Motion Detection Research Articles

Motion, path, and manner Revisited: Perceptual vs. Conceptual Motion

ABSTRACT This article aims at contributing to the theoretical tools and metalanguage used for comparing how motion, and, specifically, the enterprise of moving from one place (Loc1) to another (Loc2), is expressed across different languages. The point of departure is the well-established typological distinction between manner (or satellite-framed) languages and path (or verb-framed) languages. Several researchers have emphasised the need for (a) a more consistent theoretical basis for distinguishing between linguistic expressions lexicalising motion in a broad sense from such that incorporate the semantics of going somewhere, i.e. from Loc1 to Loc2; and (b) a further specification and differentiation of the intuitively attractive but vaguely defined variables of manner and path. The suggested approach addresses these issues in combination by incorporating key insights gained on pre-linguistic visual cognition. That includes a distinction between perceptual and conceptual motion with implications also for how the corresponding universals may be differently conveyed in different languages. The primary aim is not to present any novel empirical observations or principles of categorisation of typological trends within and across particular languages. Instead, the article brings to fore certain insights on pre-linguistic motion detection which offer new perspectives also on how the outcome can be encoded linguistically.

Bioinspired Conductivity-Enhanced, Self-Healing, and Renewable Silk Fibroin Hydrogel for Wearable Sensors with High Sensitivity.

The development of silk fibroin-based hydrogels with excellent biocompatibility, aqueous processability, and facile controllability in structure is indeed an exciting advancement for biological research and strain sensor applications. However, silk fibroin-based hydrogel strain sensors that combine high conductivity, high stretchability, reusability, and high selectivity are still desired. Herein, we report a simple method for preparing double-network hydrogels including silk fibroin and poly(acrylic acid) sodium-polyacrylate (PAA-PAAS) networks. The conformation and aggregate of silk fibroin could be facilely tuned by both ions and pH resulting from the PAA-PAAS network. The optimized hydrogel exhibits intriguing properties, such as high conductivity (3.67 S/m) and transparency, high stretchability (1186%) with a tensile strength of 110 kPa, good adhesion properties, reversible compression, self-healing, and high sensitivity (GF = 10.71). This hydrogel strain sensor can detect large-scale and small human movements in real time, such as limb movements, heartbeats, and pulse. Additionally, its ability to adsorb water and recover effectiveness after losing water from air with 90% humidity along with the capability for low-temperature motion detection facilitated by ethylene glycol further enhance its practical utility. This work offers a novel and simple approach to design flexible bionic strain sensors.

Low-cost physiology and behavioral monitor for intravital imaging in small mammals.

Functional brain imaging experiments in awake animals require meticulous monitoring of animal behavior to screen for spontaneous behavioral events. Although these events occur naturally, they can alter cell signaling and hemodynamic activity in the brain and confound functional brain imaging measurements. We developed a centralized, user-friendly, and stand-alone platform that includes an animal fixation frame, compact peripheral sensors, and a portable data acquisition system. The affordable, integrated platform can benefit imaging experiments by monitoring animal behavior for motion detection and alertness levels as complementary readouts for brain activity measurements. A custom acquisition system was designed using a powerful, inexpensive microcomputer. We customized an accelerometer and miniature camera modules for efficient, real-time monitoring of animal motion detection and pupil diameter. We then tested and validated the platform's performance with optical intrinsic signal imaging and GCaMP fluorescence calcium imaging in functional activation experiments in awake mice. The integrated platform shows promise for detecting spontaneous motion and pupil dilation while imaging. Stimulus-induced pupil dilation was found to initiate earlier than cortical hemodynamics with a slower rise time. Compared with neuronal calcium response, stimulus-induced pupil dilation initiated later with a slower rise time. We developed an integrated platform to monitor animal motion and pupil dynamics. The device can be easily coupled and synchronized with optical brain imaging systems to monitor behavior, alertness, and spontaneous motion for awake animal studies.

Full Body-Worn Textile-Integrated Nanomaterials and Soft Electronics for Real-Time Continuous Motion Recognition Using Cloud Computing.

Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system's performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.

