Human Physiological Signal Recognition Based on Piezoelectric Impedance Technique

2014 ◽  
Vol 687-691 ◽  
pp. 4089-4092
Author(s):  
Jun Zhang ◽  
Jie Huang ◽  
Hong Mei Tang ◽  
Xian Hua Li ◽  
Qing Yang Cai
Keyword(s):  
Human Body ◽  
Physiological Signals ◽  
Signal Recognition ◽  
Coupling Impedance ◽  
Experiment Platform ◽  
Physiological Signal ◽  
Impedance Technique ◽  
Piezoelectric Coupling ◽  
Piezoelectric Impedance ◽  
Set Up

In order to verify whether the piezoelectric impedance technology can be applied to detect the physiological signals of human body, the principle of piezoelectric coupling impedance theory and piezoelectric impedance technology using for human physiological signal detection was introduced in this paper. With an experiment platform set up, detection experiments based on the piezoelectric impedance technology were created. And the experimental1 was improved to avoid the influence of man-made factors on experiment result. Two methods were used to deal with the experimental data. The results show that the piezoelectric impedance technique can be applied to identify the human body physiological signal, and offers a totally new idea to detect the physiological signals of human body.

Detection on the Pulse Signal of Human Body Based on Piezoelectric Impedance Technique

2014 ◽  
Vol 989-994 ◽  
pp. 1120-1124
Author(s):  
Jun Zhang ◽  
Bang Zeng Guo ◽  
Hong Mei Tang
Keyword(s):  
Human Body ◽  
Pulse Wave ◽  
Pulse Signal ◽  
Experiment Platform ◽  
Impedance Technique ◽  
Piezoelectric Impedance ◽  
Series Of Experiments ◽  
Set Up ◽  
Impedance Sensor ◽  
Physiological Characters

Pulse wave is one of important physiological characters of human body. Detection on pulse sign is helpful for health care and treatment of disease. In order to detect the change of pulse signal of human body, the work principle of detection based on the piezoelectric impedance technique was presented and a new piezoelectric impedance sensor was designed. With an experiment platform set up, detection experiments of pulse signal were created. Two methods were used to deal with the experimental data. Results from a series of experiments intuitively show that with pressure applied to human body bigger and bigger, the pressure index increases gradually. It illustrates that the change of pulse signal is bigger and bigger. The result indicates that this method has good sensitivity and provides a new detection method. The experiment shows that the piezoelectric impedance technique is feasible to detect the change of pulse sign.

Keep Cell Phones and PDAs Away From EMG Sensors and the Human Body to Prevent Electromagnetic Interference Artifacts and Cancer

Biofeedback ◽  
2009 ◽  
Vol 37 (3) ◽  
pp. 114-116 ◽  
Author(s):  
I-Mei Lin ◽  
Erik Peper
Keyword(s):  
Human Body ◽  
Electromagnetic Interference ◽  
Cell Phone ◽  
Cell Phones ◽  
Epidemiological Studies ◽  
The Body ◽  
Physiological Signals ◽  
Cell Phone Use ◽  
Physiological Signal

Abstract Cell phones produce electromagnetic interference (EMI), which can cause artifacts in physiological recordings and be misinterpreted by the clinician. This study investigated the possible effect of EMI (electrical artifact) on physiological recordings when cell phones are activated/ringing. The procedure consisted of placing the cell phone at varying distances from surface electromyographic sensors. Depending on the orientation of the cell phone's antenna, the EMI produced an artifact in the physiological signal for up to 175 cm (6 ft) that can be misinterpreted by the therapist. To avoid EMI artifacts, clients and therapists should turn off their cell phones when recording physiological signals. This means turning the cell phone off and not just switching it to vibrate. In addition, recent epidemiological studies suggest that long-term intensive cell phone use may increase the risk of gliomas, auditory tumors, and salivary tumors on the side of the head to which the person places the cell phone. Thus, to reduce artifacts and biological harm, the authors recommend keeping the cell phone away from the body and the biofeedback equipment.

