2014
DOI: 10.1007/978-3-319-13105-4_14
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Abstract: Abstract. Mobile health is an emerging field which is attracting much attention. Nevertheless, tools for the development of mobile health applications are lacking. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of biomedical apps. The framework is devised to leverage the potential of mobile devices like smartphones or tablets, wearable sensors and portable biomedical devices. The framework provides functionalit… Show more

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Cited by 316 publications
(136 citation statements)
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“…Tools to analyze the provenance of mobile health data have also been suggested in [15]. Recently, a novel open framework to facilitate the rapid and easy development of biomedical apps has been presented in [16]. The framework is devised to leverage the potential of mobile and wearable health devices, and provides advanced functionalities for resource and communication abstraction, biomedical data acquisition, health knowledge extraction or adaptive visualization, among others.…”
Section: Related Workmentioning
confidence: 99%
“…Tools to analyze the provenance of mobile health data have also been suggested in [15]. Recently, a novel open framework to facilitate the rapid and easy development of biomedical apps has been presented in [16]. The framework is devised to leverage the potential of mobile and wearable health devices, and provides advanced functionalities for resource and communication abstraction, biomedical data acquisition, health knowledge extraction or adaptive visualization, among others.…”
Section: Related Workmentioning
confidence: 99%
“…We experiment with two datasets. The MHEALTH dataset [2] comprises vital signs recordings for ten volunteers (N = 10). We adopt 1% 34 0 58 0 5% 133 0 179 0 10% 217 2 356 0 20% 358 3 535 0 40% 560 22 755 3 60% 682 47 831 10 the measurements corresponding to cases where volunteers are standing still, sitting or lying down.…”
Section: Resultsmentioning
confidence: 99%
“…The first dataset is MHEALTH [35], a mobile health dataset that comprises body motion and vital sign measures for several volunteers of diverse profiles while performing 12 physical activities such as walking, running and climbing stairs. The dataset contains totally 1,215,745 recordings, each of which is composed of 24 types of signals from the sensors such as accelerometer, gyroscope, and magnetometer.…”
Section: A Simulation Setupmentioning
confidence: 99%