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Journal articles on the topic 'Physiological motion detection'
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1
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.
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Little, Kieran, Bobby K Pappachan, Sibo Yang, Bernardo Noronha, Domenico Campolo, and Dino Accoto. "Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques." Sensors 21, no. 2 (January 12, 2021): 498. http://dx.doi.org/10.3390/s21020498.
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Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.
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Peng, Xidong, Xinge Zhu, and Yuexin Ma. "CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2047–55. http://dx.doi.org/10.1609/aaai.v37i2.25297.
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Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. By exploring domain-invariant 3D geometric characteristics and motion patterns, we present an unsupervised domain adaptation method that overcomes above difficulties. First, we propose the Spatial Geometry Alignment module to extract similar 3D shape geometric features of the same object class to align two domains, while eliminating the effect of distinct point distributions. Second, we present Temporal Motion Alignment module to utilize motion features in sequential frames of data to match two domains. Prototypes generated from two modules are incorporated into the pseudo-label reweighting procedure and contribute to our effective self-training framework for the target domain. Extensive experiments show that our method achieves state-of-the-art performance on cross-device datasets, especially for the datasets with large gaps captured by mechanical scanning LiDARs and solid-state LiDARs in various scenes. Project homepage is at https://github.com/4DVLab/CL3D.git.
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O'Carroll, David C., and Steven D. Wiederman. "Contrast sensitivity and the detection of moving patterns and features." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1636 (February 19, 2014): 20130043. http://dx.doi.org/10.1098/rstb.2013.0043.
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Theories based on optimal sampling by the retina have been widely applied to visual ecology at the level of the optics of the eye, supported by visual behaviour. This leads to speculation about the additional processing that must lie in between—in the brain itself. But fewer studies have adopted a quantitative approach to evaluating the detectability of specific features in these neural pathways. We briefly review this approach with a focus on contrast sensitivity of two parallel pathways for motion processing in insects, one used for analysis of wide-field optic flow, the other for detection of small features. We further use a combination of optical modelling of image blur and physiological recording from both photoreceptors and higher-order small target motion detector neurons sensitive to small targets to show that such neurons operate right at the limits imposed by the optics of the eye and the noise level of single photoreceptors. Despite this, and the limitation of only being able to use information from adjacent receptors to detect target motion, they achieve a contrast sensitivity that rivals that of wide-field motion sensitive pathways in either insects or vertebrates—among the highest in absolute terms seen in any animal.
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Neelon, Michael F., and Rick L. Jenison. "Act globally, think locally." Behavioral and Brain Sciences 24, no. 2 (April 2001): 231–32. http://dx.doi.org/10.1017/s0140525x0141394x.
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The authors attempt to prove that single energy arrays cannot specify reality. We offer contrary evidence that motion structures the acoustic array to specify fundamental attributes of the source. Against direct detection in general, we cite evidence that humans weight acoustic inputs differentially when making perceptual judgments of auditory motion.
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Giansanti, Daniele, and Giovanni Maccioni. "Physiological motion monitoring: a wearable device and adaptative algorithm for sit-to-stand timing detection." Physiological Measurement 27, no. 8 (June 2, 2006): 713–23. http://dx.doi.org/10.1088/0967-3334/27/8/006.
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Yang, Sibo, Neha P. Garg, Ruobin Gao, Meng Yuan, Bernardo Noronha, Wei Tech Ang, and Dino Accoto. "Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots." Sensors 23, no. 6 (March 10, 2023): 2998. http://dx.doi.org/10.3390/s23062998.
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The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.
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Xie, Liping, Xingyu Zi, Qingshi Meng, Zhiwen Liu, and Lisheng Xu. "Detection of Physiological Signals Based on Graphene Using a Simple and Low-Cost Method." Sensors 19, no. 7 (April 6, 2019): 1656. http://dx.doi.org/10.3390/s19071656.
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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.
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Abdel-Latif, Mahmoud M., Mudassir M. Rashid, Mohammad Reza Askari, Andrew Shahidehpour, Mohammad Ahmadasas, Minsun Park, Lisa Sharp, Lauretta Quinn, and Ali Cinar. "Acute Psychological Stress Detection Using Explainable Artificial Intelligence for Automated Insulin Delivery." Signals 5, no. 3 (July 30, 2024): 494–507. http://dx.doi.org/10.3390/signals5030026.
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Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the detection of APS using physiological variables measured by the Empatica E4 wristband and employs explainable machine learning to evaluate the importance of the physiological signals. The extreme gradient boosting model is developed for classification of APS and non-stress (NS) with weighted training, achieving an overall accuracy of 99.93%. The Shapley additive explanations (SHAP) technique is employed to interpret the global importance of the physiological signals, determining the order of importance for the variables from most to least as galvanic skin response (GSR), heart rate (HR), skin temperature (ST), and motion sensors (accelerometer readings). The increase in GSR and HR are positively correlated with the occurrence of APS as indicated by high positive SHAP values. The SHAP technique is also used to explain the local signal importance for particular instances of misclassified samples. The detection of APS can inform multivariable automated insulin delivery systems to intervene to counteract the APS-induced glycemic excursions in people with type 1 diabetes.
