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Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates "movie" information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning., Competing Interests: Competing interests statement:The authors have submitted a provisional patent characterizing motion detecting neurons.

A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa -1 within the low-pressure range of 0-390 Pa and 0.012 kPa -1 within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.

Wearable electronic devices with multifunctions such as flexible, integrated, and self-powered play a crucial role in the fields of health monitoring, motion monitoring, and human-computer interaction. However, their core basic components, flexible pressure sensors, face challenges including poor long-term stability and insufficient real-time sensing accuracy. In order to solve the challenges of long-term, stable, and accurate sensing of the sensor, this paper prepares polydimethylsiloxane (SHPDMS) with intrinsic self-healing property and designs a high-sensitivity self-healing capacitive flexible pressure sensor with dual microstructures (grating microstructured electrodes and microporous dielectric layer) as the substrate based on SHPDMS. Specifically speaking, the self-healing of the sensor under mild conditions was realized by introducing reversible imine bonds with low bonding energy into the polydimethylsiloxane (PDMS) flexible substrate, which solved the problem of the material's long-term service durability. A grating-like microstructure was introduced into the flexible electrode by using a spotted bamboo taro leaf as a template, and a dual microstructure sensor was constructed by combining it with a microporous dielectric layer doped with single-walled carbon nanotubes. This way reduces the elastic modulus of the dielectric layer, improves the dielectric constant of the sensor under loading, and thus significantly improves the sensor's sensitivity and extends the measurement accuracy in a low-stress range. The prepared self-healing flexible sensor achieves a sensitivity of 3.6 kPa -1 , a minimum detection limit of 5 Pa, a response recovery time of less than 80 ms, and stability over 5000 cycles, which exceeds most previously reported silicone rubber-based capacitive flexible sensors.

Biological vision is one of the most important parts of the human perception system. However, emulating biological visuals is challenging because it requires complementary photoexcitation and photoinhibition. Here, the study presents a bidirectional photovoltage-driven neuromorphic visual sensor (BPNVS) that is constructed by monolithically integrating two perovskite solar cells (PSCs) with dual-gate ion-gel-gated oxide transistors. PSCs act as photoreceptors, converting external visual stimuli into electrical signals, whereas oxide transistors generate neuromorphic signal outputs that can be adjusted to produce positive and negative photoresponses. This device mimics the human vision system's ability to recognize colored and color-mixed patterns. The device achieves a static color recognition accuracy of 96% by utilizing the reservoir computing system for feature extraction. The BPNVS mem-reservoir chip is also proposed for handing object movement and dynamic color recognition. This work is a significant step forward in neuromorphic sensing and complex pattern recognition., (© 2024 Wiley‐VCH GmbH.)

Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model's performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets ( J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated ( J test = 0.941), followed by complete ( J test = 0.872), and then half movements ( J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms.

Significance: Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data., Aim: We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination., Approach: Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach., Results: When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results., Conclusions: This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present., (© 2024 The Authors.)

Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI., Methods: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted., Results: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 ± $$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ± $$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning., Conclusions: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human-machine interaction interfaces and wearable devices. In this study, a TE hydrogel with high toughness and temperature responsiveness was created using the Hofmeister effect and TE current effect, achieved through the cross-linking of PVA/PAA/carboxymethyl cellulose triple networks. The Hofmeister effect, facilitated by Na + and SO 4 2- ions coordination, notably increased the hydrogel's tensile strength (800 kPa). Introduction of Fe 2+ /Fe 3+ as redox pairs conferred a high Seebeck coefficient (2.3 mV K -1 ), thereby enhancing temperature responsiveness. Using this dual-responsive sensor, successful demonstration of a feedback mechanism combining deep learning with a robotic hand was accomplished (with a recognition accuracy of 95.30%), alongside temperature warnings at various levels. It is expected to replace manual work through the control of the manipulator in some high-temperature and high-risk scenarios, thereby improving the safety factor, underscoring the vast potential of TE hydrogel sensors in motion monitoring and human-machine interaction applications.

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%.

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.

Background: Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts., Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion., Method: In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion., Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images., Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact., (© 2024 American Association of Physicists in Medicine.)

