20200227_WalkInAPark_LP_steps.avi (549.11 MB)
20200227_WalkInAPark_LP_steps.avi
COLLECTION ITEM:
20200227_WalkInAPark_LP_steps.avi (LP = Lamberto Piccinini)
COLLECTION TITLE:
2020_PiccininiEtAl_FitnessTracking_Video
ARTICLE (when using this file, please, cite the following article):
Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements for analysis in an Internet of Things environment". 2020.
DESCRIPTION OF THE FILES IN THE COLLECTION:
Edited AVI files showing an operator walking in a park.
ITEM TYPE (selected from those available):
Media (i.e. ".avi" file)
MAIN CONTACTS FOR THESE FILE:
1) Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: filippo.piccinini85@gmail.com
2) Prof. Antonella Carbonaro, University of Bologna. Email: antonella.carbonaro@unibo.it
MAIN AFFILIATIONS FOR THIS PROJECT:
1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.
2) University of Bologna, Italy.
PROJECT DESCRIPTION:
The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.
PROJECT CATEGORY (selected from those available):
Computer Vision
PROJECT KEYWORDS (selected from those available):
oncology; fitness training; wearable sensors; Physical Activities; statistical inference.
LICENCE (selected from those available):
GPL 3.0+
20200227_WalkInAPark_LP_steps.avi (LP = Lamberto Piccinini)
COLLECTION TITLE:
2020_PiccininiEtAl_FitnessTracking_Video
ARTICLE (when using this file, please, cite the following article):
Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro, "Accuracy of mobile applications versus wearable devices in long-term step measurements for analysis in an Internet of Things environment". 2020.
DESCRIPTION OF THE FILES IN THE COLLECTION:
Edited AVI files showing an operator walking in a park.
ITEM TYPE (selected from those available):
Media (i.e. ".avi" file)
MAIN CONTACTS FOR THESE FILE:
1) Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: filippo.piccinini85@gmail.com
2) Prof. Antonella Carbonaro, University of Bologna. Email: antonella.carbonaro@unibo.it
MAIN AFFILIATIONS FOR THIS PROJECT:
1) Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola (FC), Italy.
2) University of Bologna, Italy.
PROJECT DESCRIPTION:
The Project focuses on challenges and opportunities today available to improve people’s well-being using IoT self-tracked Health Data. Recent statistics have shown that around 50% of people in developed countries make use of wearable devices to monitor fitness or physical activity (PA). Practically, people can constantly monitor their health status in an unobtrusive way at no cost and the great amount of patient-generated health data today available gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. All the modern smartphones and fitness trackers are equipped with accelerometers that record accelerations in one or more planes. These data elements are processed into more meaningful variables, such as step counts; time spent in sedentary, light, moderate, or vigorous PA; and flights of stairs climbed. Besides discussing the current limits of the fitness tracking technologies, we supported the usage of wearable devices for mHealth and in general oncology-related analysis about cancer prevention, cancer treatment, and survivorship.
PROJECT CATEGORY (selected from those available):
Computer Vision
PROJECT KEYWORDS (selected from those available):
oncology; fitness training; wearable sensors; Physical Activities; statistical inference.
LICENCE (selected from those available):
GPL 3.0+