Comparison of Four ActiGraph Accelerometers during Walking and Running

John, Dinesh; Tyo, Brian; Bassett, David R.

doi:10.1249/mss.0b013e3181b3af49

Cited by 153 publications

(140 citation statements)

References 24 publications

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“…Accelerometers have been shown to be valid and reliable for assessment of free-living physical activity (performance) in people with mobility limitations [35][36][37][38][39]. The accelerometer used for the present study, the Actigraph GT1M, is widely accepted in the field as the industry gold standard, and as a valid and reliable tool for assessing ambulatory physical activity [37,[39][40][41][42][43][44][45][46][47].…”

Section: Accelerometry Detailsmentioning

confidence: 99%

Objective Measurement of Free-Living Physical Activity (Performance) in Lumbar Spinal Stenosis: Are Physical Activity Guidelines Being Met?

et al. 2016

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BACKGROUND-Research suggests that people with lumbar spinal stenosis (LSS) would benefit from increased physical activity. Yet, to date, we do not have disease-specific activity guidelines for LSS, and the nature of free-living physical activity (performance) in LSS remains unknown. LSS care providers could endorse the 2008 United States Physical Activity Guidelines; however, we do not know if this is realistic. The goal of the present study was to determine the proportion of individuals with LSS meeting the 2008 Guidelines. A secondary goal was to better understand the nature of physical performance in this population.

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Section: Accelerometry Detailsmentioning

confidence: 99%

Objective Measurement of Free-Living Physical Activity (Performance) in Lumbar Spinal Stenosis: Are Physical Activity Guidelines Being Met?

et al. 2016

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“…The GT1M is a small, lightweight system that measures acceleration in the vertical plane and expresses PA as "activity counts", a proprietary quantification of the detected accelerations, in time intervals of 1 minute [18]. The Actigraph is a validated device and the most widely used device in research [19].…”

Section: Accelerometersmentioning

confidence: 99%

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

Dias

Gorzelniak²,

Schultz³

et al. 2014

Methods Inf Med

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Summary Background: Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult. Objectives: To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. Methods: We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarith mic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. Results: The support vector machine clas -sifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neural networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15. Conclusions: None of the individual feature sets provided robust for reasonable classi -fication of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

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“…However there is no easy method of gaining access to this data and then integrating it with other sources to give users a more complete picture of their overall carbon footprint. In this paper we utilise wearable sensors through the form of the Actigraph GT3x accelerometer [14] attached to the keyrings of participants. This work was motivated by the growing prevalence of wearable sensors and even lifelogging devices ( e.g.…”

Section: Related Workmentioning

confidence: 99%

“…6 users were provided with wearable accelerometers over a 6 week period of time, to capture their activity data to identify instances of driving data, using the techniques developed in the aforementioned subsections. Due to the lack of availability of SenseCams, and also the privacy concerns surrounding personal image capture [1], we provided these 6 users with an Actigraph GT3x unobtrusive tri-axis accelerometer [14] where the users were instructed to place it on their keyrings as illustrated in Figure 5, so as not to miss possible driving events. An issue with the capture of this data was the lack of real-time information, given that data had to be manually downloaded from the wearable accelerometers via USB cable.…”

Section: Effects On Energy Consumptionmentioning

confidence: 99%

Green multimedia

Qiu

Foley

Lee

et al. 2010

Proceedings of the 18th ACM International Conference on Multimedia

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In this work we discuss a new, but highly relevant, topic to the multimedia community; systems to inform individuals of their carbon footprint, which could ultimately effect change in community carbon footprint-related activities. The reduction of carbon emissions is now an important policy driver of many governments, and one of the major areas of focus is in reducing the energy demand from the consumers i.e. all of us individually. In terms of CO2 generated from energy consumption, there are three predominant factors, namely electricity usage, thermal related costs, and transport usage. Standard home electricity and heating sensors can be used to measure the former two aspects, and in this paper we evaluate a novel technique to estimate an individual's transport-related carbon emissions through the use of a simple wearable accelerometer. We investigate how providing this novel estimation of transport-related carbon emissions through an interactive web site and mobile phone app engages a set of users in becoming more aware of their carbon emissions. Our evaluations involve a group of 6 users collecting 25 million accelerometer readings and 12.5 million power readings vs. a control group of 16 users collecting 29.7 million power readings.

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Comparison of Four ActiGraph Accelerometers during Walking and Running

Cited by 153 publications

References 24 publications

Objective Measurement of Free-Living Physical Activity (Performance) in Lumbar Spinal Stenosis: Are Physical Activity Guidelines Being Met?

Objective Measurement of Free-Living Physical Activity (Performance) in Lumbar Spinal Stenosis: Are Physical Activity Guidelines Being Met?

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

Green multimedia

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