地理数据生产系统

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-04-01 DOI:10.1016/j.irbm.2022.100744
A. Patarot , N. Samama
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引用次数: 0

摘要

人们的幸福在一定程度上取决于自由感,其中一个方面当然是人们留在家里的可能性。然而,有必要“跟随”这个人的动作,如果可能的话,还需要“跟随”他的活动。问题是,很少有家庭系统能够以合理的价格和可接受的可靠性水平获得这些数据。我们提供一个简单易用、可靠且能源自给自足的人员定位系统。人是第一个“目标”,但物体也可能参与其中。材料和方法描述了该系统,并在实际使用条件下分析了其性能。对定位算法进行了说明,并描述了实际实现。结果首先给出了一个人在家活动的结果,以及可以考虑的数据处理类型的一些轨迹。展示了部署的简单性,并在老年人的家庭护理以及医院设备监测的背景下讨论了可用数据的有用性。结论我们的方法实现简单,实时可靠性高,而不是在所有情况下都追求高准确性。从概念上考虑到室内无线电测量的高度可变性,可以显著提高所产生的地理数据的可靠性。此外,我们将提到两次真正的部署和获得的相关表现,一方面是为了跟随一位独自生活在家中的老人的行为,另一方面是跟随法国一家医院急诊科的担架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Geodata Production System

Objectives

The well-being of people depends in part on the sense of freedom, and one aspect is certainly the possibility for people to remain at home. However, there is a need for “following” the movements and, if possible, the activity of the person. The problem is that very few home systems make it possible to have these data at a reasonable price, and at an acceptable reliability level. We offer a simple to use, reliable and energy self-sufficient person location system. People are the first “targets”, but objects could be involved.

Material and methods

The system is described and their performance analyzed in real conditions of use. The positioning algorithms are explained and the practical implementations described.

Results

First results on the activity of a person at home are presented as well as some tracks on the type of data processing that could be considered.

The simplicity of deployment is shown and the usefulness of the available data is discussed in the context of home care of an elderly person as well as the monitoring of hospital equipment.

Conclusion

Our approach provides simplicity of implementation and very high reliability in real time, without aiming for high accuracy in all cases. Conceptually taking into account the high variability of indoor radio measurements makes it possible to significantly increase the reliability of the geo-data produced. Moreover, we will mention two real deployments and the associated performances obtained, carried out in order to follow the behavior of an old autonomous man living alone at home, and in another hand to follow the stretchers of the emergency department of a French hospital.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
期刊最新文献
Editorial Board Contents Potential of Near-Infrared Optical Techniques for Non-invasive Blood Glucose Measurement: A Pilot Study Corrigendum to “Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models” [IRBM (2023) 100725] Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
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