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Panagi, S., Α. Hadjiconstanti, G. Charitou, D. Kaolis, I. Petrou, C. Kyriacou, and Y. Parpottas. "A moving liver phantom in an anthropomorphic thorax for SPECT MP imaging." Physical and Engineering Sciences in Medicine 45, no. 1 (January 1, 2022): 63–72. http://dx.doi.org/10.1007/s13246-021-01081-4.
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AbstractCranio-caudal respiratory motion and liver activity cause a variety of complex myocardial perfusion (MP) artifacts, especially in the inferior myocardial wall, that may also mask cardiac defects. To assess and characterise such artifacts, an anthropomorphic thorax with moving thoracic phantoms can be utilised in SPECT MP imaging. In this study, a liver phantom was developed and anatomically added into an anthropomorphic phantom that also encloses an ECG beating cardiac phantom and breathing lungs’ phantom. A cranio-caudal respiratory motion was also developed for the liver phantom and it was synchronised with the corresponding ones of the other thoracic phantoms. This continuous motion was further divided into isochronous dynamic respiratory phases, from end-exhalation to end-inspiration, to perform SPECT acquisitions in different respiratory phases. The new motions’ parameters and settings were measured by mechanical means and also validated in a clinical environment by acquiring CT images and by using two imaging software packages. To demonstrate the new imaging capabilities of the phantom assembly, SPECT/CT MP acquisitions were performed and compared to previous phantom and patients studies. All thoracic phantoms can precisely perform physiological motions within the anthropomorphic thorax. The new capabilities of the phantom assembly allow to perform SPECT/CT MP acquisitions for different cardiac-liver activity ratios and cardiac-liver proximities in supine and, for first time, in prone position. Thus, MP artifacts can be characterised and motion correction can be performed due to these new capabilities. The impact of artifacts and motion correction on defect detection can be also investigated.
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Jacobs, David M., Sverker Runeson, and Isabell E. K. Andersson. "Reliance on constraints means detection of information." Behavioral and Brain Sciences 24, no. 4 (August 2001): 679–80. http://dx.doi.org/10.1017/s0140525x01440088.
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We argue four points. First, perception always relies on environmental constraints, not only in special cases. Second, constraints are taken advantage of by detecting information granted by the constraints rather than by internalizing them. Third, apparent motion phenomena reveal reliance on constraints that are irrelevant in everyday perception. Fourth, constraints are selected through individual learning as well as evolution. The “perceptual-concept-of-velocity” phenomenon is featured as a relevant case. [Hecht; Kubovy & Epstein; Shepard]
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Zhang, Yifan, Shuang Song, Rik Vullings, Dwaipayan Biswas, Neide Simões-Capela, Nick van Helleputte, Chris van Hoof, and Willemijn Groenendaal. "Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths." Sensors 19, no. 3 (February 7, 2019): 673. http://dx.doi.org/10.3390/s19030673.
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Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.
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ROSENBERG, ARI, PASCAL WALLISCH, and DAVID C. BRADLEY. "Responses to direction and transparent motion stimuli in area FST of the macaque." Visual Neuroscience 25, no. 2 (March 2008): 187–95. http://dx.doi.org/10.1017/s0952523808080528.
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AbstractMotion transparency occurs when multiple object velocities are present within a local region of retinotopic space. Transparent signals can carry information useful in the segmentation of moving objects and in the extraction of three-dimensional structure from relative motion cues. However, the physiological substrate underlying the detection of motion transparency is poorly understood. Direction tuned neurons in area MT are suppressed by transparent stimuli, suggesting that other motion sensitive areas may be needed to represent this signal robustly. Recent neuroimaging evidence implicated two such areas in the macaque superior temporal sulcus. We studied one of these, FST, with electrophysiological methods and found that a large fraction of the neurons responded well to two opposite directions of motion and to transparent stimuli containing those same directions. A linear combination of MT-like responses qualitatively reproduces this behavior and predicts that FST neurons can be tuned for transparent motion containing specific direction and depth components. We suggest that FST plays a role in motion segmentation based on transparent signals.
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Wiederman, Steven D., and David C. O’Carroll. "Biologically Inspired Feature Detection Using Cascaded Correlations of off and on Channels." Journal of Artificial Intelligence and Soft Computing Research 3, no. 1 (January 1, 2013): 5–14. http://dx.doi.org/10.2478/jaiscr-2014-0001.
