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Статті в журналах з теми "Physiological motion detection"
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Wang, Liting, Xiaoqing Ding, and Chi Fang. "Face live detection method based on physiological motion analysis." Tsinghua Science and Technology 14, no. 6 (December 2009): 685–90. http://dx.doi.org/10.1016/s1007-0214(09)70135-x.
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Krause, Bryan M., and Geoffrey M. Ghose. "Micropools of reliable area MT neurons explain rapid motion detection." Journal of Neurophysiology 120, no. 5 (November 1, 2018): 2396–409. http://dx.doi.org/10.1152/jn.00845.2017.
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Many models of perceptually based decisions postulate that actions are initiated when accumulated sensory signals reach a threshold level of activity. These models have received considerable neurophysiological support from recordings of individual neurons while animals are engaged in motion discrimination tasks. These experiments have found that the activity of neurons in a particular visual area strongly associated with motion processing (MT), when pooled over hundreds of milliseconds, is sufficient to explain behavioral timing and performance. However, this level of pooling may be problematic for urgent perceptual decisions in which rapid detection dictates temporally precise integration. In this paper, we explore the physiological basis of one such task in which macaques detected brief (~70 ms) transients of coherent motion within ~240 ms. We find that a simple linear summation model based on realistic stimulus responses of as few as 40 correlated neurons can predict the reliability and timing of rapid motion detection. The model naturally reproduces a distinctive physiological relationship observed in rapid detection tasks in which the individual neurons with the most reliable stimulus responses are also the most predictive of impending behavioral choices. Remarkably, we observed this relationship across our simulated neuronal populations even when all neurons within the pool were weighted equally with respect to readout. These results demonstrate that small numbers of reliable sensory neurons can dominate perceptual judgments without any explicit reliability based weighting and are sufficient to explain the accuracy, latency, and temporal precision of rapid detection. NEW & NOTEWORTHY Computational and psychophysical models suggest that performance in many perceptual tasks may be based on the preferential sampling of reliable neurons. Recent studies of MT neurons during rapid motion detection, in which only those neurons with the most reliable sensory responses were strongly predictive of the animals’ decisions, seemingly support this notion. Here we show that a simple threshold model without explicit reliability biases can explain both the behavioral accuracy and precision of these detections and the distribution of sensory- and choice-related signals across neurons.
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Zhang, Long, Xuezhi Yang, and Jing Shen. "Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters." Remote Sensing 13, no. 4 (February 21, 2021): 796. http://dx.doi.org/10.3390/rs13040796.
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The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.
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Han, Mianzhe, Yuki Todo, and Zheng Tang. "An Artificial Visual System for Three Dimensional Motion Direction Detection." Electronics 11, no. 24 (December 13, 2022): 4161. http://dx.doi.org/10.3390/electronics11244161.
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For mammals, enormous amounts of visual information are processed by neurons of the visual nervous system. The research of the direction selectivity is of great significance and local direction-selective ganglion neurons have been discovered. However, research is still at the one dimensional level and concentrated on a single cell. It remains challenging to explain the function and mechanism of the overall motion direction detection. In our previous papers, we have proposed a motion direction detection mechanism on the two dimensional level to solve these problems. The previous studies did not take into account that the information in the left and right retina is different and cannot be used to detect the three dimensional motion direction. Further effort is required to develop a more realistic system in three dimensions. In this paper, we propose a new three-dimensional artificial visual system to extend motion direction detection mechanism into three dimensions. We assumed that a neuron could detect the local motion of a single voxel object within three dimensional space. We also took into consideration that the information of the left and right retinas is different. Based on this binocular disparity, a realistic motion direction mechanism for three dimensions was established: the neurons received signals from the primary visual cortex of each eye and responded to motion in specific directions. There are a series of local direction-selective ganglion neurons arrayed on the retina by a logical AND operation. The response of each local direction detection neuron will be further integrated by the next neural layer to obtain the global motion direction. We carry out several computer simulations to demonstrate the validity of the mechanism. It shows that the proposed mechanism is capable of detecting the motion of complex three dimensional objects, which is consistent with most known physiological experimental results.
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Luo, Linbo, Yuanjing Li, Haiyan Yin, Shangwei Xie, Ruimin Hu, and Wentong Cai. "Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8984–92. http://dx.doi.org/10.1609/aaai.v37i7.26079.
