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Related Concept Videos

Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Design and Analysis for Fall Detection System Simplification
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RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in

Shuenn-Yuh Lee, Ming-Yueh Ku, Yen-Hsing Tsai

    IEEE Transactions on Biomedical Circuits and Systems
    |August 13, 2024
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    Summary

    This study introduces a real-time epilepsy detection and personalization algorithm using a novel RISC-V deep learning accelerator hardware architecture. The system achieves high accuracy and enables on-device monitoring for personalized seizure detection.

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    Area of Science:

    • Neurology
    • Computer Engineering
    • Artificial Intelligence

    Background:

    • Epilepsy is a critical neurological disorder requiring real-time detection and treatment solutions.
    • Wearable devices offer potential for continuous epilepsy monitoring, but personalization of detection algorithms remains a challenge.
    • Existing hardware architectures for seizure detection lack personalization capabilities.

    Purpose of the Study:

    • To develop a real-time seizure detection and personalization algorithm.
    • To design a programmable RISC-V deep learning accelerator hardware architecture (RVDLAHA).
    • To create a dedicated RISC-V DLA (RVDLA) compiler for enhanced functionality.

    Main Methods:

    • A convolutional neural network (CNN)-based algorithm was developed for seizure detection and personalization.
    • A novel RVDLAHA was designed and implemented on Xilinx PYNQ-Z2.
    • A dedicated RVDLA compiler was created to support the hardware architecture.

    Main Results:

    • The CNN algorithm achieved 99.5% accuracy (32-bit float) and 99.3% accuracy (16-bit fixed point) in animal experiments.
    • The personalization algorithm improved testing accuracy from 85.0% to 92.9% across databases.
    • The RVDLAHA demonstrated low power consumption (0.107 W at 1 MHz) and fast processing times (detection: 1.1 ms, personalization: 1.3 ms).

    Conclusions:

    • The proposed system enables on-device, real-time seizure detection and personalization.
    • The RVDLAHA and compiler offer a powerful platform for personalized epilepsy management.
    • This integrated approach addresses critical challenges in clinical applications for epilepsy care.