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Abstract Flying insects are valuable animal models for elucidating computational processes underlying visual motion detection. For example, optical flow analysis by wide-field motion processing neurons in the insect visual system has been investigated from both behavioral and physiological perspectives [1]. This has resulted in useful computational models with diverse applications [2,3]. In addition, some insects must also extract the movement of their prey or conspecifics from their environment. Such insects have the ability to detect and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological techniques to record from small target motion detector (STMD) neurons in the insect brain that are likely to subserve these behaviors. Inspired by such recordings, we previously proposed an ‘elementary’ small target motion detector (ESTMD) model that accounts for the spatial and temporal tuning of such neurons and even their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as direction selectivity [9] and response facilitation for objects moving on extended trajectories [10] are not accounted for by this model. We therefore propose here two model variants that cascade an ESTMD model with a traditional motion detection model algorithm, the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal tuning and background clutter rejection) while also capturing the direction selectivity observed in some STMD neurons. By encapsulating the properties of biological STMD neurons we aim to develop computational models that can simulate the remarkable capabilities of insects in target discrimination and pursuit for applications in robotics and artificial vision systems.
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Puli, Akshay, and Azadeh Kushki. "Toward Automatic Anxiety Detection in Autism: A Real-Time Algorithm for Detecting Physiological Arousal in the Presence of Motion." IEEE Transactions on Biomedical Engineering 67, no. 3 (March 2020): 646–57. http://dx.doi.org/10.1109/tbme.2019.2919273.
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Sharma, Ashish, and Gaurav Sethi. "Fatigue Detection Post Physical Activity Using Machine Learning Algorithms." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012072. http://dx.doi.org/10.1088/1742-6596/2327/1/012072.
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Abstract Construction work is purely effortful and the prevention of injuries at construction job sites is essential for encouraging worker’s well being and health which is generally overlooked at the construction sites. World’s construction industry is one amongst those having unsatisfactory work health issues. A large number of laborers and construction workers have to undergo fatigue risk at their job place. This paper describes the current state of the art of the research carried out in case of fatigue assessment after performing some physical activity providing an insight into fatigue, its detection and an overview of the causes of risk fatigue and its countermeasures. A number of subjective and objective fatigue assessment approaches have been used that have further stimulated the inclusion of latest and advanced approaches for fatigue detection. Although individual’s knowledge regarding the fatigue detection approaches has been enhanced, there is as yet minimal research in the field of fatigue detection post physical activity. In this study the stress would be on objective fatigue detection techniques where the acquisition of physiological data of construction workers is required. The study provides a ground for detection of physiological fatigue based on motion capture data in the form of videos.
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Amenedo, Elena, Paula Pazo-Alvarez, and Fernando Cadaveira. "Vertical asymmetries in pre-attentive detection of changes in motion direction." International Journal of Psychophysiology 64, no. 2 (May 2007): 184–89. http://dx.doi.org/10.1016/j.ijpsycho.2007.02.001.
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Tang, Xiangbin, Aihua Yang, and Liangming Li. "Optimization of Nanofiber Wearable Heart Rate Sensor Module for Human Motion Detection." Computational and Mathematical Methods in Medicine 2022 (June 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/1747822.
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In order to further improve the detection performance of the wearable heart rate sensor for human physiological and biochemical signals and body kinematics performance, the wearable heart rate sensor module was optimized by using nanofibers. Nanoparticle-doped graphene films were prepared by adding nanoparticles to a graphene oxide solution. The prepared film was placed in toluene, and the nanoparticles were removed to complete the preparation of a graphene film with a porous microstructure. The graphene film and the conductive film together formed a wearable heart rate sensor module. The strain response test of the porous graphene film wearable heart rate sensor module verifies the validity of the research in this paper. The resistance change of the wearable heart rate sensor module based on the PGF-2 film is 8 to 16 times higher than that of the RGO film, and the sensitivity is better, proving that the sensor module designed by this method shows significant application potential in human motion detection.
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Li, Sheng, Huan Li, Yongcai Lu, Minhao Zhou, Sai Jiang, Xiaosong Du, and Chang Guo. "Advanced Textile-Based Wearable Biosensors for Healthcare Monitoring." Biosensors 13, no. 10 (September 27, 2023): 909. http://dx.doi.org/10.3390/bios13100909.
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With the innovation of wearable technology and the rapid development of biosensors, wearable biosensors based on flexible textile materials have become a hot topic. Such textile-based wearable biosensors promote the development of health monitoring, motion detection and medical management, and they have become an important support tool for human healthcare monitoring. Textile-based wearable biosensors not only non-invasively monitor various physiological indicators of the human body in real time, but they also provide accurate feedback of individual health information. This review examines the recent research progress of fabric-based wearable biosensors. Moreover, materials, detection principles and fabrication methods for textile-based wearable biosensors are introduced. In addition, the applications of biosensors in monitoring vital signs and detecting body fluids are also presented. Finally, we also discuss several challenges faced by textile-based wearable biosensors and the direction of future development.