Emotion Recognition Based on Chaos Characteristics of Physiological Signals

2013 ◽  
Vol 380-384 ◽  
pp. 3750-3753 ◽  
Author(s):  
Chun Yan Nie ◽  
Rui Li ◽  
Ju Wang
Keyword(s):  
Nonlinear Dynamics ◽  
Emotion Recognition ◽  
Classical Theory ◽  
Chaos Theory ◽  
Biomedical Engineering ◽  
Recognition System ◽  
Physiological Signals ◽  
Signal Recognition ◽  
Physiological Signal ◽  
Linear Signal

Changes of physiological signals are affected by human emotions, but also the emotional fluctuations are reflected by the body's variation of physiological signal's feature. Physiological signal is a non-linear signal ,nonlinear dynamics and biomedical engineering ,which based on chaos theory, providing us a new method for studying on the parameters of these complex physiological signals which can hardly described by the classical theory. This paper shows physiological emotion signal recognition system based on the chaotic characteristics, and than describes some current applications of chaotic characteristics for multiple physiological signals on emotional recognition.

Wrist Wearable Physiological Signal Detection Equipment with Biochemical Materials

2012 ◽  
Vol 459 ◽  
pp. 293-297 ◽  
Author(s):  
Xing Chen ◽  
Hong Lun Hou ◽  
Ming Hui Wu ◽  
Mei Mei Huo
Keyword(s):  
Health Care ◽  
Oxygen Saturation ◽  
Signal Detection ◽  
Human Body ◽  
Family Health ◽  
Medical Field ◽  
Physiological Signal ◽  
Potential Value ◽  
Care Field ◽  
Physiological Information

This paper designed a wrist Device which can detect physiological information and save the information data. The information got by device is including Oxygen saturation of blood, Pulse rate and steps. And the device even can distinguish the state of human body between fall and normal activities with 3-axis accelerometer. The equipment designed for family health care and remote healthy care field. The operation of device is so easy to be mastered that the device might have a potential value for the future medical field

Semiconductor nano materials in the reconstruction of wearable human body sensor

2021 ◽  
pp. 1-14
Author(s):  
Junbai Pan ◽  
Yangong Zheng ◽  
Jinkai Jin ◽  
Xiang Cai ◽  
Chencheng Wang
Keyword(s):  
Human Body ◽  
Bp Neural Network ◽  
Zinc Concentration ◽  
Physiological Data ◽  
Processing Unit ◽  
Nano Materials ◽  
Experimental Conditions ◽  
Control Center ◽  
Physiological Signal ◽  
Central Processing

In view of the shortcomings of the current wearable human body sensor, such as poor comfort and low sensing accuracy, the application of semiconductor nano materials in the reconstruction of wearable human body sensor is studied. The best zinc concentration of 10 mm and the best reaction temperature of 75∘C were selected as experimental conditions to prepare the modified silk. The two ends of the silk sensor were connected by silver glue and wire respectively to form a single silk sensor. The sensor is placed in the wearable clothing of the wearable human body sensor, which uses the sensor to sense the physiological signal of human body and sends it to the control center. The central processing unit of the control center uses the data eigenvalue fusion decision-making method of BP neural network to process the physiological data of human body and then transmits it to the display terminal to realize the physiological data induction of human body. The experimental results show that the human body sensor can effectively sense human heart rate, blood oxygen signal, blood pressure and other physiological signals, and the sensing accuracy is above 97%.

Kansei’s Physiological Measurement and Its Application (2)

2010 ◽  
pp. 319-329 ◽  
Author(s):  
Santoso Handri ◽  
Shusaku Nomura
Keyword(s):  
Beating Heart ◽  
Physiological Signals ◽  
Kansei Engineering ◽  
Internal Organs ◽  
Physiological Measurement ◽  
New Approach ◽  
Engineering Research ◽  
Physiological Signal ◽  
Research Fields ◽  
Metabolic Activities

Physiological signals or biosignals are electrical, chemical, or mechanical signals that created by biological events such as a beating heart or a contracting muscle producing signals that can be measured and analyzed. These signals are generated from the metabolic activities of human internal organs. Therefore, in certain conditions, physiological signals have different pattern between healthy and unhealthy individuals. Based on this information, generally, physicians take some action and treat their patients. However, utilizing physiological signals is a new approach in Kansei engineering research fields for coping with human sensitivity. This study focuses on the possibility of physiological signal application in Kansei engineering.