Herein, we report a dual-functional flexible sensor (DFFS) using a magnetic conductive polymer composed of nickel (Ni), carbon black (CB), and polydimethylsiloxane (PDMS). The material selection for the DFFS utilizes the excellent elasticity of the PDMS matrix and the synergistic interaction between Ni and CB. The DFFS has a wide strain range of 0-170%, a high sensitivity of 74.13 (140-170%), and a low detection limit of 0.3% strain. The DFFS based on superior performance can accurately detect microstrain/microvibration, oncoming/contacting objects, and bicycle riding speed. Additionally, the DFFS can be used for comprehensive monitoring of human movements. Therefore, the DFFS of this work shows significant value for implementation in intelligent wearable devices and noncontact intelligent control.

Accurately capturing human movements is a crucial element of health status monitoring and a necessary precondition for realizing future virtual reality/augmented reality applications. Flexible motion sensors with exceptional sensitivity are capable of detecting physical activities by converting them into resistance fluctuations. Silver nanowires (AgNWs) have become a preferred choice for the development of various types of sensors due to their outstanding electrical conductivity, transparency, and flexibility within polymer composites. Herein, we present the design and fabrication of a flexible strain sensor based on silver nanowires. Suitable substrate materials were selected, and the sensor's sensitivity and fatigue properties were characterized and tested, with the sensor maintaining reliability after 5000 deformation cycles. Different sensors were prepared by controlling the concentration of silver nanowires to achieve the collection of motion signals from various parts of the human body. Additionally, we explored potential applications of these sensors in fields such as health monitoring and virtual reality. In summary, this work integrated the acquisition of different human motion signals, demonstrating great potential for future multifunctional wearable electronic devices.

Objective: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning., Methods: We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances., Results: The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods., Conclusion: Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application., (© 2024. The Author(s).)

Numerous studies have shown that prior visual experiences play an important role in sensory processing and adapting behavior in a dynamic environment. A repeated and passive presentation of visual stimulus is one of the simplest procedures to manipulate acquired experiences. Using this approach, we aimed to investigate exposure-based visual learning of aging zebrafish and how cholinergic intervention is involved in exposure-induced changes. Our measurements included younger and older wild-type zebrafish and ache sb55/+ mutants with decreased acetylcholinesterase activity. We examined both within-session and across-day changes in the zebrafish optomotor responses to repeated and passive exposure to visual motion. Our findings revealed short-term (within-session) changes in the magnitude of optomotor response (i.e., the amount of position shift by fish as a response to visual motion) rather than long-term and persistent effects across days. Moreover, the observed short-term changes were age- and genotype-dependent. Compared to the initial presentations of motion within a session, the magnitude of optomotor response to terminal presentations decreased in the older zebrafish. There was a similar robust decrease specific to ache sb55/+ mutants. Taken together, these results point to short-term (within-session) alterations in the motion detection of adult zebrafish and suggest differential effects of neural aging and cholinergic system on the observed changes. These findings further provide important insights into adult zebrafish optomotor response to visual motion and contribute to understanding this reflexive behavior in the short- and long-term stimulation profiles., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This paper is on the autonomous detection of humans in off-limits mountains. In off-limits mountains, a human rarely exists, thus human detection is an extremely rare event. Due to the advances in artificial intelligence, object detection-classification algorithms based on a Convolution Neural Network (CNN) can be used for this application. However, considering off-limits mountains, there should be no person in general. Thus, it is not desirable to run object detection-classification algorithms continuously, since they are computationally heavy. This paper addresses a time-efficient human detector system, based on both motion detection and object classification. The proposed scheme is to run a motion detection algorithm from time to time. In the camera image, we define a feasible human space where a human can appear. Once motion is detected inside the feasible human space, one enables the object classification, only inside the bounding box where motion is detected. Since motion detection inside the feasible human space runs much faster than an object detection-classification method, the proposed approach is suitable for real-time human detection with low computational loads. As far as we know, no paper in the literature used the feasible human space, as in our paper. The outperformance of our human detector system is verified by comparing it with other state-of-the-art object detection-classification algorithms (HOG detector, YOLOv7 and YOLOv7-tiny) under experiments. This paper demonstrates that the accuracy of the proposed human detector system is comparable to other state-of-the-art algorithms, while outperforming in computational speed. Our experiments show that in environments with no humans, the proposed human detector runs 62 times faster than YOLOv7 method, while showing comparable accuracy.

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Yue and Hu.)