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Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets.
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Liu, Hairen, and Wei Zhang. "Data Analysis of Athletes’ Physiological Indexes in Training and Competition Based on Wireless Sensor Network." Journal of Sensors 2021 (September 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/5923893.
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The application of physiological and biochemical indicators in athlete training and competition has become a hot research topic in related fields at home and abroad. Both coaches and scientific researchers hope to use quantitative physiological and biochemical indicators to study the load, fatigue, and recovery of athletes in training competitions and use them to scientifically guide athletes in training competitions, improve sports performance, and reduce injuries. This article introduces in detail the development status of wireless sensor network technology, energy consumption detection system, and ZigBee technology. On this basis, the focus is on the design of the detection terminal (coordinator and router node), the routing protocol of the ZigBee network, and the algorithm for the detection of human energy consumption. This subject proposes a design plan for the human exercise energy consumption detection system and researches and designs the wireless sensor network coordinator, router node, and host computer monitoring system. The microprocessors of the two types of network nodes use the single-chip microcomputer. Among them, the router node is composed of sensor modules, data transmission modules, and power modules; the software part is transplanted to ZigBee protocol Z-Stack, combined with the routing algorithm, and we add the corresponding node function code to achieve them. Based on the introduction of the development status and development points of the single-chip-based motion wireless sensor, this article focuses on the analysis of the single-chip-based motion wireless sensor network products. The common features of the single-chip microcomputer are wireless, huge low power consumption, and simple development. Engineering practice shows that the designed system is relatively good in terms of reliability and stability of data transmission; even in the case of severe noise interference and electromagnetic interference, the probability of network nodes malfunctioning is still very small. The router node processes and analyzes the collected motion data, calculates the energy consumption and motion state of human motion based on the acceleration value of each axis and extracts data characteristics, and transmits the obtained results to the coordinator for real-time display.
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Güttler, Jörg, Dany Bassily, Christos Georgoulas, Thomas Linner, and Thomas Bock. "Unobtrusive Tremor Detection While Gesture Controlling a Robotic Arm." Journal of Robotics and Mechatronics 27, no. 1 (February 20, 2015): 103–4. http://dx.doi.org/10.20965/jrm.2015.p0103.
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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/12.jpg"" width=""300"" />Gesture based validation</div> A light weight robotic arm (Jaco) has been interfaced with a novel gesture detection sensor (Leap Motion Controller), substituting complicated conventional input devices, i.e., joysticks and pads. Due to the enhanced precision and high throughput capabilities of the Leap Motion Controller, the unobtrusive measurement of physiological tremor can be extracted. An algorithm was developed to constantly detect and indicate potential user hand tremor patterns in real-time. Additionally a calibration algorithm was developed to allow an optimum mapping between the user hand movement, tracked by the Leap Motion Controller, and the Jaco arm, by filtering unwanted oscillations, allowing for a more natural human-computer interaction. </span>
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DOUKAS, CHARALAMPOS, and ILIAS MAGLOGIANNIS. "ADVANCED CLASSIFICATION AND RULES-BASED EVALUATION OF MOTION, VISUAL AND BIOSIGNAL DATA FOR PATIENT FALL INCIDENT DETECTION." International Journal on Artificial Intelligence Tools 19, no. 02 (April 2010): 175–91. http://dx.doi.org/10.1142/s0218213010000108.
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The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection system that may be used for patient activity recognition and emergency treatment. Visual data captured from the user's environment, using overhead cameras among with motion and physiological data collected from the subject's body are utilized. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects, while acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event, whereas the interpretation of biosignals like electrocardiogram (ECG) and blood oxygen saturation (SPO2) can indicate the severity of the incident with the help of rules-based evaluation. The paper includes also the assessment of several classifiers and meta-classifiers in terms of accuracy in detecting falls and a user based evaluation.
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Volpes, Gabriele, Simone Valenti, Giuseppe Genova, Chiara Barà, Antonino Parisi, Luca Faes, Alessandro Busacca, and Riccardo Pernice. "Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements." Biosensors 14, no. 4 (April 20, 2024): 205. http://dx.doi.org/10.3390/bios14040205.
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Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals’ physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.