Wi-Filter: WiFi-Assisted Frame Filtering on the Edge for Scalable and Resource-Efficient Video Analytics

With the growing prevalence of large-scale intelligent surveillance camera systems, the burden on real-time video analytics pipelines has significantly increased due to continuous video transmission from numerous cameras. To mitigate this strain, recent approaches focus on filtering irrelevant video frames early in the pipeline, at the camera or edge device level. In this paper, we propose Wi-Filter, an innovative filtering method that leverages Wi-Fi signals from wireless edge devices, such as Wi-Fi-enabled cameras, to optimize filtering decisions dynamically. Wi-Filter utilizes channel state information (CSI) readily available from these wireless cameras to detect human motion within the field of view, adjusting the filtering threshold accordingly. The motion-sensing models in Wi-Filter (Wi-Fi assisted Filter) are trained using a self-supervised approach, where CSI data are automatically annotated via synchronized camera feeds. We demonstrate the effectiveness of Wi-Filter through real-world experiments and prototype implementation. Wi-Filter achieves motion detection accuracy exceeding 97.2% and reduces false positive rates by up to 60% while maintaining a high detection rate, even in challenging environments, showing its potential to enhance the efficiency of video analytics pipelines.

Retina-Inspired Flexible Visual Synaptic Device for Dynamic Image Processing.

Exploiting biomimetic perception of invisible spectra in flexible artificial human vision systems (HVSs) is crucial for real-time dynamic information processing. Nevertheless, the fast processing of motion objects in natural environments poses a challenge, necessitating that these artificial HVSs simultaneously have swift photoresponse and nonvolatile memory. Here, inspired by the human retina, we propose a flexible UV neuromorphic visual synaptic device (NeuVSD) based on GaOx@GaN-composited nanowires for dynamic visual perception. Benefiting from the combined action of oxygen adsorption kinetics on the GaOx shell and photoelectronic excitation in the GaN core, the NeuVSD can achieve rapid photoresponse and long-term plasticity simultaneously, a feature that surpasses traditional devices based on persistent photoconductivity mechanisms. Beyond the optoelectronic synaptic plasticity, the flexible NeuVSD on the PET/PI substrate exhibits good performance stability after 1000 bending cycles, profiting from the high adaptability of nanowires. Furthermore, target recognition and motion detection with weak light intensities are achieved through the establishment of neuromorphic visual neural networks, relying on dynamic exposures and edge information processing, respectively. Real-time edge detection can still be realized under a 40% noise factor and effectively remove the noise background. The flexible NeuVSD array-based artificial visual system can enhance the capability of dynamic visual information processing in low-light and high-noise conditions, thereby fostering the evolution of in-sensor computing and artificial intelligence.

Flexible Eyelid Pressure and Motion Dual-Mode Sensor Using Electric Breakdown-Induced Piezoresistivity and Electrical Potential Sensing.

Multiple ocular surface disorders are associated with the mechanical properties of the interface between the eyelid and cornea. Determining eyelid pressure is vital for diagnosing and preventing these disorders. However, current measurements rely on flat piezoresistive pressure sensor arrays that lack eye-motion sensing capabilities, resulting in discomfort and measurement inaccuracies. This study develops and evaluates an integrated, curved, flexible, dual-mode sensor array for simultaneous eyelid pressure and motion detection, using soft thermoplastic polyurethane (TPU) films as transducers and substrates. A novel manufacturing method based on the electrical breakdown of the TPU film enables piezoresistive pressure sensing, achieving a pressure detection limit of 3.2 Pa. Eyelid motion is measured through electrical potential sensing, where changes in eyelid position alter the electric potentials at the receiving electrodes. The sensor's performance was validated with animal experiments involving rabbit eyes; eyelid pressure was successfully measured during eye opening and blinking. This flexible dual-mode eyelid sensor holds promise for monitoring eyelid pressure and assessing ocular surface disorders.

Experimental and numerical evaluation of the green sand compaction process

ABSTRACT Determining the parameters of the sand moulding process plays an important role in obtaining green sand mould without wasting the energy used in the moulding process. The present work focuses on studying the variation effect of the sand density and elasto-plastic properties on the compaction process of sand moulding. Triaxial and uniaxial compression tests were used to estimate the properties of bentonite-bonded green sand. The behaviour of the green sand mould during the compaction process was investigated numerically by commercial finite element software, and experimentally using a flowability sensor. Detection of the green sand movement by a flowability sensor showed good agreement between the experimental and numerical results. Similar behaviour was observed for the distance-dependent displacement decrease with increasing green sand density. Stress concentration has an effect on the sand flow, density distribution and displacement of green mould sand.

High linearity and low hysteresis LMPs/MXene/AgNWs strain sensor for human motion detection

High linearity and low hysteresis LMPs/MXene/AgNWs strain sensor for human motion detection

Motion-Onset Visually Evoked Potentials (VEPs) are Amplified in The Deaf.

