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  • 1.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An Intelligent Healthcare Service to Monitor Vital Signs in Daily Life – A Case Study on Health-IoT2017In: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, no 3, 43-55 p.Article in journal (Refereed)
    Abstract [en]

    Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons’ health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS’s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation result demonstrates the feasibility and performance of the implemented system through the proposed framework.

  • 2.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    In-Vehicle Stress Monitoring Based on EEG Signal2017In: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, no 7, 55-71 p.Article in journal (Refereed)
    Abstract [en]

    In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers’ stress monitoring system based on electroencephalography (EEG) signals enabling individual-focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

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