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Koh, Junho, Junhyung Lee, Youngwoo Lee, Jaekyum Kim, and Jun Won Choi. "MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 1179–87. http://dx.doi.org/10.1609/aaai.v37i1.25200.
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Most scanning LiDAR sensors generate a sequence of point clouds in real-time. While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets. In this paper, we propose a novel 3D object detection architecture, which can encode LiDAR point cloud sequences acquired by multiple successive scans. The encoding process of the point cloud sequence is performed on two different time scales. We first design a short-term motion-aware voxel encoding that captures the short-term temporal changes of point clouds driven by the motion of objects in each voxel. We also propose long-term motion-guided bird’s eye view (BEV) feature enhancement that adaptively aligns and aggregates the BEV feature maps obtained by the short-term voxel encoding by utilizing the dynamic motion context inferred from the sequence of the feature maps. The experiments conducted on the public nuScenes benchmark demonstrate that the proposed 3D object detector offers significant improvements in performance compared to the baseline methods and that it sets a state-of-the-art performance for certain 3D object detection categories. Code is available at https://github.com/HYjhkoh/MGTANet.git.
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Lin, Hong Dun, Yen Shien Lee, Yu Jen Su, and Bor Nian Chuang. "Nanosecond Pulse Near-Field Sensing Based Non-Contact Physiological Signals Measurement." Advanced Materials Research 301-303 (July 2011): 1214–19. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1214.
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Clinically, basic physiological signal measurement, such as cardiovascular vibration and respiration motion detection, reveals important vital information of clinical diagnosis or personal health status evaluation. In routine, there are many preferable non-invasive methods, including electrocardiogram (ECG), pressure-sensitive transducers and applanation tonometry, to get insight of the sign of life. However, the operation of traditional monitors is relied on professionals’ experience, and also the sensing probes needed to contact to the user’s skin directly. The measurement procedure is easy to cause inconvenient and uncomfortable. To improve the issues of these measuring techniques, the non-contact and non-invasive measuring method will become an important innovation. In this paper, the novel nanosecond pulse near-field sensing (NPNS) based screening technology, which includes radio frequency (RF) pulse transmission and a flat antenna connected to transceiver of miniature radar, is proposed to monitor physical activity inside body. A dedicate analysis software built in Smartphone is also provided to calculate desired parameters, including heart rate and breath rate, for clinical or common personal applications. To evaluate the performance, the proposed method was applied on motion measurement at the body site of chest. As a result, the proposed method is validated to perform the capability of continuously long-term monitoring to reveal cardiovascular information in real-time.
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Chwalek, Patrick, David Ramsay, and Joseph A. Paradiso. "Captivates: A Smart Eyeglass Platform for Across-Context Physiological Measurements." GetMobile: Mobile Computing and Communications 27, no. 2 (August 3, 2023): 18–22. http://dx.doi.org/10.1145/3614214.3614220.
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We present Captivates, an open-source smartglasses system designed for long-term, in-the-wild psychophysiological monitoring at scale. Captivates integrate many underutilized physiological sensors in a streamlined package, including temple and nose temperature measurement, blink detection, head motion tracking, activity classification, 3D localization, and head pose estimation. Captivates was designed with an emphasis on (1) manufacturing and scalability, so we can easily support large-scale user studies for ourselves and offer the platform as a generalized tool for ambulatory psychophysiology research; (2) robustness and battery life, so long-term studies result in trustworthy data across an individual's entire day in natural environments without supervision or recharge; and (3) aesthetics and comfort, so people can wear them in their normal daily contexts without self-consciousness or changes in behavior.
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Li, Haoying, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, and Zhihai Xu. "Real-World Deep Local Motion Deblurring." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 1314–22. http://dx.doi.org/10.1609/aaai.v37i1.25215.
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Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods and our proposed local blur-aware techniques are effective.
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Markova, Valentina, Todor Ganchev, Silvia Filkova, and Miroslav Markov. "MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders." Algorithms 17, no. 5 (April 29, 2024): 187. http://dx.doi.org/10.3390/a17050187.
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Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals.
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Qian, Ning. "Computing Stereo Disparity and Motion with Known Binocular Cell Properties." Neural Computation 6, no. 3 (May 1994): 390–404. http://dx.doi.org/10.1162/neco.1994.6.3.390.
