FitnessTracking_DataAuthorizationDocuments

COLLECTION ITEM:Data Authorization Documents<br>COLLECTION TITLE:2020_PiccininiEtAl_FitnessTracking_Video<br>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". 2020.<br>DESCRIPTION OF THE FILES IN THE COLLECTION:Authorization documents to process personal data<br>ITEM TYPE (selected from those available):PDF manually signed.<br>MAIN CONTACT FOR THESE FILES:Dr. Filippo Piccinini, PhD, IRST IRCCS Meldola Italy. Email: [e-mail]<br>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.<br>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.<br>PROJECT CATEGORY (selected from those available):Computer Vision<br>PROJECT KEYWORDS (selected from those available):oncology; fitness training; wearable sensors; physical activities; statistical inference.<br>LICENCE (selected from those available):GPL 3.0+

数据集条目：数据授权文档
数据集标题：2020_Piccinini等人健身追踪视频
引用说明（使用本文件时，请引用以下文献）：Filippo Piccinini、Giovanni Martinelli、Antonella Carbonaro，《移动应用与可穿戴设备在长期步数监测中的精度对比》，2020年。
数据集内文件说明：用于处理个人数据的授权文档。
条目类型（可选值）：手动签署的PDF文件。
本数据集主要联系人：意大利梅尔多拉的罗马涅肿瘤研究与治疗科学研究所（IRST IRCCS）的Filippo Piccinini博士，电子邮箱：[e-mail]。
本项目主要依托单位：1) 意大利梅尔多拉（FC）罗马涅肿瘤研究与治疗科学研究所（IRST IRCCS）；2) 意大利博洛尼亚大学。
项目简介：本项目聚焦当前利用物联网（Internet of Things, IoT）自主采集的健康数据提升民众福祉所面临的挑战与机遇。最新统计数据显示，发达国家约有50%的民众使用可穿戴设备监测健身或身体活动（Physical Activity, PA）。实际上，人们可以以无侵入、零成本的方式持续监测自身健康状态；当前海量的患者自主生成健康数据，也为实时监测生命参数、推动专业人员与患者间的沟通变革带来了全新机遇。所有现代智能手机与健身追踪设备均配备加速度传感器，可记录单平面或多平面的加速度信号，这些数据会被处理为更具实用价值的指标，例如步数、久坐/轻量/中量/高强度身体活动时长，以及攀登楼层数。除了探讨当前健身追踪技术的局限性外，本研究还支持将可穿戴设备应用于移动健康（mobile Health, mHealth）以及与肿瘤相关的癌症预防、治疗与生存随访分析。
项目分类（可选值）：计算机视觉。
项目关键词（可选值）：肿瘤学；健身训练；可穿戴传感器；身体活动；统计推断。
授权协议（可选值）：GPL 3.0+