The loss of a sensory modality triggers a phenomenon known as cross-modal plasticity, where areas of the brain responsible for the lost sensory modality are reorganized and repurposed to the benefit of the remaining modalities. After perinatal or congenital deafness, superior visual motion detection abilities have been psychophysically identified in both humans and cats, and this advantage has been causally demonstrated to be mediated by reorganized auditory cortex. In our study, we investigated visually evoked potentials (VEPs) in response to motion-onset stimuli of varying speeds in both hearing and perinatally deafened cats under light anesthesia. While the peak latencies did not differ between the two groups, we observed significantly greater VEP amplitudes in deaf cats, specifically in the P1 component and the signal power of the overall waveform. Through sigmoidal modelling, we identified that the speed offset and steepness at the threshold for 50% maximum neural activity was unchanged, showing that neuronal activity was modulated by motion speeds in a comparable manner between the hearing and deaf subjects and the deaf had greater potentials at all dot speeds. Our results suggests that the increased cortical activity by the auditory and visual cortices of deaf cats may account for their superior behavioral advantage in motion detection and indicates that cross-modal plasticity plays a significant role in the cortical processing of motion.

Barred Owl (<i>Strix varia</i>) activity and diet recorded with a camera trap at a natural cavity nest in Manitoba, Canada (2016–2017)

Camera trap images (n = 27 092) collected at a natural Barred Owl (Strix varia) nest site in Manitoba, Canada, 2016–2019, were used to quantify nesting behaviour and identify prey delivered to the nest. Adult Barred Owl activity increased prior to egg laying and again after incubation. Adults were mostly active at night, but daytime activity increased during the nestling period in 2016 and more so with a larger brood in 2017. Nestlings were active at the nest entrance both day and night for 8–9 days prior to fledging at ≤27–32 d old. Two of three nestling activity peaks (0400 and 2000 Central Daylight Time [CDT]) corresponded to prey delivery activity peaks whereas a third (1200 CDT) did not. Only 31/65 prey were identified to species, but 12 new prey taxa were documented for Barred Owl in Manitoba. Activity at the cavity during the non-breeding season before and after a nest predation event were documented; the nest site was abandoned after an American Black Bear (Ursus americanus) visited the nest cavity in May 2018. Factors affecting the quality and quantity of images, and hence data obtained from them, included camera position relative to the nest cavity entrance, light levels, camera trigger speed, non-target species, and the configuration and settings of motion detection sensors. Improved camera traps may overcome these limitations. This is the first published study on this owl species using this increasingly popular technology.

Learning physics while monitoring vibrations of the Earth—investigating seismic phenomena by using a Raspberry Pi

Abstract This article deals with the context of seismology and its relevance to the teaching of physics in a modern and highly motivating way. A modular system called Raspberry Shake RS1D is presented, which is capable of detecting earthquakes as well as everyday vibrations. The Raspberry Shake 1D Vertical Motion Seismograph combines a Raspberry Pi mini-computer, a vertical geophone, a 24-bit digitizer and a near-real-time (miniSEED) data transmission. While the Raspberry Shake is a low cost professional and already well known tool for seismologists, we as physicists 'simply' used that computer-based measurement system. 

We describe how the seismometer works. We present the detection of ground motion from earthquakes as well as from everyday vibrations using selected examples. Thereby, we focus on teaching physics in the context of seismology, presenting didactic ideas for learning about acoustics, for utilizing measurement systems and for applying scientific methods.

This article is especially relevant for teaching undergraduate students. Basic facts about earthquakes that are essential for the study of the seismic phenomena discussed in this article are conveyed.

Mussel‐Inspired Highly Sensitive, Stretchable, and Self‐Healable Yarns Enabled by Dual Conductive Pathways and Encapsulation for Wearable Electronics

AbstractWearable electronic textiles, capable of detecting human motions and recognizing gestures, represent the forefront of personalized electronics. However, the integration of high stretchability, sensitivity, durability, and self‐healable/self‐bondable capabilities into one platform remains challenging. Herein, mussel‐inspired stretchable, sensitive, and self‐healable/self‐bonded conductive yarns enabled by dual electron transfer pathways and dual encapsulation technology are presented. Specifically, covered spandex yarns provide the necessary stretchability and adsorption capacity, while supramolecular polydopamine layer affords enhanced interfacial interactions. Reduced graphene oxide nanosheets and silver nanoparticle‐based sensing layers offer dual electron transfer pathways. Dual encapsulations with self‐healable/self‐bondable ability not only mitigate the crack propagation but also protect inner conductive materials from detachment. Benefiting from these rational designs, the composite yarns exhibit a large sensing range (158% strain), high sensitivity (22.88), low detection limit (0.0345%), fast response/recovery times (105/150 ms), and remarkable robustness (enduring 10 000 cycles at 20% strain). Furthermore, pressure sensors and sensing arrays are assembled by stacking conductive yarns perpendicularly using a self‐bondable function, and self‐healable helical‐structured conductors are fabricated through the shape‐memory effect. Important applications of multifunctional yarns in physiological motion detection, gesture recognition, and circuit connection are demonstrated. This concept creates opportunities for the construction of multifunctional and high‐performance wearable electronic textiles.

