e-Infrastructure and e-Services for Developing Countries. 10th EAI International Conference, AFRICOMM 2018, Dakar, Senegal, November 29-30, 2019, Proceedings

Research Article

Cyber-Healthcare Kiosks for Healthcare Support in Developing Countries

  • @INPROCEEDINGS{10.1007/978-3-030-16042-5_18,
        author={Mukuzo Bagula and Herman Bagula and Munyaradzi Mandava and Claude Kakoko Lubamba and Antoine Bagula},
        title={Cyber-Healthcare Kiosks for Healthcare Support in Developing Countries},
        proceedings={e-Infrastructure and e-Services for Developing Countries. 10th EAI International Conference, AFRICOMM 2018, Dakar, Senegal, November 29-30, 2019, Proceedings},
        proceedings_a={AFRICOMM},
        year={2019},
        month={3},
        keywords={Cyber-healthcare Internet-of-Things Patient condition recognition Disease identification Patient prioritisation},
        doi={10.1007/978-3-030-16042-5_18}
    }
    
  • Mukuzo Bagula
    Herman Bagula
    Munyaradzi Mandava
    Claude Kakoko Lubamba
    Antoine Bagula
    Year: 2019
    Cyber-Healthcare Kiosks for Healthcare Support in Developing Countries
    AFRICOMM
    Springer
    DOI: 10.1007/978-3-030-16042-5_18
Mukuzo Bagula1, Herman Bagula1, Munyaradzi Mandava2, Claude Kakoko Lubamba2, Antoine Bagula2,*
  • 1: University of Cape Town
  • 2: University of the Western Cape
*Contact email: abagula@uwc.ac.za

Abstract

Cyber-healthcare can be described to be virtual medicine applied in reality. It involves the use of healthcare professionals consulting and treating patients via the internet and other modern communication platforms and using different techniques and devices of the Internet-of-Things (IoT) to automate manual processes. This paper aims to revisit cyber-healthcare and its applications in the health sector in the developing countries with the expectation of (i) assessing the field-readiness of emerging bio-sensor devices through a cross-sectional pilot study that benchmark the arduino sensors against manually captured vital signs using calibrated devices and (ii) comparing unsupervised and supervised machine learning techniques when used in Triage systems to prioritise patients.