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Dharmansyah, Dhika. "LITERATURE REVIEW: DESIGN OF INTERNET OF HEALTH THINGS (IOHT) MODEL FOR FALL RISK DETECTION IN ELDERLY AT HOME." Journal of Nursing Culture and Technology 1, no. 1 (May 1, 2024): 30–36. https://doi.org/10.70049/jnctech.v1i1.8.
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Background: Fall is a serious health issue among the elderly population with various contributing internal, environmental, and balancing activity-related risk factors. Internet of Health Things (IOHT) has a great potential to improve real-time elderly health monitoring and enable early detection of falls through risk-based intervention. Purpose: To know design an IOHT-based fall risk detection model for the elderly at home utilizing appropriate sensors and machine learning algorithms. Methods: A literature review was conducted to explore recent fall detection studies using motion, physiological, and environmental sensors in an IoT/IOHT-based system. Key findings were extracted and categorized based on sensor types and fall detection approaches. Results: Several motion sensors (accelerometer, gyroscope), physiological sensors (plantar pressure, inertial sensors), and environmental sensors (ultrasonic, sound) have been applied individually or in combination for falls risk prediction and detection among the elderly. Deep learning-based models have shown promising performance in identifying fall risks using multi-parameter sensor data. Conclusions: An IOHT model integrating various sensors shows potential for comprehensive fall risk monitoring and early intervention for the elderly at home. However, further developments in hardware, algorithms, clinical validation, and privacy/security are still needed to maximize the benefits of IOHT-enabled elderly healthcare.
Дисертації з теми "Physiological motion detection"
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Serieyssol, Alizée. "Correction des mouvements physiologiques sans appareillage externe en TEP : applications aux acquisitions à faible statistique pour la radioembolisation hépatique et la cardiologie." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0355.
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La Tomographie par Emission de Positons (TEP) est une modalité d’imagerie essentielle au diagnostic et au suivi thérapeutique en oncologie. Les mouvements physiologiques peuvent dégrader la qualité de l'image et avoir un impact sur la précision diagnostique et la quantification des images TEP. Ce programme de recherche se concentre sur la détection des mouvements physiologiques (respiration et battements cardiaques) sans utilisation d'appareillage externe, pour des applications cliniques très spécifiques. Des méthodes de compensation de ces mouvements sont développées afin de reconstruire une image corrigée de ces effets. Deux applications cliniques ont été identifiées pour évaluer les méthodologies mises en œuvre. La première concerne la radioembolisation hépatique basée sur l'imagerie TEP à l'90Y qui nécessite le développement de méthodes de détection et de correction du mouvement respiratoire pour des données impliquant une très faible statistique de comptage. La seconde est l’imagerie TEP cardiaque au 18F-FDG nécessitant le développement d’une méthode de double détection des mouvements respiratoire et cardiaque ainsi que des méthodes de compensation de ces deux mouvements physiologiques. Les résultats obtenus avec les méthodes de détection proposées sont comparés à ceux obtenus par des dispositifs externes : un soufflet (46-265679G-1, GE HealthCare) pour les signaux respiratoires et une électrocardiogramme (ECG) pour le signal cardiaque. Concernant les méthodes de correction, deux méthodes de correction sont proposées pour la radioembolisation hépatique et leur impact sur la dosimétrie post-traitement est étudiée en comparaison avec les résultats obtenus sans utilisation de méthodes de correction. La première méthode développée consiste à ne conserver que la phase quiescente du cycle respiratoire tandis que la deuxième utilise toute la statique en proposant un recalage rigide entre toutes les phases du cycle respiratoire. Deux autres méthodes sont implémentées pour la cardiologie et basées sur l’estimation de vecteurs de déformation 3D déterminés à partir des triggers cardiaques et respiratoires calculés grâce à la méthode détection proposée. La première méthode estime ces vecteurs de déformation par un recalage rigide entre les images de chaque cycle respiratoire alors que la deuxième méthode utilise les différents volumes du cœur. Dans cette méthode, les vecteurs de déformations 3D sont calculés en identifiant les volumes télédiastolique et télésystolique. L’évaluation de l’efficacité de ces méthodes est réalisée en comparant les images obtenues avec ces méthodes aux images non corrigées du mouvement ainsi qu’à celle reconstruite avec la méthode de correction utilisée en routine clinique sur les caméras TEP/TDM (algorithme Q.Static, General Electric HealthCare). Les résultats obtenus montrent une réelle amélioration de la qualité des images avec, pour les images cardiologiques, de meilleurs résultats que ceux obtenus avec la méthode de correction utilisée en routine clinique. Les résultats dosimétriques obtenus