Biometric System based on RFID and Heart Sound Verification
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Abstract:
Heart sound is one of the most important physiological signals of human body. A new access control system using dual- authentication technology is developed based on heart sound biometric and Radio Frequency Identification (RFID). ID information of the user is acquired by RFID as the user enters the range of the access control system. Then the electronic stethoscope collects the heart sound of the user. Mel Frequency Cepstrum Coefficient (MFCC) feature is extracted to compute the model parameter in Gaussian Mixture Models (GMM). The decision of verification is made to judge whether an input heart sound signal belongs to a stated identity. The system has high security since it realizes dual identification verification of one person.Keywords:
Mel-frequency cepstrum
Heart sounds
Identification
Stethoscope
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This paper is proposed the hardware structure and signal processing method of the RF-Tag based on heart sound to develop the mobile biomedical information device for the u-healthcare system. The RF-Tag in this study is consisted of a skin temperature sensor, a dynamic microphone for heart sound detection, Bluetooth communication to transmute healthcare data, and pulse period detection algorithm with adaptive gain controller. We experimented to evaluate performance of the RF-Tag in noisy environments. In addition, the RF-Tag has shown the good performance in the results of experiment. If the proposed methods in this study apply to design the u-healthcare device, we will be able to get the exact health data on real time in mobile environments.
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This paper presents a new system for water flow detection on real life recordings and its application to medical context. The recognition system is based on an original feature for sound event detection in real life. This feature, called "spectral cover" shows an interesting behaviour to recognize water flow in a noisy environment. The system is only based on thresholds. It is simple, robust, and can be used on every corpus without training. An experiment is realized with more than 7 hours of videos recorded by a wearable device. Our system obtains good results for the water flow event recognition (F-measure of 66%). A comparison with classical approaches using MFCC or low levels descriptors with GMM classifiers is done to attest the good performance of our system. Adding the spectral cover to low levels descriptors also improve their performance and confirms that this feature is relevant.
Feature (linguistics)
Mel-frequency cepstrum
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The door physical security system with voice recognition pattern is intended to amplify the security system and make it easier for users to access the house entrance. The experiment was carried out on a proportional small-scale prototype consisting of input media in the form of a reed switch sensor and a microphone on the user's smartphone, an actuator in the type of a solenoid door lock controlled by a relay, and the output media as a display on the Android application. By using a sensor reed switch to read the condition of a closed or open door, a servo motor to move the automatic entry and a solenoid door lock to perform automatic door locking. We can match the voice of the user entered with voice data that has been recorded on the embedded system. The application of the Mel Frequency Cepstrum Coefficients method in determining the extraction of sound features and Dynamic Time Warping to determine the sound match score. If the sound matching process is successful (valid), the system will give an output in the form of the action of opening, closing, and locking the house door automatically. The success rate of the system in doing pattern voice recognition is 80-90%. The optimal distance for accessing house doors in this system is less than 10 meters. The total response time required by the system to open and close and lock the user's door based on the allowed user input is ± 6 seconds.
Dynamic Time Warping
Lock (firearm)
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In this article, we present a novel user identification mechanism for smart spaces called Echo-ID (referred to as E-ID). Our solution relies on inaudible sound signals for capturing the user's behavioral tapping/typing characteristics while s/he types the PIN on a PIN-PAD, and uses them to identify the corresponding user from a set of ${N}$ enrolled inhabitants. E-ID proposes an all-inclusive pipeline that generates and transmits appropriate sound signals, and extracts a user-specific imprint from the recorded signals (E-Sign). For accurate identification of the corresponding user given an E-Sign sample, E-ID makes use of deep-learning (i.e., CNN for feature extraction) and SVM classifier (for making the identification decision). We implemented a proof of the concept of E-ID by leveraging the commodity speaker and microphone. Our evaluations revealed that E-ID can identify the users with an average accuracy of 93% to 78% from an enrolled group of 2-5 subjects, respectively.
Echo (communications protocol)
Identification
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Heart sound is one of the most important physiological signal of human body.Because of the totally different characteristics and high stability in various people,heart sound can be used as biometric verification feature.In this paper,based on GMM dynamic threshold algorithm and radio frequency identification technology,we develop a heart sound verification system.The system realizes identity registration and verification.
SIGNAL (programming language)
Feature (linguistics)
Human heart
Identification
Heart sounds
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Stethoscope
Heart sounds
Phonocardiogram
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