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Real-time visual analytics for in-home medical rehabilitation of stroke patient—systematic review

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Abstract

This paper is focused on real-time visual analytics for home-based rehabilitation dedicated for brain stroke survivors. This research is at the intersection of three main domains: visual analytics for time-oriented data and dynamic visual analytics with specific focus on data analytics for rehabilitation systems. This study has emphasized the analysis of the most important research works in these domains. The studies included in this review are published between January 2008 and December 2020 that met eligibility criteria. From 243 papers retrieved from research including the Google Scholar database and manual research, 69 papers were finally included. This paper presents a classification of the reviewed research based on key features required by the visual analytics for real-time monitoring of patients. The findings suggested that real-time monitoring visual analytics for biodata captured during the rehabilitation sessions was not sufficiently addressed by previous research. To provide real-time monitoring visual analytics of biodata, the concept of a unified framework that combines the processing of batch and stream data in a distributed architecture is proposed. The system is currently under development; its validation will be carried out by an experimental study and the evaluation of the system performance.

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Authors and Affiliations

  1. Centre d’études doctorales Télécoms et Technologies de l’Information (CEDOC-2TI), INPT, Rabat, Morocco

    Maryam Boumrah & Amina Radgui

  2. Arts et Metiers Institute of Technology, CNAM, LIFSE, END-ICAP-INSERM U1179, HESAM University, F-75013, Paris, France

    Samir Garbaya

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Boumrah, M., Garbaya, S. & Radgui, A. Real-time visual analytics for in-home medical rehabilitation of stroke patient—systematic review. Med Biol Eng Comput 60, 889–906 (2022). https://doi.org/10.1007/s11517-021-02493-w

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