avec l’utilisation des deux méthodes de correction pour les données à l’Yttrium-90 démontrent une augmentation de la dose à la tumeurPositron emission tomography (PET) is an essential imaging modality for diagnosis and therapeutic follow-up in oncology. Physiological motion can degrade image quality and affect the diagnostic accuracy and quantification of PET images. This research program focuses on the detection of physiological motion (respiration and cardiac beating) without the use of an external device for very specific clinical applications. Methods to compensate for these movements will be developed to reconstruct an image corrected for these effects. Two clinical applications have been identified to evaluate the implemented methods. The first concerns hepatic radioembolization based on 90Y PET imaging, which requires the development of methods to detect and correct for respiratory motion for data with very low counting statistics. The second is 18F-FDG cardiac PET imaging, involving the development of a method for the dual detection of respiratory and cardiac movements, as well as methods for compensating for these two physiological movements. The results obtained with the proposed detection methods are compared with those obtained with external devices: a bellow (46-265679G-1, GE HealthCare) for the respiratory signals and an electrocardiogram (ECG) for the cardiac signal. Two correction methods are proposed for hepatic radioembolization and their impact on post-treatment dosimetry was evaluated in comparison with results obtained without the use of correction methods. The first method developed consists in keeping only the quiescent phase of the respiratory cycle, while the second uses all the statistics, proposing a rigid registration between all the respiration phases. Two other methods have been implemented for cardiology, based on the estimation of 3D deformation vectors obtained from cardiac and respiratory triggers calculated with the proposed detection method. The first method estimates these deformation vectors through a rigid registration between the images of each respiratory cycle, while the second method uses the different volumes of the heart. In this method, 3D deformation vectors are calculated by identifying the end diastolic and end systolic volumes. The efficacity of these methods is evaluated by comparing the images obtained using these methods with the non-motion-corrected images, as well as with the image reconstructed with the correction method used in clinical routine on PET/CT cameras (Q.Static algorithm, General Electric HealthCare). The obtained results demonstrate a real improvement in terms of image quality, with better results for cardiological images than those obtained with the correction method used in clinical routine. Dosimetric results obtained with both correction methods for Yttrium-90 data show an increase of the tumor dose
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Wu, Ping-Hsun, and 吳秉勳. "Design of Phase- and Self-Injection-Locked Radar and Its Application in Detection of Physiological Motions." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/05261518445379753062.
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博士國立臺灣大學
電信工程學研究所
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The phase- and self-injection-locked radar is presented in this dissertation for robust detection of physiological motions with high sensitivity. The innovative method injects the Doppler phase-modulated echo signal back into a phase-locked oscillator and obtains the baseband signal by directly sampling the voltage-controlled oscillator tuning voltage controlled by the phase-locked loop without any demodulation circuits. Phase noise analysis indicates that the proposed radar has the advantages of both the phase-locked oscillators and self-injection-locked oscillators to achieve superior signal-to-noise ratio gain against the low-frequency phase noise in the bandwidth containing the physiological motion information. Consequently, the proposed radar can serve for long-range detections with less transmitted power. In addition, this dissertation addresses the dc offset and the null point problems, which are two major challenging issues for conventional Doppler radar designs, in regard to reliable detection. The dc offset caused by clutter reflections and circuit imperfections is eliminated simply using a dual-tuning voltage-controlled oscillator without sophisticated clutter cancellation techniques. Analysis based on the classic injection locking equation shows that the dc offset can be removed without sensitivity degradation. Path-diversity transmission that switches between orthogonal self-injection-locked loops is employed to eliminate null points and reduce average transmitted power. Several prototype circuits are designed to justify the theory and design equations. Experiments confirm successful detection of physiological motions from a distance of 4 meters with −22 dBm average transmitted power.
Книги з теми "Physiological motion detection"
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Kautz, Dirk. Micro-iontophoretic studies on the physiological mechanism of auditory motion-direction: Detection in the inferior colliculus of the barn owl (Tyto alba). [s.l.]: [s.n.], 1997.
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Office, General Accounting. Air pollution: Improvements needed in detecting and preventing violations : report to the chairman, Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives. Washington, D.C: GAO, 1990.
Знайти повний текст джерелаЧастини книг з теми "Physiological motion detection"
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Okawai, Hiroaki, and Mitsuru Takashima. "Physiological Detection of Satisfaction for Services by Body Motion Wave Revealing Unconscious Responses Reflecting Activities of Autonomic Nervous Systems." In Serviceology for Smart Service System, 279–86. Tokyo: Springer Japan, 2017. http://dx.doi.org/10.1007/978-4-431-56074-6_31.
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Hemlathadhevi, A., Anu Disney D., Nishant Behar, Lalit Mohan Pant, C. M. Naveen Kumar, and Madiha Tahreem. "Framework Towards Detection of Stress Level Through Classifying Physiological Signals Using Blockchain Technology." In Advances in Computational Intelligence and Robotics, 403–16. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7367-5.ch027.
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Stress is a common aspect of everyday existence that everyone encounters at some point. Chronic stress, on the other hand, jeopardizes our well-being and disrupts our usual life. As a consequence, the ability to function and handle critical circumstances is significantly diminished. As a result, comprehending stresses and designing procedures with stress in mind is essential. This study introduces us to stress level detection through physiological signal classification using the blockchain technique. The present study proposes a framework for evaluating sleeping patterns by continually tracking the physiological signals that happen during the rapid eye motions and non-rapid eye motion phases of sleep. Aside from physiological variable shifts, length of sleep, snoring spectrum, eye motion, and limb motions are additionally examined.
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Gaggioli Andrea, Pioggia Giovanni, Tartarisco Gennaro, Baldus Giovanni, Ferro Marcello, Cipresso Pietro, Serino Silvia, et al. "A System for Automatic Detection of Momentary Stress in Naturalistic Settings." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-121-2-182.
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Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.
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HIRAHARA, Makoto, and Takashi NAGANO. "A NEURAL NETWORK FOR VISUAL MOTION DETECTION THAT CAN EXPLAIN PSYCHOPHYSICAL AND PHYSIOLOGICAL PHENOMENA." In Artificial Neural Networks, 1393–96. Elsevier, 1991. http://dx.doi.org/10.1016/b978-0-444-89178-5.50096-8.
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S.M, Revathi, Srinivasan R, Balamurugan C.R, and Kareemullah H. "Driver Stress Detection Based on IOT Motion Sensor Using Wearable Glove." In Applications of Artificial Intelligence and Machine Learning in Healthcare. Technoarete Publishing, 2022. http://dx.doi.org/10.36647/aaimlh/2022.01.b1.ch002.
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Stress conditions experienced by the driver is a serious problem in road safety. Driver error is the most common cause of road accidents. In this paper skin conductance is taken for analysis of driver drowsiness fatigue and mental stress. In order to minimize human error while driving it monitors stress and fatigue by measuring physiological parameters like skin acting like a conductor gives a response also called as Galvanic skin response and the motion is continuously monitored over a period of time. Internet of Things (IOT) based sensor used in driver’s health care is novel approach from the classical ways that includes visiting hospitals for clinical procedure and constant supervision of the person. It connects the health care professionals with the driver through smart device to monitor vitals without affecting the freedom of movement of the driver. This chapter introduces a viewof IOT functionality and its application with the sensing and wireless technique for implementing the required stress monitoring system for drivers. Further the Captured data is sent to an IOT Cloud Where Machine learning algorithms were deployed for computing the percentage of alertness and stress if the stress levels go beyond the threshold levels, then alert signal is sent to the driver from buzzer.
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Abadi, Richard V. "Perception with Unstable Fixation." In Advances in Understanding Mechanisms and Treatment of Infantile Forms of Nystagmus, 23–32. Oxford University PressNew York, NY, 2008. http://dx.doi.org/10.1093/oso/9780195342185.003.0003.
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Abstract Fixation behavior has an enormous influence on perception. Too little image motion and the scene fades; too much, and blurring and oscillopsia are experienced. To keep within an optimal operating range, a number of feedback control systems counter drifts and suppress unwanted saccades. Vision, which is driven by both bottom-up and topdown processing, is an important component. Thus physiological microsaccades and saccadic intrusions are modulated by exogenous and endogenous attention, while early onset afferent defects often lead to strabismus and nystagmus. The visual consequences of such fixation failures depend on the onset time of the visual loss, the nature of any attendant afferent defect, and the retinal-image dynamics. This chapter describes psychophysical studies that examine the spatial (contrast sensitivity, visual acuity, vernier acuity, and stereopsis) and temporal (absolute and relative detection and discrimination motion thresholds) visual performance of individuals with idiopathic and nonidiopathic congenital nystagmus.
