Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Ashraf Ali, Weam Samara, Doaa Alhaddad, Andrew Ware, Omar A. Saraereh

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    Abstract

    Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.
    Original languageEnglish
    Article number1016
    Number of pages18
    JournalSensors
    Volume22
    Issue number3
    DOIs
    Publication statusPublished - 28 Jan 2022

    Keywords

    • human activity recognition
    • CNN (Convolutional Neural Network)
    • sensors
    • machine learning
    • motion pattern
    • Naive Bayes

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