Computer Vision and Advanced Computational Algorithms for Risk Assessment and Performance Enhancement in Track and Field Teaching

This paper discusses the application of computer vision and advanced calculation algorithm in evaluating the teaching risk and teaching effect of track and field. Because of the inherent uncertainty and risk of PE and sports activities (especially track and field), it is necessary to establish an effective risk management mechanism. Using computer vision technology, this paper puts forward a method of analyzing and processing video data to detect and track moving objects, so as to identify potential risks in real time. This method not only improves the safety of students in track and field classes, but also provides valuable insights for improving teaching methods and reducing sports injuries. This paper discusses the background subtraction motion detection algorithm, which is very important for dynamic image modeling and shadow suppression, and can realize accurate motion state detection. The ultimate goal is to ensure the healthy development of school sports and optimize the teaching results of track and field sports.

Research on Precise Servo Control of Hand Motion Real-time Detection Based on Surface Electromyography Signals Integration and Analysis of Muscle Response to Workout Exercises

This study explores the integration of surface electromyography (sEMG) signals for precise servo control in real-time hand movement detection. It also further utilizes the feedback system designed to measure effects of workout exercises on forearm sEMG signals and delves into how different time and types of workouts affect forearm muscle response. Medical electrodes captured sEMG signals from forearm muscles and converted them into digital signals using an Arduino UNO R3 microcontroller board and an Olimex EMG shield to monitor hand pose variations. The digital signals were then processed to calculate the root mean square (RMS) voltage, which was then used to control the position of a servo motor. The implementation of a proportional-derivative (PD) controller further enhanced the accuracy and stability of the servo movements. Then the feedback system was used to measure how sEMG signals vary due to hand opening and closing before and after a range of workout exercises with varying time and workout types, and the results were compared to reach the conclusion that both workout time and type influences how forearm muscles respond to hand closing and opening. Future work may explore the effect of more time and types of workouts on muscle response, expand the range of test subjects, and improve the accuracy and stability of the test system

Research on Composite Strain and Stress Sensors Based on Piezoresistive and Triboelectric Effects

Abstract In view of the problems that robotic arms find it difficult to effectively identify and grasp workpieces with different textures and hardness in complex industrial environments, and the low path planning accuracy of robotic arms in practical application scenarios, this paper proposes a composite sensor based on piezoresistive effect and triboelectric effect. The piezoresistive sensor and the triboelectric sensor simultaneously generate output signals, and the identification of different achieves high-precision dynamic monitoring of the robotic arm's gripping objects and joint movements. The base material of the piezoresistive sensor and the negative electrode material of the triboelectric sensor are both porous MXene/PDMS structures. The piezoresistive sensor enhances its conductivity by spin-coating CNT slurry on the surface of the base material and uses PVA hydrogel as the electrode. The triboelectric sensor uses copper as the positive electrode material. Experiments show that the developed sensor has a measurement range (0.015 kPa-70 kPa) and good repeatability. The experiment verifies that the composite sensor can be applied to the high-precision detection of robotic arm's flexible gripping and joint movements.

A flexible piezoresistive three-dimensional strain sensor based on laser-induced graphene/nanosilver/MWCNTs for precise human all-range motion detection

Flexible piezoresistive strain sensors are crucial for monitoring human motion, but achieving the right balance between sensitivity and operating range has always been challenging. Additionally, the complexity of muscle movements across different body parts means that relying on sensors with limited dimensional sensing is insufficient. This paper presents a flexible piezoresistive three-dimensional strain sensor (FPTDSS) designed to address these challenges. The FPTDSS features a wide operating range capable of detecting various human movements and boasts a high sensitivity, with a maximum gauge factor of 20 479. It can capture strain information along both the X- and Y-axes, as well as small vibrations along the Z-axis, through its intrinsic stretching and vibration properties. The sensor's effectiveness comes from the synergy between laser-induced graphene, silver nanoparticles (a zero-dimensional nanomaterial), and multi-walled carbon nanotubes (a one-dimensional nanomaterial). The synergistic effect of nanomaterials with different dimensions enables the FPTDSS to perform three-dimensional strain sensing, allowing for accurate detection of a broad range of complex human motions without requiring intricate circuit designs or preparation processes. This approach moves beyond limited strain information to provide a comprehensive view of three-dimensional strain, making the sensor versatile for detecting everything from subtle pulse vibrations to significant joint movements.

