دانلود و نمایش مقالات مرتبط با Physiological signals::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی 2

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Physiological signals

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN
تشخیص شخصی نوار قلب ECG مبتنی بر وابستگی های عملکردی و ساختاری سیگنالها با استفاده از نمایش فرکانس زمان و CNN مورفولوژیکی تکاملی-2021
Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks
مقاله انگلیسی
2 Biometric recognition using wearable devices in real-life settings
تشخیص بیومتریک با استفاده از دستگاه های پوشیدنی در تنظیمات واقعی-2021
The popularity of wearable devices, such as smart glasses, chestbands, and wristbands, is nowadays rapidly growing, thanks to the fact that they can be used to track physical activity and monitor users’ health. Recently, researchers have proposed to exploit their capability to collect physiological signals for enabling automatic user recognition. Wearable devices inherently provide the means for detecting their unauthorized usage, or for being used as front-end in biometric recognition systems controlling the access to either physical or virtual locations and services. The present work evaluates the feasibility of performing biometric recognition using signals captured by wearable devices, considering data collected through off-the-shelf commercial wristbands, and comparing recordings taken during two distinct sessions separated by an average time of 7 days. In more detail, recognition is performed leveraging on electrodermal activity (EDA) and blood volume pulse (BVP), considering measurements taken from 17 subjects performing natural activities such as attending or teaching lectures. Several tests have been carried out to determine the most effective representation of the considered EDA and BVP signals, as well as the most suitable classifier. The best recognition performance has been achieved exploiting convolutional neural networks to extract discriminative characteristics from the combined spectrograms of the employed EDA and BVP data, guaranteeing average correct identification rate of 98.58% for test samples lasting 30 seconds.
Keywords: Wearable | Biometrics | Machine learning
مقاله انگلیسی
3 Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals
تجزیه و تحلیل عاطفی داده های بزرگ چند متغیره: یک مرور جامع با استفاده از سیگنال های متنی ، صوتی ، تصویری و فیزیولوژیکی-2020
Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of researchers and practitioners in various fields, including artificial intelligence, natural language processing, cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the literature have been limited to discussions using text, audio, and visual modalities. So far, to the authors knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of physiological modalities for affective computing brings several benefits in that the signals can be used in different environmental conditions, more robust systems can be constructed in combination with other modalities, and it has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.
Keywords: Affective computing | Multimodal fusion | Sentiment databases | Sentiment analysis | Affective applications
مقاله انگلیسی
4 Assessing occupants’ personal attributes in relation to human perception of environmental comfort: Measurement procedure and data analysis
ارزیابی خصوصیات شخصی سرنشینان در رابطه با ادراک انسان از آسایش محیطی: روش اندازه گیری و تجزیه و تحلیل داده ها-2020
The assessment of occupants’ wellbeing and productivity impact on building energy management is becoming a key topic in recent years due to increasing performance of the building stock still threaten by occupants’ behavior variability. The paper aims to deeply investigate human perception in indoors which drives occupants’ wellbeing and behavior through a novel measurement procedure, aimed at producing a multipurpose comfort perception scheme, i.e. considering thermal, visual, acoustic, and air quality comfort spheres. Data belonging to different domains of human perception are simultaneously measured: physical environmental parameters, physiological signals, and subjective responses. A preliminary series of measurement tests is here presented specifically focused on human response to thermal stimuli, i.e. subject exposed to increasing/decreasing temperature. Obtained data and are thus analyzed by coupling (i) physiological signals and subject responses through machine learning techniques, and (ii) personal attributes to sensation votes and environmental data variations. Results show potentials of the proposed measurement procedure which allows a comprehensive collection of physical attributes, physiological signals, and subjects’ psychological characterization. In conclusion, this work demonstrates the strict connection, with a prediction accuracy up to 84%, between physiological parameters (Heart Rate Variability and its indices) and human thermal comfort, opening the perspective of real-time measuring comfort for control and energy management purposes, taking into account human-centric parameters.
Keywords: Indoor whole comfort | Occupancy behavior | Wearable sensing | Microclimate | Multi-domain comfort | Energy efficiency in buildings
مقاله انگلیسی
5 Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device
نظارت بر زمان واقعی نوار قلب با استفاده از سنجش فشاری در دستگاه لبه چند هسته ای ناهمگن-2020
In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the hu- man body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and la- tency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gateway-centric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper ex- plores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big. little TM multicore for different signal dimension and allocated computational resources. Ex- perimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.
