dimension, as a nonlinear feature, and evaluate its verification performance on its own and in combination with other conventional features. As a result, an EER of 0 % was achieved when using five features and 14 electrodes, which accounted for 70 support vector machine (SVM) models. However, the construction of the 70 SVM models required extensive calculations. Thus, we reduced the number of SVM models to 24 while maintaining an EER = 0 %

Person Authentication Using Brain Waves Evoked by Individual-related and Imperceptible Visual Stimuli

Person authentication using biometric information has become increasingly popular owing to the fact that continuous authentication is required in most user management systems. This study introduces an individual-related stimulus, instead of common stimuli, to improve the verification performance based on biometric authentication using brain waves evoked by imperceptible visual stimulation. Imperceptible visual stimulation is considered over visual stimulation to overcome obstacles that a user may face when using a system. Compared with previous studies that used circular figures as common stimuli, herein, we ensured a higher evoked response by using individual face image stimulation. Imperceptible stimuli were confirmed by changing the image intensity and presenting a high-speed stimulation. Individual imperceptible face image stimulation confirmed that the following event-related potential (ERP) components: N 170, N 250, and N 400 were obtained. Furthermore, by using various time zones, including the ERP components as features, we verified the performance of eight subjects and achieved an equal error rate (EER) of 6.2 %

Preleukemia: hematological disorders prior to onset of leukemia

Published data on Japanese leukemia patients with a preleukemic hematological disorder were assessed. The reexamined cases were from the &#34;Japona Centra Revuo Medicina&#34; reported during the period from 1952 to 1971. Among preleukemic hematological disorders, hypoplastic anemia was the most frequently reported (41 of 62 cases). These &#34;hypoplastic preleukemia&#34; patients were rather elderly and terminated mostly in atypical myelocytic leukemia. The chief hematological feature of the hypoplastic preleukemia cases was the coexistence of a relative erythroid hyperplasia and a slight increase of myeloblasts in the bone marrow that was unusual in hypoplastic anemia. The presence of pancytopenia and hypocellular marrow with a relative erythroid hyperplasia combined with a slight increase of myeloblasts probably indicates hypoplastic preleukemia that terminates later in acute leukemia.</p

Unconscious Biometrics for Continuous User Verification

In user management system, continuous or successive (ondemand) authentication is required to prevent identity theft. In particular, biometrics of which data are unconsciously presented to authentication systems is necessary. In this paper, brain waves and intra-palm propagation signals are introduced as biometrics and their verification performances using actually measured data are presented

Correlation Analysis of Features for Fusing in User Verification Using EEG Evoked by Ultrasound

In user verification using electroencephalograms (EEGs) evoked by ultrasound, an error rate of 0% was achieved. However, to achieve this, the classifiers for the number of features multiplied by the number of electrodes must be learned. Therefore, reducing the number of classifiers is crucial and must be achieved. This study confirmed that the random selection of features and electrodes facilitates further reduction in the number of classifiers. Random selection is equivalent to evenly selecting electrodes for each feature and electrode position. Consequently, the effectiveness of even selection was statistically confirmed. Furthermore, even selection resulted in the fusion of uncorrelated features. Thus, four statistical values of an EEG were introduced, and the effectiveness of fusing uncorrelated (independent) features was confirmed

Introduction of New Features in Writer Verification Based on Finger-writing of a Simple Symbol

In this study, a method of verifying an individual from their style of drawing simple symbols that everyone is familiar with and never forgets was studied. Individuals were asked to draw symbols using their fingertips on a digital device screen. Various features, such as the finger pressure, the touch area, and the touch direction, which were directly detected by a tablet device, were measured. In addition, the finger volume, force, and amount of work that were derived from the directly detected features were calculated. Subsequently, the verification performance of these features was evaluated

A Study on Evaluation of Healing Level Using Brainwave Stimulated by Tourist Spot Image

Healing is an emotion that even the person himself is hard to notice. In this paper, healing levels of tourist spots are evaluated using brainwaves stimulated by tourist spot images. Healing level is estimated by a ratio of the sum of spectral elements in α waveband to that in β waveband. From an experiment using eight subjects, it is found that tourist spots in white- and gray-colored images are evaluated as being healed since the visual perception of human beings is greatly influenced by color

Wavelet Transform and Machine Learning-Based Biometric Authentication Using EEG Evoked by Invisible Visual Stimuli

In this study, we propose the authentication of individuals using electroencephalograms (EEGs) evoked by the application of invisible visual stimuli. In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks