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36 results for “Accelerometry”
Thigh Accelerometry Position Study
<p>The Thigh Accelerometry Position Study (TAPS) provides raw sensor data of thigh-worn accelerometer measurements that are collected simultaneously at different positions on the thigh, i.e., centre vs. upper thigh. Thirty-six participants wore two accelerometers for up to seven days during everyday life.</p><p>TAPS allows existing and newly developed algorithms to be validated with respect to these different placements.</p><p>The data is accompanied by a study description that provides details of the data collection and available data (<a href="https://zenodo.org/api/records/10150882/draft/files/TAPS_Study_Description_v1.pdf/content">TAPS_Study_Description_v1.pdf</a>. Structured metadata is available in DDI format in <a href="https://zenodo.org/api/records/10150882/draft/files/TAPS_DDI_Metadata_v1.xml/content">TAPS_DDI_Metadata_v1.xml</a>). The instructions, questionnaire and participant information used in TAPS are available in English and German (<a href="https://zenodo.org/api/records/10150882/draft/files/TAPS_FieldworkDocuments.zip/content">TAPS_FieldworkDocuments.zip</a>).</p><p> </p>
PAAL ADL Accelerometry dataset v2.0
<p>The PAAL ADL Accelerometry dataset (v2.0) has been acquired with a high-quality wearable multisensor device, the <a href="https://www.empatica.com/research/e4/">Empatica E4</a>. In this dataset, among the signals collected by the sensors embedded in the Empatica E4, only the acceleration has been extracted to monitor the users performing different activities of daily living (ADLs). To promote the real-life acquisition procedure, subjects acted in their natural environment, with no instructions about how and for how long to perform each activity (other than a minimum time). The device was worn on the dominant hand.</p> <p>The dataset includes <strong>24 different ADLs</strong> performed using real objects. Each activity was repeated between 3 and 5 times (on average) by <strong>52 healthy subjects</strong>, characterized by a gender balance <strong>(26 women and 26 men)</strong>, and a large age range (between 18 and 77 years, mean = 44.08 years and standard deviation = 17.06 years).</p> <p>The PAAL ADL Accelerometry dataset (v2.0) is composed of three files:</p> <ul> <li>users.csv: each line contains (participant id, gender, age) of each user performing the ADLs in the dataset. <em>N.B: gender labels are 'man' and 'woman'</em>.</li> <li>ADLs.csv: each line contains (ADL id, ADL name)</li> <li>data.zip: folder with 6,072 files of accelerometer data of users performing ADLs. The name of each file indicates the name of the ADL, the user id and the repetition. Each row in the files represents the continuous gravitational force (g) applied to each of the three spacial dimensions (x, y, and z). The scale is limited to [-2g, +2g]. The sampling frequency is 32 Hz, with a resolution of 0.015 g (8 bit). More information about the format <a href="https://support.empatica.com/hc/en-us/articles/202028739-How-is-the-acceleration-data-formatted-in-E4-connect-">here</a>.</li> </ul>
Applying time series analyses on continuous accelerometry data – Dataset
<p>Data and analysis script accompanying the study:</p> <p>Applying time series analyses on continuous accelerometry data – a clinical example in older adults with and without cognitive impairment</p>
Dataset for the comparison of accelerometry-based and self-reported physical activity and their association with cardiovascular risk markers in children from South Africa
<p>Dataset used to evaluate and compare self-reported with accelerometry-based physical activity measurements as well as their associations with cardiovascular risk markers among South African school-aged children from disadvantaged communities.</p> <p>It encompasses anonymized, unique, identification numbers, demographic and body-mass-index, blood pressure, lipid panel and blood glucose measures.</p>
Self-reported and accelerometry measures of sleep components in adolescents living in Pacific Island countries and territories: Exploring the role of sociocultural background
<p>Data from self-report questionnaires and accelerometer extraction on sleep components (onset sleep time, wake-up time and sleep duration on weekdays and weekend).</p> <p>Participant characteristics: 10 to 16 years old students.</p> <p>Number of participants: 182.</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p>
Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients (DATASET)
<p>see README.txt for descriptions of files and formats<br> </p>
Accelerometry data for an Imperial Cormorant
<p>Tri-axial accelerometry data for an Imperial Cormorant.</p> <p>Part of a data set from "Identification of animal movement patterns using tri-axial accelerometry"</p> <p>doi: 10.3354/esr00084</p>
Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry
<p>This repository contains code and data for the research project 'Estimation of activity induced energy expenditure using thigh-worn accelerometry and machine learning approaches'.</p> <ul> <li>The <strong>code </strong>subfolder contains Jupyter Notebooks and a Python file with helper functions. Further, the models subfolder contains the trained models.</li> <li>The <strong>data </strong>subfolder contains the raw accelerometer files (AX) as well as the raw data from the indirect calorimetry (CPET). Further, different processing files can be found here. The file log_master.csv contains the sociodemographic and timestamp data.</li> <li>The <strong>figures</strong> subfolder contains all relevant figures, which are created as part of running the Jupyter Notebooks. These figures are also part of the research publication.</li> </ul>