Keywords: Ambulatory ECG monitoring | Heterogeneous multicore solution | Compressive sensing | Edge computing
مقاله انگلیسی
6 SKA-PS: Secure key agreement protocol using physiological signals
SKA-PS: پروتکل توافق کلید امن با استفاده از سیگنال های فیزیولوژیکی-2019
In this paper, we propose SKA-PS, a novel Secure Key Agreement protocol using Physiological Signals, for Body Area Networks (BANs). Our protocol generates symmetric cryptographic keys using the physio- logical parameters derived from the physiological signals of the users, such as electrocardiogram, photo- plethysmogram and blood pressure. In our construction, we reduce the problem of secure key agreement into the problem of set reconciliation by representing the physiological parameter sequences generated from the physiological signals of the BAN users with appropriate sets. When properly selected param- eters are applied, biosensors of the same BAN user can agree on symmetric cryptographic keys with remarkably high true match and low false match rates (as much as 100% and 0.46% for pairwise execu- tion, and 100% and 0.26% for group execution, respectively), and low communication, computational and storage costs. We implemented our model in an embedded system, thus the results show real imple- mentation outcomes. Moreover, we comparatively analyze the performance of SKA-PS with two existing bio-cryptographic key agreement protocols and show that SKA-PS outperforms both in all performance metrics.
Keywords: Cryptographic key generation | Body area network security | Physiological signals | Key agreement | Bio-cryptography
مقاله انگلیسی
7 A mobile application to report and detect 3D body emotional poses
یک برنامه کاربردی تلفن همراه برای گزارش و کشف نکات سه بعدی عاطفی بدن-2019
Most research into automatic emotion recognition is focused on facial expressions or physiological signals, while the exploitation of body postures has scarcely been explored, although they can be useful for emo- tion detection. This paper first explores a mechanism for self-reporting body postures with a novel easy- to-use mobile application called EmoPose. The app detects emotional states from self-reported poses, classifying them into the six basic emotions proposed by Ekman and a neutral state. The poses identi- fied by Schindler et al. have been used as a reference and the nearest neighbor algorithm used for the classification of poses. Finally, the accuracy in detecting emotions has been assessed by means of poses reported by a sample of users.
Keywords: Affective com puting | App | Emotion detection | Mobile application | Pose detection | Expert system
مقاله انگلیسی
8 Music and natural sounds in an auditory steady-state response based brain–computer interface to increase user acceptance
موسیقی و صداهای طبیعی در یک رابط مغز و رایانه مبتنی بر پاسخ پایدار شنوایی برای افزایش پذیرش کاربران-2017
Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain– computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42 Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy.
Keywords:Brain–computer interface (BCI) | Auditory steady-state response (ASSR) | Auditory stimulation | Music | Natural sounds | Ergonomics
مقاله انگلیسی
9 Computer tool to evaluate the cue reactivity of chemically dependent individuals
ابزار رایانه ای برای ارزیابی واکنش پذیری نشانه های وابسته به مواد شیمیایی-2017
Article history:Received 12 February 2016Revised 31 October 2016Accepted 23 November 2016Keywords: Computer tool AnxietyCue reactivity Chemically dependentBackground and objective: Anxiety is one of the major influences on the dropout of relapse and treat- ment of substance abuse treatment. Chemically dependent individuals need (CDI) to be aware of their emotional state in situations of risk during their treatment. Many patients do not agree with the di- agnosis of the therapist when considering them vulnerable to environmental stimuli related to drugs. This research presents a cue reactivity detection tool based on a device acquiring physiological signals connected to personal computer. Depending on the variations of the emotional state of the drug addict, alteration of the physiological signals will be detected by the computer tool (CT) which will modify the displayed virtual sets without intervention of the therapist.Methods: Developed in 3ds Max® software, the CT is composed of scenarios and objects that are in the habit of marijuana and cocaine dependent individual’s daily life. The interaction with the environment is accomplished using a Human-Computer Interface (HCI) that converts incoming physiological signals indicating anxiety state into commands that change the scenes. Anxiety was characterized by the average variability from cardiac and respiratory rate of 30 volunteers submitted stress environment situations. To evaluate the effectiveness of cue reactivity a total of 50 volunteers who were marijuana, cocaine or both dependent were accompanied.Results: Prior to CT, the results demonstrated a poor correlation between the therapists’ predictions and those of the chemically dependent individuals. After exposure to the CT, there was a significant increase of 73% in awareness of the risks of relapse.Conclusion: We confirmed the hypothesis that the CT, controlled only by physiological signals, increases the perception of vulnerability to risk situations of individuals with dependence on marijuana, cocaine or both.© 2016 Elsevier Ireland Ltd. All rights reserved.
Keywords: Computer tool | Anxiety | Cue reactivity | Chemically dependent
مقاله انگلیسی
10 Smart Phone Based Data Mining for Human Activity Recognition
تلفن های هوشمند مبتنی بر داده کاوی برای به رسمیت شناختن فعالیت انسانی-2015
Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activitymonitoring for eHealth application scenarios for elderly, disabled and people with special needs.© 2014 The Authors. Published by Elsevier B.V.Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014)
smart phone | activity recognition | machine learning | assisted living
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 686 :::::::: بازدید دیروز: 3084 :::::::: بازدید کل: 3770 :::::::: افراد آنلاین: 13