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Senthilkumar, Laushya, Joana M. Warnecke, Julian Bollmann, and Thomas M. Deserno. "Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240576.
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Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.
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NEGRINI Alberto, NEGRINI Stefano, and SANTAMBROGIO Giorgio C. "Data Variability in the Analysis of Spinal Deformity: a Study Performed by means of the AUSCAN System." In Studies in Health Technology and Informatics. IOS Press, 1995. https://doi.org/10.3233/978-1-60750-859-5-101.
Повний текст джерелаАнотація:
This paper tackles the problem of analysing the variability of the experimental data provided by the AUSCAN System, an opto-electronic measurement device for the detection of the 3-D spinal geometry during static and dynamic posture. Various sources of data variability, ranging from pure errors of detection up to physiological variations of the spinal morphology due the control mechanism of posture, have been considered. The final results of this study show that the dynamic component of posture, associating with the motor-posture control system, causes the largest variability in the measure of the spinal morphology.
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Rajamohana S. P., Dharani A., Anushree P., Santhiya B., and Umamaheswari K. "Machine Learning Techniques for Healthcare Applications." In Advances in Social Networking and Online Communities, 236–51. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7522-1.ch012.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.
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Rajamohana S. P., Dharani A., Anushree P., Santhiya B., and Umamaheswari K. "Machine Learning Techniques for Healthcare Applications." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, 386–402. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch021.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.
Тези доповідей конференцій з теми "Physiological motion detection"
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Uddin, Md Taufeeq, and Shaun Canavan. "Synthesizing Physiological and Motion Data for Stress and Meditation Detection." In 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2019. http://dx.doi.org/10.1109/aciiw.2019.8925245.
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Perdana, Rizky Naufal, Budhi Irawan, Casi Setianingsih, Dian Rezky Wulandari, Ivan Satrio Pamungkas, Fajri Nurfauzan, Adinda Ophelia Putri Sakinah, and Muhammad Raihan Ramadhan. "Design of Smartdoor for Live Face Detection Based on Image Processing Using Physiological Motion Detection." In 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE). IEEE, 2022. http://dx.doi.org/10.1109/ismode56940.2022.10180411.
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Peng, Zheng, Ilde Lorato, Xi Long, Rong-Hao Liang, Deedee Kommers, Peter Andriessen, Ward Cottaar, Sander Stuijk, and Carola van Pul. "Body Motion Detection in Neonates Based on Motion Artifacts in Physiological Signals from a Clinical Patient Monitor." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630133.
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Gupta, Sanskriti, and Rekha Vig. "Detection and Correction of Head Motion and Physiological Artifacts in BOLD fMRI: A Study." In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2019. http://dx.doi.org/10.1109/confluence.2019.8776963.
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Ma, Zheren, Brandon C. Li, Zeyu Yan, Dongmei Chen, and Wei Li. "Wearable Sleepiness Detection Based on Characterization of Physiological Dynamics." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9849.
Повний текст джерелаАнотація:
Sleepiness has been considered as one of the major contributors to driver error that causes many automobile accidents. Among various technologies developed to address this issue, the electrooculography (EOG) signal is considered most suitable for sleepiness detection. It is simple, and resilient to environmental factors such as light intensity and driver movement. Most importantly, the physiological signal changes in an early stage and can be used to detect the on-set of human sleepiness. In this paper, we introduce the development of a wearable sleepiness detection system based on analyzing EOG signal dynamics. The system includes wearable sensors, amplifying and transmitting circuits, and a smart phone that could alarm the driver if sleepiness is detected. In this system, the EOG signal is considered as a neurophysiological response of the oculomotor system. Blink signatures are extracted from the EOG signal. It was found that the poles of a linearized blinking motion associated with an alert state are different from those associated with a sleepy state. Based on this understanding, an algorithm to detect the driver’s sleepiness was developed. A proof of concept device design has been completed. This system will help a driver to correct the behavior, and ultimately saves lives.