Hierarchical elastic and conductive multiwall carbon nanotube carboxymethylcellulose silica hybrid aerogel for strain sensors

Abstract Flexible and wearable piezoelectric pressure sensors have attracted extensive interest for their potential applications in human health monitoring. However, it remains a challenge to fabricate exceedingly elastic, conductive and steady strain-sensitive aerogels. Herein, an elastic and conductive carboxymethylcellulose sodium/methyltrimethoxysilane/multiwall carbon nanotube (CMC/MTMS/MWCNT) aerogel with interconnected porous microstructure was fabricated by a solution mixing and freeze-drying technique. Owing to covalent, ionic and hydrogen-bonding interactions between flexible CMC chains, MTMS and MWCNTs, the as-prepared CMC/MTMS/MWCNT hydrophobic aerogel with 50 wt% MWCNT loading exhibits good elasticity (1500 steady compression cycles at 30% strain), a broad pressure detection range (0–150 kPa) and reasonable compression sensitivity with gauge factor of 1.02 under 20%–30% strain. Additionally, the CMC/MTMS/MWCNT aerogel was used as a strain sensor and successfully demonstrated human motion detection—not only large-scale actions (finger bending) but also small-scale muscle movements (swallowing). With these results, our CMC/MTMS-50 aerogel is a promising candidate for utilization in flexible and wearable strain sensors.

Voxel-based lesion symptom mapping localizes residual visual function in hemianopia.

Damage to the primary visual cortex (V1) results in visual field deficits on the contralateral side of the world corresponding to the damaged region. Patients with such loss nonetheless show varying residual vision within this apparently blind region, with the neural mechanisms underlying this ability obscured by small study populations. We identified lesions on structural scans from 39 patients (12 female) with hemianopia and occipital lobe damage. We estimated the proportion of damage throughout visual cortex, and correlated this with performance in up to three different tests of residual vision in the blind field. We then performed voxel-based lesion symptom mapping (VLSM) to determine the precise neural regions associated with residual vision. V1 damage did not show a relationship with residual vision measured with any task, although the extent of damage to V4 and hMT+/V5 both correlated with the ability to detect contrast-modulated stimuli. Indeed, damage to hMT+/V5 severely impacted performance across all three tasks, particularly motion detection. Although hMT+/V5 appeared necessary for residual vision, some patients with intact hMT+/V5 had no residual vision, indicating this area alone was not sufficient. VLSM revealed that damage to the optic radiation laterally was most highly associated with poor residual vision. Thus, VLSM indicates that hMT+/V5 and the optic radiation specifically are critical for residual vision in hemianopia.Significance Statement Visual loss is extremely common after acquired brain injury, but is notoriously difficult to treat. Residual vision, or 'blindsight' represents a potential target for visual retraining and plasticity. In the largest single method study in blindsight, we apply the technique of voxel-based lesion symptom mapping for the first time. We show that extrastriate cortex is important for residual vision, but its involvement in lesions cannot explain all behavioural results. Instead, a region of optic radiation close to the visual motion area and lateral occipital cortex is most highly associated with residual vision in hemianopia. This represents a potential target for hemianopia recovery training, and may help us understand why some patients benefit from extensive visual retraining while others do not.

Intelligent Video Recording Optimization using Activity Detection for Surveillance Systems

Surveillance systems often struggle with managing vast amounts of footage, much of which is irrelevant, leading to inefficient storage and challenges in event retrieval. This paper addresses these issues by proposing an optimized video recording solution focused on activity detection. The proposed approach utilizes a hybrid method that combines motion detection via frame subtraction with object detection using YOLOv9. This strategy specifically targets the recording of scenes involving human or car activity, thereby reducing unnecessary footage and optimizing storage usage. The developed model demonstrates superior performance, achieving precision metrics of 0.855 for car detection and 0.884 for person detection, and reducing the storage requirements by two-thirds compared to traditional surveillance systems that rely solely on motion detection. This significant reduction in storage highlights the effectiveness of the proposed approach in enhancing surveillance system efficiency. Nonetheless, some limitations persist, particularly the occurrence of false positives and false negatives in adverse weather conditions, such as strong winds.