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Many models for stereo disparity computation have been proposed, but few can be said to be truly biological. There is also a rich literature devoted to physiological studies of stereopsis. Cells sensitive to binocular disparity have been found in the visual cortex, but it is not clear whether these cells could be used to compute disparity maps from stereograms. Here we propose a model for biological stereo vision based on known receptive field profiles of binocular cells in the visual cortex and provide the first demonstration that these cells could effectively solve random dot stereograms. Our model also allows a natural integration of stereo vision and motion detection. This may help explain the existence of units tuned to both disparity and motion in the visual cortex.
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Carras, Porto. "Detection of Fourier and non-Fourier motion in the human visual system." International Journal of Psychophysiology 18, no. 2 (November 1994): 114–15. http://dx.doi.org/10.1016/0167-8760(94)90355-7.
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Liu, Ruiqing, Juncai Zhu, and Xiaoping Rao. "Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network." Symmetry 14, no. 7 (June 29, 2022): 1340. http://dx.doi.org/10.3390/sym14071340.
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Murine behavior recognition is widely used in biology, neuroscience, pharmacology, and other aspects of research, and provides a basis for judging the psychological and physiological state of mice. To solve the problem whereby traditional behavior recognition methods only model behavioral changes in mice over time or space, we propose a symmetrical algorithm that can capture spatiotemporal information based on behavioral changes. The algorithm first uses the improved DeepLabCut keypoint detection algorithm to locate the nose, left ear, right ear, and tail root of the mouse, and then uses the ConvLSTM network to extract spatiotemporal information from the keypoint feature map sequence to classify five behaviors of mice: walking straight, resting, grooming, standing upright, and turning. We developed a murine keypoint detection and behavior recognition dataset, and experiments showed that the method achieved a percentage of correct keypoints (PCK) of 87±1% at three scales and against four backgrounds, while the classification accuracy for the five kinds of behaviors reached 93±1%. The proposed method is thus accurate for keypoint detection and behavior recognition, and is a useful tool for murine motion behavior recognition.
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Fan, Shaocan, and Zhenmiao Deng. "Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System." Sensors 24, no. 7 (March 23, 2024): 2058. http://dx.doi.org/10.3390/s24072058.
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An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.
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Tlemsani, Fatima Zohra, Hayriye Gidik, Elham Mohsenzadeh, and Daniel Dupont. "Textile Heat Flux Sensor Used in Stress Detection of Children with CP." Solid State Phenomena 333 (June 10, 2022): 153–60. http://dx.doi.org/10.4028/p-v03hy7.
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This work is part of the European project MOTION (Interreg 2 Seas Mers Zeeën), which aims to develop an exoskeleton for children with cerebral palsy (CP). The developed exoskeleton is equipped with a smart garment in order to detect the stress (e.g. physical, physiological) during the rehabilitation. Five different sensors, i.e. electrocardiogram (ECG), respiratory rate (RR), pressure, galvanic skin response (GSR) and textile heat fluxmeter (THF), are integrated into this smart garment for stress detection. This paper focuses on the development of the textile heat fluxmeter. Several researchers used heat fluxmeters in physiological studies to measure the body heat exchanges with the environment. However, the non-permeability of such fluxmeter gives inaccurate measurements in wet condition. Innovative flexible textile heat fluxmeter may detect, analyze, and monitor the heat and mass transfers with minimum disturbance due to its porosity. Moreover, it is desirable to have flexible sensors when they need to be in contact with the human body, in which the flexibility and non-irritability requirements are of utmost importance.
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Wen, Wen, and Fang Fang. "Flexible sensors in smart textiles and their applications." Wearable Technology 2, no. 2 (June 16, 2022): 83. http://dx.doi.org/10.54517/wt.v2i2.1651.
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<p>Sensors are the core part of intelligent smart textiles, and flexible sensors play an important role in wearable smart textiles because of their softness, bend ability and stretch ability, and excellent electrical properties. Based on the working principle of sensors, the research progress of flexible sensors for smart textiles in recent years is reviewed, and the sensing mechanism, sensing materials and application status of different sensors are introduced respectively; the main research directions of flexible sensors for smart textiles are summarized: physiological parameter detection, pressure detection and motion detection, and the applications of the three research directions are reviewed. On this basis, the problems of intelligent flexible sensors and their development prospects are pointed out.</p>
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Li, Shun-Xin, Hong Xia, Yi-Shi Xu, Chao Lv, Gong Wang, Yun-Zhi Dai, and Hong-Bo Sun. "Gold nanoparticle densely packed micro/nanowire-based pressure sensors for human motion monitoring and physiological signal detection." Nanoscale 11, no. 11 (2019): 4925–32. http://dx.doi.org/10.1039/c9nr00595a.
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Lomas, Dennis. "Representation of basic kinds: Not a case of evolutionary internalization of universal regularities." Behavioral and Brain Sciences 24, no. 4 (August 2001): 686–87. http://dx.doi.org/10.1017/s0140525x01500084.
