Title: | Real-Time Visual Analytics for Remote Monitoring of Patient’s Health |
Authors: | Boumrah, Maryam Garbaya, Samir Radgui, Amina |
Citation: | WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 368-378. |
Issue Date: | 2023 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://hdl.handle.net/11025/54445 |
ISBN: | 978-80-86943-32-9 |
ISSN: | 2464–4617 (print) 2464–4625 (CD/DVD) |
Keywords: | vizuální analýza;vzdálené sledování pacienta;mozková mrtvice;rehabilitace;design zaměřený na uživatele;Kafka |
Keywords in different language: | visual analysis;patient remote monitoring;brain stroke;rehabilitation;user-centered design;Kafka |
Abstract in different language: | The recent proliferation of advanced data collection technologies for Patient Generated Health Data (PGHD) has made remote health monitoring more accessible. However, the complex nature of the big volume of medical generated data presents a significant challenge for traditional patient monitoring approaches, impeding the effective extraction of useful information. In this context, it is imperative to develop a robust and cost-effective framework that provides the scalability and deals with the heterogeneity of PGHD in real-time. Such a system could serve as a reference and would guide future research for monitoring patient undergoing a treatment at home conditions. This study presents a real-time visual analytics framework offering insightful visual representations of the multimodal big data. The proposed system was designed following the principles of User Centered Design (UCD) to ensure that it meets the needs and expectations of medical practitioners. The usability of this framework was evaluated by its application to the visualization of kinematic data of the upper limbs’ movement of patients during neuromotor rehabilitation exercises. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG 2023: Full Papers Proceedings |
Files in This Item:
File | Description | Size | Format | |
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G53-full.pdf | Plný text | 4,13 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/54445
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