Calibration of Swarm accelerometer data by GPS positioning and linear temperature correction
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Space Research"
DOI: 10.1016/j.asr.2018.04.041
Abstract: Abstract Swarm, a mission of the European Space Agency, consists of three satellites orbiting the Earth since November 2013. In addition to the instrumentation aimed at fulfilling the mission’s main goal, which is the observation… read more here.
Keywords: temperature; swarm accelerometer; gps; accelerometer ... See more keywords
Assessing the effects of sampling frequency on behavioural classification of accelerometer data
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Experimental Marine Biology and Ecology"
DOI: 10.1016/j.jembe.2018.12.003
Abstract: Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g., diel, tidal, lunar, seasonal, annual) gives unique insight into their ecology. Bio-logging tools such as accelerometers allow the remote study of elusive or… read more here.
Keywords: accelerometer data; classification; sampling frequency; ecology ... See more keywords
Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.
Sign Up to like & getrecommendations! Published in 2017 at "Journal of science and medicine in sport"
DOI: 10.1016/j.jsams.2016.06.003
Abstract: OBJECTIVES Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting… read more here.
Keywords: classifier wrist; free living; random forest; activity ... See more keywords
Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Sports Sciences"
DOI: 10.1080/02640414.2019.1703301
Abstract: ABSTRACT Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms… read more here.
Keywords: wear detection; non; accelerometer data; non wear ... See more keywords
Identification of carnivore kill sites is improved by verified accelerometer data
Sign Up to like & getrecommendations! Published in 2020 at "Animal Biotelemetry"
DOI: 10.1186/s40317-020-00206-y
Abstract: Background Quantifying kill rates is central to understanding predation ecology. However, estimating kill rates and prey composition in carnivore diets is challenging due to their low densities and cryptic behaviors limiting direct observations, especially when… read more here.
Keywords: identification carnivore; activity; kill sites; accelerometer data ... See more keywords
Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods
Sign Up to like & getrecommendations! Published in 2022 at "PLoS ONE"
DOI: 10.1371/journal.pone.0266970
Abstract: Introduction Machine learning (ML) accelerometer data processing methods have potential to improve the accuracy of device-based assessments of physical activity (PA) in young children. Yet the uptake of ML methods by health researchers has been… read more here.
Keywords: classification; accuracy; machine learning; accelerometer data ... See more keywords
Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation.
Sign Up to like & getrecommendations! Published in 2021 at "JMIR mHealth and uHealth"
DOI: 10.2196/29849
Abstract: BACKGROUND Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. OBJECTIVE This study aims to introduce a pipeline to infer sleep onset, duration, and quality from… read more here.
Keywords: quality; pipeline; accelerometer; open source ... See more keywords
Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition
Sign Up to like & getrecommendations! Published in 2018 at "Informatics"
DOI: 10.3390/informatics5020029
Abstract: Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when… read more here.
Keywords: physical activity; activity; feature selection; accelerometer data ... See more keywords