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Shepard claims that “evolutionary internalization of universal regularities in the world” takes place. His position is interesting and seems plausible with regard to “default” motion detection and aspects of colour constancy which he addresses. However, his claim is not convincing with regard to object recognition. [Shepard]
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Feng, Huanhuan, Yaming Liu, Liang Feng, Limeng Zhan, Shuaishuai Meng, Hongjun Ji, Jiaheng Zhang, et al. "Additively Manufactured Flexible Electronics with Ultrabroad Range and High Sensitivity for Multiple Physiological Signals’ Detection." Research 2022 (August 8, 2022): 1–11. http://dx.doi.org/10.34133/2022/9871489.
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Flexible electronics can be seamlessly attached to human skin and used for various purposes, such as pulse monitoring, pressure measurement, tensile sensing, and motion detection. Despite their broad applications, most flexible electronics do not possess both high sensitivity and wide detection range simultaneously; their sensitivity drops rapidly when they are subjected to even just medium pressure. In this study, ultrabroad-range, high-sensitivity flexible electronics are fabricated through additive manufacturing to address this issue. The key to possess high sensitivity and a wide detection range simultaneously is to fabricate flexible electronics with large depth-width ratio circuit channels using the additive manufacturing inner-rinsing template method. These electronics exhibit an unprecedented high sensitivity of 320 kPa−1 over the whole detection range, which ranges from 0.3 to 30,000 Pa (five orders of magnitude). Their minimum detectable weight is 0.02 g (the weight of a fly), which is comparable with human skin. They can stretch to over 500% strain without breaking and show no tensile fatigue after 1000 repetitions of stretching to 100% strain. A highly sensitive and flexible electronic epidermal pulse monitor is fabricated to detect multiple physiological signals, such as pulse signal, breathing rhythm, and real-time beat-to-beat cuffless blood pressure. All of these signals can be obtained simultaneously for detailed health detection and monitoring. The fabrication method does not involve complex expensive equipment or complicated operational processes, so it is especially suitable for the fabrication of large-area, complex flexible electronics. We believe this approach will pave the way for the application of flexible electronics in biomedical detection and health monitoring.
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Saha, Amitave, Xiuzai Zhang, Bappi Chandra Saha, and Sunit Mistry. "Human Physiological Condition Monitoring System based on Microcontrollers." European Journal of Electrical Engineering and Computer Science 7, no. 3 (May 13, 2023): 6–16. http://dx.doi.org/10.24018/ejece.2023.7.3.513.
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With the rise in living standards, people are increasingly focusing on their health, and monitoring their physiological condition has become a popular trend. Sudden illnesses or hidden chronic conditions can cause abnormal fluctuations in temperature and heart rate, which can be difficult to detect without continuous monitoring. The main objective of this project is to develop a cost-effective and efficient system to monitor vital signs such as heart rate, body temperature, and steps taken, based on systematic experimental studies. The system utilizes a pulse sensor DS18B20, and ADXL345 to monitor the heart rate, body temperature, and motion status of the human body. The circuit and the HC-05 work together to transmit the detected data from the sensor to the MCU, which further transmits it to the LCD1602 or mobile phone interface for real-time display. The system provides a user-friendly interface and real-time monitoring, making it easier for individuals to keep track of their health status. This paper presents an innovative approach to human physiological condition monitoring using a microcontroller-based system, which has significant potential for improving healthcare by enabling early detection and prevention of medical conditions, ultimately leading to better quality of life.
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Parkinson, Rachel H., Sinan Zhang, and John R. Gray. "Neonicotinoid and sulfoximine pesticides differentially impair insect escape behavior and motion detection." Proceedings of the National Academy of Sciences 117, no. 10 (February 24, 2020): 5510–15. http://dx.doi.org/10.1073/pnas.1916432117.
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Insect nervous systems offer unique advantages for studying interactions between sensory systems and behavior, given their complexity with high tractability. By examining the neural coding of salient environmental stimuli and resulting behavioral output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behavior and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of nontarget species, such as bees, has motivated the development of new compounds that can potentially mitigate putative resistance in target species and declines of nontarget species. We used a neuroethologic approach, including behavioral assays and multineuronal recording techniques, to investigate effects of imidacloprid (IMD) and the novel insecticide sulfoxaflor (SFX) on visual motion-detection circuits and related escape behavior in the tractable locust system. Despite similar LD50 values, IMD and SFX evoked different behavioral and physiological effects. IMD significantly attenuated collision avoidance behaviors and impaired responses of neural populations, including decreases in spontaneous firing and neural habituation. In contrast, SFX displayed no effect at a comparable sublethal dose. These results show that neonics affect population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of SFX reflect a different mode of action than that of IMD. More broadly, we suggest that neuroethologic assays for comparative neurotoxicology are valuable tools for fully addressing current issues regarding the proximal effects of environmental toxicity in nontarget species.