Ionic polymer sensors (IPSs) have broad application prospects in health monitoring, environmental perception, and human-computer interaction. The performance of IPSs with chemically prepared electrodes is generally superior to that with physically prepared electrodes due to the area difference of the electric double layer (EDL), but the effects of the electrode characteristics prepared by chemical methods on the performance of IPSs have not been revealed. Therefore, in this paper, we studied the impact of the characteristics of chemically prepared electrodes on the performance of IPSs and realized the performance optimization of IPSs through electrode characteristic regulation. By controlling the matrix surface roughening, immersion reduction plating (IRP) cycles, and electroplating (EP) time, the sensing performances of IPS samples with different electrode interface roughnesses, electrode penetration depths, and surface resistances were investigated, respectively. The experimental results indicated that the response voltage of the IPS can be improved by increasing the electrode interface roughness and the electrode penetration depth and reducing the surface resistance. In addition, we have proven that the sensing performance of the IPS is determined by its intrinsic capacitance characteristics. Through coupling electrode characteristic regulations such as roughening and increasing IRP cycles and EP time, a high-performance IPS was obtained, and its response amplitude was improved by 237.8%. The obtained high-performance sensor has been applied in human motion detection, which has good potential to develop wearable devices with high stability for physiological activity monitoring.

Fiber-based flexible sensors have promising application potential in human motion and healthcare monitoring, owing to their merits of being lightweight, flexible, and easy to process. Now, high-performance elastic fiber-based strain sensors with high sensitivity, a large working range, and excellent durability are in great demand. Herein, we have easily and quickly prepared a highly sensitive and durable fiber-based strain sensor by dip coating a highly stretchable polyurethane (PU) elastic fiber in an MXene/waterborne polyurethane (WPU) dispersion solution. Benefiting from the electrostatic repulsion force between the negatively charged WPU and MXene sheets in the mixed solution, very homogeneous and stable MXene/WPU dispersion was successfully obtained, and the interconnected conducting networks were correspondingly formed in a coated MXene/WPU shell layer, which makes the as-prepared strain sensor exhibit a gauge factor of over 960, a large sensing range of over 90%, and a detection limit as low as 0.5% strain. As elastic fiber and mixed solution have the same polymer constitute, and tight bonding of the MXene/WPU conductive composite on PU fibers was achieved, enabling the as-prepared strain sensor to endure over 2500 stretching-releasing cycles and thus show good durability. Full-scale human motion detection was also performed by the strain sensor, and a body posture monitoring, analysis, and correction prototype system were developed via embedding the fiber-based strain sensors into sweaters, strongly indicating great application prospects in exercise, sports, and healthcare.

The ability to detect and assess world-relative object-motion is a critical computation performed by the visual system. This computation, however, is greatly complicated by the observer's movements, which generate a global pattern of motion on the observer's retina. How the visual system implements this computation is poorly understood. Since we are potentially able to detect a moving object if its motion differs in velocity (or direction) from the expected optic flow generated by our own motion, here we manipulated the relative motion velocity between the observer and the object within a stationary scene as a strategy to test how the brain accomplishes object-motion detection. Specifically, we tested the neural sensitivity of brain regions that are known to respond to egomotion-compatible visual motion (i.e., egomotion areas: cingulate sulcus visual area, posterior cingulate sulcus area, posterior insular cortex [PIC], V6+, V3A, IPSmot/VIP, and MT+) to a combination of different velocities of visually induced translational self- and object-motion within a virtual scene while participants were instructed to detect object-motion. To this aim, we combined individual surface-based brain mapping, task-evoked activity by functional magnetic resonance imaging, and parametric and representational similarity analyses. We found that all the egomotion regions (except area PIC) responded to all the possible combinations of self- and object-motion and were modulated by the self-motion velocity. Interestingly, we found that, among all the egomotion areas, only MT+, V6+, and V3A were further modulated by object-motion velocities, hence reflecting their possible role in discriminating between distinct velocities of self- and object-motion. We suggest that these egomotion regions may be involved in the complex computation required for detecting scene-relative object-motion during self-motion., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Purpose: To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI., Methods: A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets., Results: Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age., Conclusions: Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision., (© 2023. The Author(s).)

