Vehicle Activity Recognition (VAR) involves identifying changes in a vehicle's behavior within a fixed time frame under specific conditions. Powered Two-Wheeler (PTW) fall detection is a critical VAR problem, where the goal is to monitor the vehicle and detect fall events using various sensors and cameras. However, the high risks associated with collecting real-world PTW fall data has resulted in limited amount of available data for building accurate recognition models. In this work, we leverage the publicly accessible datasets created especially for PTW fall detection. This dataset contain accelerometer and gyrometer data recorded over a certain time interval. We have labeled the data into four classes. Many deep learning and machine learning techniques have been used in our analysis. Of the evaluated techniques, Random Forest exhibited the highest efficacy in accurately identifying fall incidents, highlighting its potential for effective PTW fall detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Accelerometer-Based Two-Wheeler Fall Detection Analysis


    Beteiligte:


    Erscheinungsdatum :

    17.12.2024


    Format / Umfang :

    853963 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Fall event detection by gyroscopic and accelerometer sensors in smart phone

    Wu, Yung-Gi / Tsai, Sheng-Lun | British Library Online Contents | 2015


    Fall event detection by gyroscopic and accelerometer sensors in smart phone

    Wu, Yung-Gi / Tsai, Sheng-Lun | British Library Online Contents | 2015


    Helmet Detection in 2-Wheeler

    D, Bhavanash Rai | SAE | 2024


    TWO-WHEELER

    NEIL P QUADE / JOSH L EDEL / JOHN E FELDMAN et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    THREE-WHEELER

    KANEGA HISAYOSHI | Europäisches Patentamt | 2016

    Freier Zugriff