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20 results for “Motion Detection”
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
Data from: Illusory speeding-up and slowing-down of objects moving at constant speed emerges from natural motion detection algorithms
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Using motion-detection cameras to monitor foraging behaviour of individual butterflies
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Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
<p>Scoring large amounts of thermal tolerance traits live or with recorded video can be time consuming and susceptible to investigator bias, and as with many physiological measurements, there can be trade-offs between accuracy and throughput. Recent studies show that particle tracking is a viable alternative to manually scoring videos, although it may not detect subtle movements, and many of the software options are proprietary and costly. In this study, we present a novel strategy for automated scoring of thermal tolerance videos by inferring motor activity with motion detection using an open-source Python command line application called DIME (Detector of Insect Motion Endpoint). We apply our strategy to both dynamic and static thermal tolerance assays, and our results indicate that DIME can accurately measure thermal acclimation responses, generally agrees with visual estimates of thermal limits, and can significantly increase the throughput over manual methods.</p>
Effect of Alphagan on Retinal Blood Flow Autoregulation and Motion Detection in Patients With Normal Pressure Glaucoma
ClinicalTrials.gov study NCT01105065. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
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Neonicotinoid and sulfoximine pesticides differentially impair insect escape behaviour and motion detection
<p><span><span><span><span><span><span><span><span><span><span><span>Insect nervous systems offer unique advantages for studying interactions between sensory systems and behaviour given that they are complex and yet highly tractable. By examining the neural coding of salient environmental stimuli and resulting behavioural output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behaviour and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of non-target species, such as bees, has motivated development of new compounds, which could potentially mitigate putative resistance in target species (1, 2) and declines of non-target species. We used a neuroethological approach, including behavioural assays and multineuronal recording techniques, to investigate effects of imidacloprid and the novel insecticide sulfoxaflor on visual motion-detection circuits and related escape behaviour in the tractable locust system. Despite similar LD50 values, imidacloprid and sulfoxaflor evoked different behavioural and physiological effects. Imidacloprid significantly attenuated collision avoidance behaviours and impaired responses of neural populations, including a decrease in spontaneous firing and decreased neural habituation. In contrast, sulfoxaflor displayed no effect at a comparable sublethal dose. These are the first results to show that a neonic affects population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of sulfoxaflor reflect an altered mode of action to imidacloprid. More broadly, we suggest that neuroethological assays for comparative neurotoxicology are a valuable tool to fully address current issues regarding proximal effects of environmental toxicity in non-target species.</span></span></span></span></span></span></span></span></span></span></span></p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - raw figures of all quantities from all simulations
<p>We performed many simulations in many conditions, calculated many quantities and reported them in the main paper. However, the journal manuscript did not place for so many figures. Nevertheless, the reported values of all simulations rely on the results that were also summarized in the figures. These figures were produced by the Jupyter notebooks analyzing the simulation results.</p> <p>Each figure file has the following nomenclature:</p> <p>TypeOfFigure_concentration_diffusionCoefficient_1or2pop_simulationDuration(long)_Type of PSF.</p> <p>For example:</p> <p><em>molecule_positions_pure_and_impure_bursts_varying_F_C62_D22_5_psfNumeric</em> - report on the molecular position histograms of pure and impure bursts for varying F values, in the simulation with C=62pM, D=22.5 um^2/s, a NUmerical PSF model and 60s duration of simulation (if nothing appears, it was 60s; if the word <em>long</em> appears, it was 180s).</p>
Artificial Intelligence-Based Motion Analysis for Early Detection of COPD
ClinicalTrials.gov study NCT07010211. IPD Sharing: YES. Countries: 0. Publications: 2.
Detection of Vocal Fold Motion Impairment on Noninvasive Positive Pressure
ClinicalTrials.gov study NCT06406725. IPD Sharing: NO. Countries: 1. Publications: 3.
A motion-detection based camera trap for small nocturnal mammals with low latency and high signal-to-noise ratio
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Data from: MotionMeerkat: integrating motion video detection and ecological monitoring
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Neonicotinoid and sulfoximine pesticides differentially impair insect escape behaviour and motion detection
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Acoustic particle motion detection in the snapping shrimp (Alpheus richardsoni)
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High proper motion objects detected in unTimely W2 band data
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Detection of Acid Sphingomyelinase/Ceramide Pathway Activation in Radiotherapy Patients Using Intravoxel Incoherent Motion (IVIM) Diffusion-weighted Magnetic Resonance Imaging and Serum Biomarkers
ClinicalTrials.gov study NCT02465723. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Artificial intelligence applied to detect lethargy through individual motion monitoring: A case of ASF in wild boar
<p>Early detection of infectious diseases is the most cost-effective strategy in disease<br> surveillance for reducing the risk of outbreaks. Latest deep learning and computer<br> vision improvements are powerful tools that open up a new field of research in<br> epidemiology and disease control. In this work, these techniques were employed to<br> develop an algorithm aimed to track and compute individual animal motion in real time.<br> This algorithm was used in experimental trials in order to assess African swine fever<br> (ASF) infection course in Eurasian wild boar. Overall, the outcomes showed a strong<br> correlation between motion reduction and fever caused by ASF infection. In addition,<br> infected animals computed significant low movements compared to uninfected animals.<br> The obtained results suggest that a motion monitoring system based on artificial<br> intelligence may be used to trigger suspicions of fever. It would help farmers and animal<br> health services to early detect clinical signs compatible with infectious diseases. This<br> technology shows a promising start up for implementing non-intrusive, economic and<br> real time solutions in the livestock industry with especial interest in ASF, considering<br> the current concern in the world pig industry.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.