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Ji, Xiaoqiang, Zhi Rao, Wei Zhang, Chang Liu, Zimo Wang, Shuo Zhang, Butian Zhang, Menglei Hu, Peyman Servati, and Xiao Xiao. "Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring." Micromachines 13, no. 11 (November 1, 2022): 1880. http://dx.doi.org/10.3390/mi13111880.
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With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84–85% with the lowest overfit problem, which proves its potential application in other related fields.
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Coelho, Carlos M., Janete Silva, Alfredo F. Pereira, Emanuel Sousa, Nattasuda Taephant, Kullaya Pisitsungkagarn, and Jorge A. Santos. "VISUAL-VESTIBULAR AND POSTURAL ANALYSIS OF MOTION SICKNESS, PANIC, AND ACROPHOBIA." Acta Neuropsychologica 15, no. 1 (March 12, 2017): 21–33. http://dx.doi.org/10.5604/12321966.1237325.
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Background: Visual-vestibular and postural interactions can act as cues that trigger motion sickness and can also have a role in some anxiety disorders. We explore a method to detect individual sensitivity to visual-vestibular unusual patterns, which can signal a vulnerability to develop motion sickness and possibly anxiety disorders such as a fear of heights and panic. Material/Methods: 65 undergraduate students were recruited for the purposes of this study as voluntary participants (44 females); average age 21.65 years (SD=2.84) with normal or corrected to normal vision, without vestibular or postural deficiencies. Panic was assessed with the Albany Panic and Phobia Questionnaire, Motion Sickness with the Motion Sickness Susceptibility Questionnaire and Acrophobia was assessed by means of the Acrophobia Questionnaire. The Sharpened Romberg Test was used to test participant’s postural balance. The Rod and Frame Test (RFT) measures the participant’s ability to align a rod to the vertical within a titled frame providing a measure of error in the perception of verticality by degrees. This test was changed to measure the error offered when a participant’s head was tilted, and to trace the error caused by manipulating the vestibular system input. Results: The main findings show only motion sickness to be correlated with significant errors while performing a visual-vestibular challenging situation, and fear of heights is the only anxiety disorder connected with postural stability, although all disorders (fear of heights, panic and motion sickness) are correlated between each other in the self-report questionnaires. Conclusions: All disorders are correlated to each other in the surveys, and might have some common visual-vestibular origin, in theory. The rod and frame test was exclusively correlated with motion sickness whereas the postural stability test only displayed sensibility to acrophobia. Panic disorder was correlated to neither the RFT nor the Romberg. Although this method was initially employed to increase sensibility in order to detect anxiety disorders, it ended up showing its value in the detection of motion sickness.
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Jing, Yu, Fugui Qi, Fang Yang, Yusen Cao, Mingming Zhu, Zhao Li, Tao Lei, Juanjuan Xia, Jianqi Wang, and Guohua Lu. "Respiration Detection of Ground Injured Human Target Using UWB Radar Mounted on a Hovering UAV." Drones 6, no. 9 (September 3, 2022): 235. http://dx.doi.org/10.3390/drones6090235.
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As an important and basic platform for remote life sensing, unmanned aerial vehicles (UAVs) may hide the vital signals of an injured human due to their own motion. In this work, a novel method to remove the platform motion and accurately extract human respiration is proposed. We utilized a hovering UAV as the platform of ultra-wideband (UWB) radar to capture human respiration. To remove interference from the moving UAV platform, we used the delay calculated by the correlation between each frame of UWB radar data in order to compensate for the range migration. Then, the echo signals from the human target were extracted as the observed multiple range channel signals. Owing to meeting the independent component analysis (ICA), we adopted ICA to estimate the signal of respiration. The results of respiration detection experiments conducted in two different outdoor scenarios show that our proposed method could accurately separate respiration of a ground human target without any additional sensor and prior knowledge; this physiological information will be essential for search and rescue (SAR) missions.
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Zhang, Ting, Julien Sarrazin, Guido Valerio, and Dan Istrate. "Estimation of Human Body Vital Signs Based on 60 GHz Doppler Radar Using a Bound-Constrained Optimization Algorithm." Sensors 18, no. 7 (July 12, 2018): 2254. http://dx.doi.org/10.3390/s18072254.
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In this study, a bound-constrained optimization algorithm is applied for estimating physiological data (pulse and breathing rate) of human body using 60 GHz Doppler radar, by detecting displacements induced by breathing and the heartbeat of a human subject. The influence of mutual phasing between the two movements is analyzed in a theoretical framework and the application of optimization algorithms is proved to be able to accurately detect both breathing and heartbeat rates, despite intermodulation effects between them. Different optimization procedures are compared and shown to be more robust to receiver noise and artifacts of random body motion than a direct spectrum analysis. In case of a large-scale constrained bound, a parallel optimization procedure executed in subranges is proposed to realize accurate detection in a reduced span of time.