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Ray, Arkaprova, Iman Habibagahi, and Aydin Babakhani. "Fully Wireless and Batteryless Localization and Physiological Motion Detection System for Point-of-care Biomedical Applications." In 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2022. http://dx.doi.org/10.1109/biocas54905.2022.9948647.
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Galaup, Clement, Lama Séoud, and Patrice Renaud. "Multimodal HCI: a review of computational tools and their relevance to the detection of sexual presence." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004477.
Повний текст джерелаАнотація:
Cybersexuality, referring to sexual interactions facilitated by or involving sexual technologies, for better or worse, is poised to play an increasingly significant role in people's lives. The psychophysiological states stemming from such interactions with sexual technologies, and especially virtual reality (VR) scenarios, is termed "sexual presence" (SP). To automatically assess such a state may help detect problematic sexual responses, particularly for forensic purposes. This work aims to review the different methods used to analyse and algorithmically evaluate multimodal electroencephalography (EEG) -centric physiological signals through a multimodal human-computer interface (HCI) and to pinpoint those who prove relevant to the detection of (SP).Multimodal HCI are defined as the processing of combined natural modalities with multimedia system or environment. Each modality engages different human capabilities (cognitive, sensory, motion, perceptual). These capabilities, in response to the multimedia environment, can be quantified through psychophysiological signals such as EEG, electrocardiography (ECG), skin conductance, skin temperature, respiration rate, eye gaze, head movements, to name only the most common.While existing surveys have focused on the specific use of EEG to analyse emotions or on the measurement techniques and methods that have been used to record psycho-physiological signals, this work reviews the computational tools, mostly using machine and deep learning, to process, analyse and combine various physiological signals in HCI.Papers published in the last 10 years, combining at least two psycho-physiological signals in an HCI system were collected and reviewed, regardless of the field of application. The focus was mostly on the methodological aspects such as signal synchronization and calibration, fusion approach, model architecture, learning strategy. We put an emphasis on the methods that can be used to detect a subject’s condition in real time. At the light of this review, we can identify a research gap in terms of computational tools for multimodal data classification and prediction.This review will allow us to draw on existing work in other fields of application to address our specific application: to analyse EEG, oculometry and sexual plethysmography (penile for the men and vaginal for the women) signals together, using deep learning, to detect SP in subjects immersed in a VR environment with sexual content.
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Pungu Mwange, Marie-Anne, Fabien Rogister, and Luka Rukonic. "Measuring driving simulator adaptation using EDA." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001489.
Повний текст джерелаАнотація:
Most research about simulator adaptation focus on driving style and participants' comfort. However, in recent years, there is a growing interest in physiological data analysis as part of the user experience (UX) assessment. Furthermore, the application of machine learning (ML) techniques to those data may allow the automatic detection of stress and cognitive load. Previously, we noticed that new participants in experiments with our simulator were often in a constant state of tension. This prevented optimal training of our ML models as many of the collected data were not representative of a person's normal state.Our work focuses on improving driver's UX by keeping the cognitive load and stress at levels that do not interfere with the primary task of driving. We use a custom-made driving simulator as our testing platform and evaluate participants' emotional state with physiological signals, specifically electrodermal activity (EDA). EDA is the variation of the skin conductance created by sweat glands. It is linked to the sympathetic nervous system and is an indication of physiological and psychological arousal. We selected EDA because several studies have shown that it is a fast indicator of stress and cognitive load.To ensure that we are consistently collecting accurate data that could be fed to ML algorithms, we need to be able to correlate physiological reactions to external stimuli. We want to avoid them to be confused with general tension. Therefore, we need to determine the time it takes for most participants to physiologically adapt to our simulator. In this between-subjects study, we examined the impact of short time (ca. 10 min) exposures to the simulation and compared it with a longer exposure period (ca. 35 min).Another problem we faced was that some participants were too indisposed by driving in the simulator to complete testing sessions. Therefore, we needed to find a way to discriminate them during the recruitment process. Literature has shown that there might be a link between motion sickness and simulator sickness and in this study, we searched for a correlation between the motion sickness susceptibility questionnaire (MSSQ) and the self-reported simulator sickness using the simulator sickness questionnaire (SSQ).For our investigation, we recruited 22 people through an agency. They were divided in two groups. Group A (short-time exposures) had 10 participants between 25 and 69 years old (M=49.5; SD=17.1, 5 women, 5 men) and group B (long-time exposure) had 12 people between 28 and 65 years old (M=43; SD=12.8, 5 women, 7 men). We requested from the agency to recruit only active drivers of automatic transmissions cars as our simulator mimics this type of vehicle.Motion sickness susceptibility and discomfort felt in the simulator are moderately correlated. The coefficient value is 0.51. The number of participants of our study being small, further research is necessary to determine if the MSSQ can be used as a discriminator in the recruitment phase. In addition, we can conclude that a longer exposure of 35 min results overall in better physiological adaptation.