Data from: Energetic fitness: field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics
1) Evaluating the fitness of organisms is an essential step towards understanding their responses to environmental change. Connections between energy expenditure and fitness have been postulated for nearly a century. However, testing this premise among wild animals is constrained by difficulties in measuring energy expenditure while simultaneously monitoring conventional fitness metrics such as survival and reproductive output. 2) We addressed this issue by exploring the functional links between field metabolic rate (FMR), body condition, sex, age and reproductive performance in a wild population. 3) We deployed 3D accelerometers on 115 Adélie penguins (Pygoscelis adeliae) during four breeding seasons at one of the largest colonies of this species, Cape Crozier, on Ross Island, Antarctica. The demography of this population has been studied for the past 18 years. From accelerometry recordings, collected for birds of known age and breeding history, we determined the vector of the dynamic body acceleration (VeDBA) and used it as a proxy for FMR. 4) This allowed us to demonstrate relationships between FMR, a breeding quality index (BQI), and body condition. Notably, we found a significant quadratic relationship between mean VeDBA during foraging and BQI for experienced breeders, and individuals in better body condition showed lower rates of energy expenditure. 5) We conclude that using FMR as a fitness component complementary to more conventional fitness metrics will yield greater understanding of evolutionary and conservation physiology.
CMT1A-BioStampNPoint2023: Charcot-Marie-Tooth disease type 1A accelerometry dataset from three wearable sensor study
<p>The CMT1A-BioStampNPoint2023 dataset provides data from a wearable sensor accelerometry study conducted for studying gait, balance, and activity in 15 individuals with Charcot-Marie-Tooth disease Type 1A (CMT1A). In addition to individuals with CMT1A, the dataset also includes data for 15 controls that also went through the same in-clinic study protocol as the CMT1A participants with a substantial fraction (9) of the controls also participating in the in-home study protocol. For the CMT1A participants, data is provided for 15 participants for the baseline visit and associated home recording duration and, additionally, for a subset of 12 of these participants data is also provided for a 12-month longitudinal visit and associated home recording duration. For controls, no longitudinal data is provided as none was recorded. The data were acquired using lightweight MC 10 BioStamp NPoint sensors (MC 10 Inc, Lexington, MA), three of which were attached to each participant for gathering data over a roughly one day interval. For additional details, see the description in the "README.md" included with the dataset.</p>
Data from: Energetic fitness: field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics
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CMT1A-BioStampNPoint2023: Charcot-Marie-Tooth disease type 1A accelerometry dataset from three wearable sensor study
Open the record for dataset details and reuse information.
Cryptic behaviour and activity cycles of a small mammal keystone species revealed through accelerometry: a case study of Merriam’s kangaroo rats
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Data from: The secret life of ground squirrels: accelerometry reveals sex-dependent plasticity in above-ground activity
The sexes differ in how and when they allocate energy towards reproduction, but how this influences phenotypic plasticity in daily activity patterns is unclear. Here, we use collar-mounted light loggers and triaxial accelerometers to examine factors that affect time spent above ground and overall dynamic body acceleration (ODBA), an index of activity-specific energy expenditure, across the active season of free-living, semi-fossorial arctic ground squirrels (Urocitellus parryii). We found high day-to-day variability in time spent above ground and ODBA with most of the variance explained by environmental conditions known to affect thermal exchange. In both years, females spent more time below ground compared with males during parturition and early lactation; however, this difference was fourfold larger in the second year, possibly, because females were in better body condition. Daily ODBA positively correlated with time spent above ground in both sexes, but females were more active per unit time above ground. Consequently, daily ODBA did not differ between the sexes when females were early in lactation, even though females were above ground three to six fewer hours each day. Further, on top of having the additional burden of milk production, ODBA data indicate females also had fragmented rest patterns and were more active during late lactation. Our results indicate that sex differences in reproductive requirements can have a substantial influence on activity patterns, but the size of this effect may be dependent on capital resources accrued during gestation.