The advancement of smart textiles has resulted in significant development in wearable textile sensors and offers novel interfaces to sense physical movements in daily life. Knitting, as a traditional textile fabrication method, is being used in promising ways to realize fully seamless fabrication and unobtrusive sensing in wearable textile applications. However, current flat-knitted sensors can sense strain only in the horizontal plane. This research presents a novel fully machine-knitted spacer piezoresistive sensor structure with a three-directional sensing ability that can detect both the pressure in the vertical direction and the strain in the warp/weft direction. Besides, it can sense the pressure under 1 kPa, which is critical in comfortable on-body interaction, one-piece integration, and wearable applications. Three sizes spacer-knitted sensors are evaluated in terms of their mechanical performance, stability cycles, and reaction to external factors such as sweat, laundering, etc. Then, the effect of material choice on sensor performance is evaluated and the rationale behind the use of different materials is summarized. Specifically, this research presents a detailed evaluation of the applications with both a single sensor and multiple sensor arrays for fine and gross motion sensing in several scenarios. The testing results demonstrate a fully machine-knitted piezoresistive sensor that can detect multidirectional motions (vertical, warp, and weft directions). In addition, this knitted sensor is scalable and can be facilely and seamlessly integrated into any garment piece. This universal knitted sensor structure could be made with a wide variety of materials for high sensitivity for multidirectional strain/pressure sensing, making it a high-compatibility sensor structure for wearable applications.

Organic retinomorphic sensors offer the advantage of in-sensor processing to filter out redundant static backgrounds and are well suited for motion detection. To improve this promising structure, here, the key role of interfacial energetics in promoting charge accumulation to raise the inherent photoresponse of the light-sensitive capacitor is studied. Specifically, incorporating appropriate interfacial layers around the photoactive layer is crucial to extend the carrier lifetime, as confirmed by intensity-modulated photovoltage spectroscopy. Compared to its photodiode counterpart, the retinomorphic sensor shows better detectivity and response speed due to the additional insulating layer, which reduces the dark current and the RC time constant. Lastly, three retinomorphic sensors are integrated into a line array to demonstrate the detection of movement speed and direction, showing the potential of retinomorphic designs for efficient motion tracking., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Various aspects of visual functioning, including motion perception, change with age. Yet, there is a lack of comprehensive understanding of age-related alterations at different stages of motion processing and in each motion system. To understand the effects of aging on second-order motion processing, we investigated optomotor responses (OMR) in younger and older wild-type (AB-strain) and acetylcholinesterase (ache sb55/+ ) mutant zebrafish. The mutant fish with decreased levels of acetylcholinesterase have been shown to have delayed age-related cognitive decline. Compared to previous results on first-order motion, we found distinct changes in OMR to second-order motion. The polarity of OMR was dependent on age, such that second-order stimulation led to mainly negative OMR in the younger group while older zebrafish had positive responses. Hence, these findings revealed an overall aging effect on the detection of second-order motion. Moreover, neither the genotype of zebrafish nor the spatial frequency of motion significantly changed the response magnitude. Our findings support the view that age-related changes in motion detection depend on the activated motion system., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Neuromorphic vision based on photonic synapses has the ability to mimic sensitivity, adaptivity, and sophistication of bio-visual systems. Significant advances in artificial photosynapses are achieved recently. However, conventional photosyanptic devices normally employ opaque metal conductors and vertical device configuration, performing a limited hemispherical field of view. Here, a transparent planar photonic synapse (TPPS) is presented that offers dual-side photosensitive capability for nearly panoramic neuromorphic vision. The TPPS consisting of all two dimensional (2D) carbon-based derivatives exhibits ultra-broadband photodetecting (365-970 nm) and ≈360° omnidirectional viewing angle. With its intrinsic persistent photoconductivity effect, the detector possesses bio-synaptic behaviors such as short/long-term memory, experience learning, light adaptation, and a 171% pair-pulse-facilitation index, enabling the synapse array to achieve image recognition enhancement (92%) and moving object detection., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Biomimetic visual sensors utilizing bacteriorhodopsin (bR) were fabricated by using an inkjet method. The inkjet printer facilitated the jetting of the bR suspension, allowing for the deposition of bR films. The resulting inkjet-printed bR film exhibited time-differential photocurrent response characteristics similar to those of a dip-coated bR film. By adjusting the number of printed bR film layers, the intensity of the photocurrent could be easily controlled. Moreover, the inkjet printing technique enabled unconstrained patterning, facilitating the design of various visual information processing functions, such as visual filters. In this study, we successfully fabricated two visual filters, namely, a two-dimensional Difference of Gaussian (DOG) filter and a Gabor filter. The printed DOG filter demonstrated edge detection capabilities corresponding to contour recognition in visual receptive fields. On the other hand, the printed Gabor filter proved effective in detecting objects of specific sizes as well as their motion and orientation. The integration of bR and the inkjet method holds significant potential for the widespread implementation of highly functional biomaterial-based visual sensors. These sensors have the capability to provide real-time visual information while operating in an energy-efficient manner.