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Silva, Leonardo, Rafael Lima, Giovani Lucafo, Italo Sandoval, Pedro Garcia Freitas, and Otávio A. B. Penatti. "Photoplethysmography Signal Quality Assessment using Attentive-CNN Models." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbcas.2024.2206.
Повний текст джерелаАнотація:
Due to the rapid popularization of wearable computers such as smartwatches, Health Monitoring Applications (HMA) are becoming increasingly popular because of their capability to track different health indicators, including sleep patterns, heart rate, and activity tracking movements. These applications usually employ Photoplethysmography (PPG) sensors to monitor various aspects of an individual’s health and well-being. PPG is a non-invasive and cost-effective optical technique based on the detection of blood volume changes in the microvascular bed of tissue, capturing the dynamic physiological changes in the body with continuous measurements taken over time. Analyzing PPG as a time series enables the extraction of meaningful information about cardiovascular health and other physiological parameters, such as Heart Rate Variability (HRV), Peripheral Oxygen Saturation (SpO2), and sleep status. To enable reliable health indicators, it is important to have robustly sampled PPG signals. However, in practice, the PPG signal is often corrupted with different types of noise and artifacts due to motion, especially in scenarios where wearables are used. Therefore, Signal Quality Assessment (SQA) plays a fundamental role in determining the reliability of a given PPG for use in HMA. Considering this, in this work, we propose a novel PPG SQA method focused on the balance between storage size and classifier quality, aiming to achieve a lightweight and robust model. This model is developed using recent advances in attention-based strategies to significantly improve the performance of purely Convolutional Neural Network (CNN)-based SQA classifiers.
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Kretzschmar, Florian, Matthias Beggiato, and Alois Pichler. "Detection of Discomfort in Autonomous Driving via Stochastic Approximation." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002437.
Повний текст джерелаАнотація:
One of the most important goals in the field of autonomous driving development is to make the experience for the passenger as pleasant and comfortable as possible. In addition to traditional influence factors on passenger comfort, new aspects arise due to the transfer of control from the human to the vehicle. Some of these are apparent safety, motion sickness, user preferences regarding driving style and information needs. Ideally, the vehicle and the passenger should form a team, whereby the vehicle should be able to detect and predict situations of discomfort in real time and take measures accordingly. This requires not only the continuous monitoring of the passengers state but also the implementation of adequate mathematical models. To investigate how this teaming of human and automated agents can be shaped in the most effective way is a key topic of the Collaborative Research Center “Hybrid Societies (https://hybrid-societies.org/). In this framework, driving simulator data from the previous project “KomfoPilot” (https://bit.ly/komfopilot) is re-analyzed using new mathematical models. The participants in the study completed several automated drives and reported continuously situations of discomfort using a handset control. Sensor data was collected simultaneously using eye tracking glasses, a smart band, seat pressure sensors and video cameras for motion and face tracking. While pupil diameter, heart rate, interblink intervals, skin conductance and head movement have already been identified as potential single indicators of discomfort, it is now necessary to integrate these and other findings of the project into a functional multivariate model. In this paper, we investigate how such a model can be shaped to offer high prediction accuracy and viable practical implementation. The first important question – which arises from the heterogeneity of the participants – is whether to work with training data on an individual or aggregated level. We compare both possibilities by applying techniques from the field of stochastic approximation for clustering of the chosen training set and subsequent classification of the test data. In the case of an individual model for each participant, we furthermore divide the participants into subgroups and analyze whether there is a connection between the physiological reactions of a passenger and his/her demographic characteristics and driving experience. Finally, we discuss the potential of our method as a reliable prediction model as well as implications for future driving simulator studies and related research.