A Mobile Biomedical Device by Novel Antenna Technology for Cloud Computing Resource toward Pervasive Healthcare

Chen, Ke-Ren; Lin, Yu-Lun; Huang, Mu-Sheng

doi:10.1109/bibe.2011.29

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Furthermore, the system transmits sensor data to a healthcare management centre for further investigations. Chen et al [10] presented pervasive healthcare monitoring system (PHMS) combined with cloud computing environment, mobile application, and planar super wideband (SWB). The PHMSs can deliver facilities for those who need long term and continuous collection of data, in particular disabled and elderly people for living independent life.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease

Khalaf

Hussain

Al‐Jumeily

et al. 2015

Intelligent Computing Theories and Methodologies

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Abstract. Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with realtime health monitoring and remote/personals health-care. Healthcare organisations are still required to pay more attention for some improvements in terms of cost-effectiveness and maintaining efficiency, and avoid patients to take admission at hospital. Sickle cell disease (SCD) is one of the most challenges chronic obtrusive disease that facing healthcare, affects a large numbers of people from early childhood. Currently, the vast majority of hospitals and healthcare sectors are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful. This work proposes an alert system that could send instant information to the doctors once detects serious condition from the collected data of the patient. In addition, this work offers a system that can analyse datasets automatically in order to reduce error rate. A machine-learning algorithm was applied to perform the classification process. Two experiments were conducted to classify SCD patients from normal patients using machine learning algorithm in which 99% classification accuracy was achieved using the Instance-based learning algorithm.

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Section: Introductionmentioning

confidence: 99%