At present, wearable flexible pressure sensors have broad application prospects in fields such as motion monitoring and information transmission. However, it is still a challenge to design flexible pressure sensors with high sensitivity over a large sensing range and simple fabrication. Here, we use a simple "dipping-drying" method to fabricate a fabric-based flexible pressure sensor by coupling silver nanowires (AgNWs) with Ti 3 C 2 -MXene. The interaction between MXene and AgNWs helps realize a dual-layer sensing network, achieving good synergistic effects between pressure sensitivity and sensing range. The effects of the material combination and dip-coating sequence on the sensor's performance are systematically studied. The results show that the sensor was impregnated sequentially with AgNWs solution, and the MXene solution has the highest sensitivity (0.168 kPa -1 ) over a wide range (190 kPa). Meanwhile, it has the advantages of low response hysteresis and detection limit, as well as good linearity and durability. We further demonstrate the application of this sensor in human physiological signal monitoring and motion pattern recognition. It can also encrypt and transmit information according to different pressing states. In addition, the proposed pressure sensor array exhibits spatial resolution detection capabilities, laying the foundation for applications in the fields of motion monitoring and human-computer interaction.

Purpose: This study aimed to develop and validate a deep learning-based method that detects inter-breath-hold motion from an estimated cardiac long axis image reconstructed from a stack of short axis cardiac cine images., Methods: Cardiac cine magnetic resonance image data from all short axis slices and 2-/3-/4-chamber long axis slices were considered for the study. Data from 740 subjects were used for model development, and data from 491 subjects were used for testing. The method utilized the slice orientation information to calculate the intersection line of a short axis plane and a long axis plane. An estimated long axis image is shown along with a long axis image as a motion-free reference image, which enables visual assessment of the inter-breath-hold motion from the estimated long axis image. The estimated long axis image was labeled as either a motion-corrupted or a motion-free image. Deep convolutional neural network (CNN) models were developed and validated using the labeled data., Results: The method was fully automatic in obtaining long axis images reformatted from a 3D stack of short axis slices and predicting the presence/absence of inter-breath-hold motion. The deep CNN model with EfficientNet-B0 as a feature extractor was effective at motion detection with an area under the receiver operating characteristic (AUC) curve of 0.87 for the testing data., Conclusion: The proposed method can automatically assess inter-breath-hold motion in a stack of cardiac cine short axis slices. The method can help prospectively reacquire problematic short axis slices or retrospectively correct motion., (© 2023. BioMed Central Ltd., part of Springer Nature.)

This paper describes the preliminary results of measuring the impact of human body movements on plants. The scope of this project is to investigate if a plant perceives human activity in its vicinity. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight-week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox. We compared this experimental group of vegetables to a control group of vegetables whose voltage differential was also measured while not being exposed to eurythmy. We placed a plant spikerbox connected to lettuce or beans in the vegetable plot while the eurythmist was performing their gestures about 2 m away; a second spikerbox was connected to a control plant 20 m away. Using t -tests, we found a clear difference between the experimental and the control group, which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.

Due to the rapid development of the IoT ecosystem, a large amount of IoT data has been generated. However, in the current development process of the Internet of Things, how to ensure data security is a key issue. Traditional IoT security protection is relatively basic. With the increase of data size, these facilities need to be further improved in terms of computing, information security, storage information, and stability. However, traditional security and privacy protection methods are often insufficient for large topology structures. Blockchain is based on distributed networks, characterized by monotonous and decentralized data. Blockchain also provides a new approach to encrypting IoT security information. This article introduces blockchain technology into the Internet of Things, which can prevent reliance on centralized servers and effectively protect internet data and information security. Then this article also analyzed the motion recognition and detection of community sports, as well as the widespread application of computer vision and machine learning technologies in various fields such as computer mining and data security. Motion detection technology is very important in the field of computer vision and has applications in various real-world scenarios. This article studies new ideas for information encryption in the computer Internet of Things, and also analyzes machine learning and motion detection systems and applies them to community sports, improving the development of community sports., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

