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155 results for “accelerometers”
Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
<p>Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. </p> <p>Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. </p> <p>Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%).</p> <p>We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild.</p>
Accelerometer Sensing for Micra AV Study
ClinicalTrials.gov study NCT04245345. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Comparing Rate Response With CLS Versus Accelerometer ICD Settings in Heart Failure Patients With BIOTRONIK CRT-Ds
ClinicalTrials.gov study NCT02693262. IPD Sharing: NO. Countries: 1. Publications: 8.
Micra Accelerometer Sensor Study 2
ClinicalTrials.gov study NCT02930980. IPD Sharing: UNDECIDED. Countries: 3. Publications: 1.
Micra Atrial TRacking Using A Ventricular AccELerometer 2
ClinicalTrials.gov study NCT03752151. IPD Sharing: Not stated. Countries: 8. Publications: 3.
Accelerometer Use in the Prevention of Exercise-Associated Hypoglycemia in Type 1 Diabetes: Outpatient Exercise Protocol
ClinicalTrials.gov study NCT02047643. IPD Sharing: Not stated. Countries: 1. Publications: 19.
Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers
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Data from: the Self-Calibrating Tilt Accelerometer: a method for observing tilt and correcting drift with a triaxial accelerometer
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Less is more: on-board lossy compression of accelerometer data increases biologging capacity
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Data from: Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data
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Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study
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Data from: swimming through sand: using accelerometers to observe the cryptic, pre-emergence life-stage of sea turtle hatchlings
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Ecological inference using data from accelerometers needs careful protocols
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Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
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Data for: Domestic cat accelerometer data calibrated with behaviours
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Synchronization mechanism within the blind zone of the differential resonant accelerometer
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ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
<p> </p> <p><strong>ShimFall&ADL dataset</strong></p> <p> </p> <p><strong>Version </strong>1.0 (2020-06-19)</p> <p><strong>Please cite as:</strong> "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p> <p> </p> <p><strong>Disclaimer</strong><br> While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p><br> <strong>Contact</strong><br> For inquiries regarding the ShimFall&ADL dataset, please contact:<br> Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland<br> Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.</p> <p> </p> <p><strong>Dataset summary</strong><br> The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.</p> <p>Participants performed the following activities of daily living (ADL):<br> Jumping<br> Lying down<br> Bending/picking up<br> Sitting to a chair<br> Standing up from a chair<br> Walking</p> <p>Participants performed the following falls:<br> Steep (hard)<br> Front (soft)<br> Front (hard)<br> Left (soft)<br> Left (hard)<br> Right (soft)<br> Right (hard)<br> Back (soft)<br> Back (hard)</p> <p><br> <strong>Data</strong><br> Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows:<br> Row 1: x1\ty1\tz1<br> Row 2: x2\ty2\tz2<br> ...<br> Row N: xN\tyN\tzN</p> <p>The files within the dataset are named as follows:<br> adl_<ADL activity>_<Participant ID>.dat<br> <Fall Type>fall_<soft,hard>_<Participant ID>.dat</p> <p>For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.</p> <p><br> <strong>Additional information</strong><br> For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p>
Westland petrel data combined GPS and accelerometer data 2016 & 2017
<p>This study investigated the foraging niche of dimorphic males and females Westland petrel during the chick-rearing period. At-sea movements were recorded with GPS, behaviours and foraging behaviour were recorded with accelerometers, and trophic niche was inferred with stable isotopes (carbon, nitrogen). Altogether, these fine-scale data allowed to look at the foraging niche used by males and females.</p>
Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free-ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (VeDBA) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at-sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern (Callorhinus ursinus) and 20 lactating Antarctic fur seals (Arctocephalus gazella) with GPS, time-depth recorders and tri-axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water (DLW) method. VeDBA was derived from tri-axial acceleration, and at-sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri-axial acceleration and travelling speed. We found that VeDBA did not accurately predict the total energy expended by fur seals during their full foraging trips (R2 = 0·36). However, the accuracy of VeDBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity-specific VeDBA was paired with time-activity budgets (R2 = 0·70). Activity-specific VeDBA also accurately predicted the energy expenditures of each activity independent of each other (R2 > 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free-ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time-activity budgets that make up foraging trips rather than being derived as a single measure of VeDBA applied to entire foraging trips. Our activity-based method provides a cost-effective means to accurately calculate energy expenditures of fur seals using acceleration and time-activity budgets, that can be transfered to studies on other species.
Data from: Analysis of animal accelerometer data using hidden Markov models
Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre-specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.
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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.