Data from: Combined use of GPS and accelerometry reveals fine scale three-dimensional foraging behaviour in the short-tailed shearwater
Determining the foraging behaviour of free-ranging marine animals is fundamental for assessing their habitat use and how they may respond to changes in the environment. However, despite recent advances in bio-logging technology, collecting information on both at-sea movement patterns and activity budgets still remains difficult in small pelagic seabird species due to the constraints of instrument size. The short-tailed shearwater, the most abundant seabird species in Australia (ca 23 million individuals), is a highly pelagic procellariiform. Despite its ecological importance to the region, almost nothing is known about its at-sea behaviour, in particular, its foraging activity. Using a combination of GPS and tri-axial accelerometer data-loggers, the fine scale three-dimensional foraging behaviour of 10 breeding individuals from two colonies was investigated. Five at-sea behaviours were identified: (1) resting on water, (2) flapping flight, (3) gliding flight, (4) foraging (i.e., surface foraging and diving events), and (5) taking-off. There were substantial intra- and inter- individual variations in activity patterns, with individuals spending on average 45.8% (range: 17.1–70.0%) of time at sea resting on water and 18.2% (range: 2.3–49.6%) foraging. Individuals made 76.4 ± 65.3 dives (range: 8–237) per foraging trip (mean duration 9.0 ± 1.9 s), with dives also recorded during night-time. With the continued miniaturisation of recording devices, the use of combined data-loggers could provide us with further insights into the foraging behaviour of small procellariiforms, helping to better understand interactions with their prey.
Data and code used for 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'.</p> <p> </p> <h2><strong>File structure</strong></h2> <p><strong>- analysis</strong></p> <p>The analysis subfolder contains all R scripts used for the study:</p> <p>1. Sample size estimation<br>2. Synchronisation of the timestamps<br>3. Processing of the raw data<br>4. Calculating the interrater agreement<br>5. Calculating all performance metrics and producing the plots</p> <p>In addition, the two subfolders contain the plots and result tables produced when running the scripts.</p> <p> </p> <p><strong>- data</strong></p> <p>The data folder contains all raw data as well as the processed data. A subfolder for each subject contains the video annotations (.eaf), the raw acceleration data (.csv) and the SENS motion classification data (.csv).</p> <p>The ActiPASS subfolder contains the raw acceleration files in binary file format as well as the ActiPASS output.</p> <p>The shiny subfolder contains the R code used for running the shiny app during data collection as well as the logged data and timestamps.</p> <p> </p> <p><span><strong><span>- documents</span></strong></span></p> <p><span>This folder contains any additional documents used in the study.</span></p> <p> </p> <h2><strong><span>Requirements</span></strong></h2> <p>The data processing and analysis was performed in R (Version 4.3.2). To run the full analysis in R, the following packages need to be installed:</p> <ul> <li>tidyverse</li> <li>xml2</li> <li>lubridate</li> <li>here</li> <li>dygraphs</li> <li>hms</li> <li>irr</li> <li>yardstick</li> <li>cowplot</li> <li>gt</li> </ul>
Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
Identifying Physical Activity Intensity Through Accelerometry in Heart Failure
ClinicalTrials.gov study NCT03659877. IPD Sharing: NO. Countries: 1. Publications: 1.
Global Positioning Satellite and Accelerometry to Assess Human Locomotion (ACTI GPS)
ClinicalTrials.gov study NCT01805219. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Exploratory Pilot Study of Physical Activity Monitoring in Adult Patients With Haemophilia A by Means of Accelerometry
ClinicalTrials.gov study NCT02253693. IPD Sharing: NO. Countries: 1. Publications: 2.
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