How neurons detect the direction of motion is a prime example of neural computation: Motion vision is found in the visual systems of virtually all sighted animals, it is important for survival, and it requires interesting computations with well-defined linear and nonlinear processing steps-yet the whole process is of moderate complexity. The genetic methods available in the fruit fly Drosophila and the charting of a connectome of its visual system have led to rapid progress and unprecedented detail in our understanding of how neurons compute the direction of motion in this organism. The picture that emerged incorporates not only the identity, morphology, and synaptic connectivity of each neuron involved but also its neurotransmitters, its receptors, and their subcellular localization. Together with the neurons' membrane potential responses to visual stimulation, this information provides the basis for a biophysically realistic model of the circuit that computes the direction of visual motion.

The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed., (© 2023. The Author(s).)

The wearable electronic technique is increasingly becoming an effective approach to overcoming the communication obstacles between signers and non-signers. However, the efficacy of conducting hydrogels currently proposed as flexible sensor devices is hindered by their poor processability and matrix mismatch, which frequently results in adhesion failure at the combined interfaces and deterioration of mechanical and electrochemical performance. Herein, we propose a hydrogel composed of a rigid matrix in which the hydrophobic and aggregated polyaniline was homogeneously embedded, while quaternate-functionalized nucleobase moieties endowed the flexible network with adhesiveness. Accordingly, the resulting hydrogel with chitosan- graft- polyaniline (chi- g -PANI) copolymers exhibited a promising conductivity (4.8 S·m -1 ) because of the uniformly dispersed polyaniline components and a high strain strength (0.84 MPa) because of the chain entanglement of chitosan after soaking. In addition, the modified adenine molecules not only realized synchronization in improving the stretchability (up to 1303%) and exhibiting a skin-like elastic modulus (≈184 kPa), but also provided a durable interfacial contact with various materials. The hydrogel was further fabricated into a strain-monitoring sensor for information encryption and sign language transmission based on its sensing stability and strain sensitivity of up to 2.77. The developed wearable sign language interpreting system provides an innovative strategy to assist auditory or speech-impaired people in communicating with non-signers using visual-gestural patterns including body movements and facial expressions.

An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor's taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor's categorization enables us to compare the effectiveness of each sensor's system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

The measurement of in-plane motion in microelectromechanical systems (MEMS) is a challenge for existing measurement techniques due to the small size of the moving devices and the low amplitude of motion. This paper studied the possibility of using images obtained using a scanning electron microscope (SEM) together with existing motion detection algorithms to characterize the motion of MEMS. SEM imaging has previously been used to detect motion in MEMS device. However, the differences in how SEM imaging and optical imaging capture motion, together with possible interference caused by electrical actuation, create doubts about how accurately motion could be detected in a SEM. In this work, it is shown that existing motion detection algorithms can be used to detect movement with an amplitude of 69 nm. In addition, the properties of SEM images, such as bright edges, complement these algorithms. Electrical actuation was found to cause error in the measurement, however, the error was limited to regions that were electrically connected to the actuating probes and minimal error could be detected in regions that were electrically insulated from the probes. These results show that an SEM is a powerful tool for characterizing low amplitude motion and electrical contacts in MEMS and allow for the detection of motion under 100 nm in amplitude.

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1., (© 2022 International Society for Magnetic Resonance in Medicine.)

Neurodegenerative diseases such as Parkinson's disease affect motor symptoms with abnormally increased or reduced movements. Symptoms such as tremor and hand movement disorders can be subtle and vary daily such that the actual condition of the disease may not fully present in clinical sessions. Health examination and monitoring, if available in the living space, can capture comprehensive and quantitative information about a patient's motor symptoms, allowing physicians to make a precise diagnosis and devise a more personalized treatment. WiFi-based sensing is a potential solution for passively detecting human motion in a contactless way that collects no personally identifiable information. This study proposed an approach for human micromotion detection using the WiFi channel state information, which can be realized in a regular-sized room for home health monitoring and examination. Three types of motion were tested to evaluate the proposed method in quantifying micromotion using single and multiple WiFi links. The results show that micromotion could be captured at all distributed locations in the experimental environment (4.2 m × 7.9 m). Our computer algorithm computed the frequency and duration of simulated hand tremors with an average accuracy of 90.9% (single WiFi link)-95.7% (multiple WiFi links).

Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement of fish to facilitate human interaction. This study proposed an interactive robot, a robotic fish, to alter fish swarm behaviors by performing an effective, unobstructed, yet necessary, defined set of actions to enhance human interaction. The approach incorporated a minimalistic but futuristic physical design of the robotic fish with cameras and infrared (IR) sensors, and developed a fish-detecting and swarm pattern-recognizing algorithm. The fish-detecting algorithm was implemented using background subtraction and moving average algorithms with an accuracy of 78%, while the swarm pattern detection implemented with a Convolutional Neural Network (CNN) resulted in a 77.32% accuracy rate. By effectively controlling the behavior and swimming patterns of fish through the smooth movements of the robotic fish, we evaluated the success through repeated trials. Feedback from a randomly selected unbiased group of subjects revealed that the robotic fish improved human interaction with fish by using the proposed set of maneuvers and behavior.

Flexible sensors have attracted increasing attention owing to their important applications in human activity monitoring, medical diagnosis, and human-machine interaction. However, the rational design of low-cost sensors with desirable properties (e.g., high sensitivity and excellent stability) and extended applications is still a great challenge. Herein, a simple and cost-effective strategy is reported by immersing polyurethane (PU) sponge in graphene oxide solution followed by in situ chemical reduction to construct a reduced graphene oxide (RGO)-wrapped PU sponge sensor. Ascribed to the excellent compressive resilience of PU sponge and an electrically conductive RGO layer, the constructed flexible sensor exhibits satisfactory sensing performance with high sensitivity (17.65 kPa -1 ) in a low-load range (0-3.2 kPa), a wide compression strain range (0-80%), and reliable stability (8000 cycles). In addition, these sensors can be successfully applied to monitor human movements and identify the weight of objects. Through the use of a sensor array integrated with a signal acquisition circuit, the reasonably designed sensors can realize tactile feedback via mapping real-time spatial distribution of pressure in complicated tasks and show potential applications in flexible electronic pianos, electronic skin, and remote real-time control of home electronics.

Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360° horizontal and 90° vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved.

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings' overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO 2 ) of the investigated apartment building, as well as users' habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models' validity for further studies.

Although fiber-based flexible piezoresistive pressure sensors have received extensive attention because of their simple fabrication and easy integration, the common practice of using a single material as the sensing layer often leads to unsatisfactory sensitivity and a limited sensing range. Herein, we exploit the combination of reduced graphene oxide (rGO) and two-dimensional transition-metal carbides and nitrides (MXene), use a polyester filament (PET) as the fiber matrix, and fabricate an MX/rGO PET-based flexible pressure sensor using the "dipping-drying" method. A systematic study is conducted concerning the effect of the dip-coating sequence and material combination on the sensor's resistance and sensitivity, which reveals that MX/rGO PET has the smallest resistance and the highest sensitivity (1.24 kPa -1 ). A series of tests are conducted to evaluate the pressure sensing characteristics of the MX/rGO PET-based pressure sensor, confirming its good linearity, fast response speed, low detection limit, and stable performance. In addition, the sensor has been successfully used to monitor various human joint activities and physiological signals such as breathing, demonstrating great application potential in the field of personal health care. To further enhance the practical utility, an APP has been designed to analyze and display the collected signals, and the constructed sensor network also provides an ingenious method for information encryption and transmission via pressure sensing. In all, the MX/rGO PET-based pressure sensor proposed in this work is expected to provide a competitive scheme for wearable flexible electronic devices in information transmission and human-computer interaction in the future.

Background: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders., Objective: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger., Methods: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model)., Results: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74., Conclusions: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients., (©Takafumi Koyama, Ryota Matsui, Akiko Yamamoto, Eriku Yamada, Mio Norose, Takuya Ibara, Hidetoshi Kaburagi, Akimoto Nimura, Yuta Sugiura, Hideo Saito, Atsushi Okawa, Koji Fujita. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 03.10.2022.)

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies ( 18 F-FDG, N = 15; 11 C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner ( 18 F-FDG, N = 10; 11 C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18 F-FDG (-3.4 ± 1.7% for 11 C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for 11 C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for 18 F-FDG (-0.4 ± 3.2% for 11 C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner., Competing Interests: Declaration of Competing Interest The other authors declare that they have no conflict of interest., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance., (© 2022 Institute of Physics and Engineering in Medicine.)

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.

Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases-IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.