scholarly journals Detection of Physiological Signals Based on Graphene Using a Simple and Low-Cost Method

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1656 ◽  
Author(s):  
Liping Xie ◽  
Xingyu Zi ◽  
Qingshi Meng ◽  
Zhiwen Liu ◽  
Lisheng Xu
Keyword(s):  
Graphene Sheet ◽  
Pulse Wave ◽  
Low Cost ◽  
Wearable Sensors ◽  
Unmet Need ◽  
Expansion Method ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Solid Foundation ◽  
Processing Circuit

Despite that graphene has been extensively used in flexible wearable sensors, it remains an unmet need to fabricate a graphene-based sensor by a simple and low-cost method. Here, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a sensor is fabricated by sealing of a graphene sheet with polyurethane (PU) medical film. Compared with other graphene-based sensors, it greatly simplifies the fabrication process and enables the effective measurement of signals. The resistance of graphene sheet changes linearly with the deformation of the graphene sensor, which lays a solid foundation for the detection of physiological signals. A signal processing circuit is developed to output the physiological signals in the form of electrical signals. The sensor was used to measure finger bending motion signals, respiration signals and pulse wave signals. All the results demonstrate that the graphene sensor fabricated by the simple and low-cost method is a promising platform for physiological signal measurement.

scholarly journals Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4253 ◽  
Author(s):  
JeeEun Lee ◽  
Sun K. Yoo
Keyword(s):  
Feature Selection ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Cognitive Status ◽  
Physiological Signals ◽  
Self Assessment ◽  
Physiological Signal ◽  
Distribution Shape ◽  
The Difference ◽  
The Individual

First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.

Linear and Nonlinear Approaches to the Analysis of R-R Interval Variability

2004 ◽  
Vol 5 (3) ◽  
pp. 211-221 ◽  
Author(s):  
Autumn Schumacher
Keyword(s):  
Signal Analysis ◽  
Interval Data ◽  
Isolated Rat Heart ◽  
Physiological Signals ◽  
Physiological Data ◽  
Analysis Techniques ◽  
Physiological Signal ◽  
Quantification Analysis ◽  
Linear And Nonlinear ◽  
Signal Variability

Analysis techniques derived from linear and non-linear dynamics systems theory qualify and quantify physiological signal variability. Both clinicians and researchers use physiological signals in their scopes of practice. The clinician monitors patients with signal-analysis technology, and the researcher analyzes physiological data with signal-analysis techniques. Understanding the theoretical basis for analyzing physiological signals within one’s scope of practice ensures proper interpretation of the relationship between physiological function and signal variability. This article explains the concepts of linear and nonlinear signal analysis and illustrates these concepts with descriptions of power spectrum analysis and recurrence quantification analysis. This article also briefly describes the relevance of these 2 techniques to R-to-R wave interval (i.e., heart rate variability) signal analysis and demonstrates their application to R-to-R wave interval data obtained from an isolated rat heart model.

Assessing Remote Physiological Signals Acquisition Experiments

2014 ◽  
Author(s):  
Carla Barros ◽  
Celina P. Leão ◽  
Filipe Pereira ◽  
Filomena Soares ◽  
José Machado ◽  
...  
Keyword(s):  
Engineering Education ◽  
Learning Style ◽  
Human Body ◽  
Biomedical Engineering ◽  
Research Team ◽  
Engineering Students ◽  
Physiological Signals ◽  
Remote Laboratory ◽  
Remote Laboratories ◽  
Biomedical Engineering Education

A great number of remote laboratories has been implemented in the engineering field. Nevertheless, there are few approaching the bioengineering area. The present paper will describe not only an innovative remote laboratory developed for biomedical engineering education, but also its assessment based on the target public’s feedback. The remote laboratory developed by the research team intends to provide the physiological signals remote acquisition from human body, supported by theory to a greater understanding of learned concepts. This tool is geared towards the undergraduate biomedical engineering students. Therefore, a sample of twelve students took part in a limited study conducted to quantitatively and qualitatively assess the remote laboratory. The study was undertaken using two questionnaires, one distributed before and other after the performance of a remote experiment. Moreover, the information about the learning style/method, employed by each student, was collected in order to devise strategies for future applications development and to make the remote laboratory suitable for the target public.

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