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Pipke, Matt, Srilakshmi Alla, Jadranka Sekaric, Dylan Richards, Maged Gendy, Sabra Abbott, Daniela Grimaldi, Kathryn Reid, and Phyllis Zee. "0275 Deep Learning-based Sleep Detection using Torso Patch Vital Signs Improves Sleep-Wake Detection over Wrist Actigraphy." SLEEP 46, Supplement_1 (May 1, 2023): A122. http://dx.doi.org/10.1093/sleep/zsad077.0275.
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Abstract Introduction Motion-based wearable sensors, typically on wrist, have long been used for free-living sleep detection and quantification. However, it is hard to differentiate sleep from sedentary awake time by immobility alone. Vital signs, like heart rate and respiration rate, can greatly enhance determination of wake-sleep state, and are easily monitored with newer wearable sensors. Deep learning techniques are particularly adept at learning labeled physiological states. By combining movement plus vital signs in a deep neural network algorithm, improved sleep detection, fragmentation and sleep staging should be possible compared to activity alone. We report on performance of a deep learning sleep detection and REM/NREM algorithm providing 24-hour evaluation with high specificity using data from a torso-wearable patch sensor as compared to polysomnography (PSG). Methods Twenty-six healthy adults (mean age 53.7 years, 81% female) contributed 150 nights of PSG during laboratory visits, during which participants simultaneously wore a multi-day skin-adherent patch with continuous single-lead ECG and 3-axis accelerometer streams, as well as a wrist activity monitor. A pre-trained deep neural network algorithm generated epoch-level Wake/REM/NREM classification (Sleep equals REM plus NREM) using vital signs and movement derived from the patch sensor ECG and accelerometer waveforms and was then compared to expert human staging of PSGs. The wrist actigraphy sleep-wake determinations (Actiware) were also compared to PSG. Results Data includes 900 hours sleeping and 139 hours awake, of which 195 hours of sleep were in REM state. Using patch data, the deep neural net algorithm achieved 92% sensitivity and 85% specificity to detect sleep as compared to PSG; REM was detected with 85% sensitivity and 97% specificity. By comparison, the wrist motion-based algorithm only exhibited 33% specificity and 95% sensitivity, essentially overcalling immobile wake as sleep. Conclusion Sleep evaluation in free-living environments with wearable sensors can be greatly improved over conventional motion-based wrist sensors by leveraging continuous vital signs. Deep learning-trained neural network algorithms are particularly effective for use with such data, as demonstrated with this algorithm. Support (if any) R01 HL140580 and P01 AG011412
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Zareen, Farhath, Mohammed Elazab, Brett Hanzlicek, Adam Doelman, Dennis Bourbeau, Steve JA Majerus, Margot S. Damaser, and Robert Karam. "Optimization of activity-driven event detection for long-term ambulatory urodynamics." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 238, no. 6 (June 2024): 608–18. http://dx.doi.org/10.1177/09544119241264304.
Full textAbstract:
Lower urinary tract dysfunction (LUTD) is a debilitating condition that affects millions of individuals worldwide, greatly diminishing their quality of life. The use of wireless, catheter-free implantable devices for long-term ambulatory bladder monitoring, combined with a single-sensor system capable of detecting various bladder events, has the potential to significantly enhance the diagnosis and treatment of LUTD. However, these systems produce large amounts of bladder data that may contain physiological noise in the pressure signals caused by motion artifacts and sudden movements, such as coughing or laughing, potentially leading to false positives during bladder event classification and inaccurate diagnosis/treatment. Integration of activity recognition (AR) can improve classification accuracy, provide context regarding patient activity, and detect motion artifacts by identifying contractions that may result from patient movement. This work investigates the utility of including data from inertial measurement units (IMUs) in the classification pipeline, and considers various digital signal processing (DSP) and machine learning (ML) techniques for optimization and activity classification. In a case study, we analyze simultaneous bladder pressure and IMU data collected from an ambulating female Yucatan minipig. We identified 10 important, yet relatively inexpensive to compute signal features, with which we achieve an average 91.5% activity classification accuracy. Moreover, when classified activities are included in the bladder event analysis pipeline, we observe an improvement in classification accuracy, from 81% to 89.0%. These results suggest that certain IMU features can improve bladder event classification accuracy with low computational overhead. Clinical Relevance: This work establishes that activity recognition may be used in conjunction with single-channel bladder event detection systems to distinguish between contractions and motion artifacts for reducing the incorrect classification of bladder events. This is relevant for emerging sensors that measure intravesical pressure alone or for data analysis of bladder pressure in ambulatory subjects that contain significant abdominal pressure artifacts.