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  • Open Access


    TinyML-Based Classification in an ECG Monitoring Embedded System

    Eunchan Kim1, Jaehyuk Kim2, Juyoung Park3, Haneul Ko4, Yeunwoong Kyung5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1751-1764, 2023, DOI:10.32604/cmc.2023.031663

    Abstract Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and… More >

  • Open Access


    TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

    Ramon Sanchez-Iborra1, Luis Bernal-Escobedo2, Jose Santa3,*, Antonio Skarmeta2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3869-3885, 2022, DOI:10.32604/cmc.2022.022610

    Abstract A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful ML algorithms in constrained units.… More >

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