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PracticalHAR: A Practical Method of Human Activity Recognition Based on Embedded Sensors of Mobile Phone

Published: 18 April 2024 Publication History

Abstract

Human Activity Recognition (HAR) is the process of identifying human activity states based on collected data, with applications in health monitoring, rehabilitation therapy, and smart homes. However, accurate recognition often necessitates the use of multiple sensors, which can cause discomfort and significantly reduce practicality and convenience. Currently, smartphones with embedded sensors have broadened the potential for HAR, enabling healthcare monitoring, life logging, fitness tracking, and more. This paper conducts experiments on the WISDM public dataset and a self-collected dataset, demonstrating that a one-dimensional convolutional neural network structure does not necessarily underperform a two-dimensional one, using only accelerometer data. Moreover, the experimental results on the self-collected dataset suggest that utilizing the barometer built into smartphones can enhance activity recognition rates. Specifically, we first built the corresponding convolutional neural network model and conducted experiments on the WISDM dataset, and the accuracy reached 95.42%. Next, we collected data from the smartphone’s embedded barometer and accelerometer to construct a dataset. Experimental results on our self-collected dataset show that the inclusion of barometer data can improve the accuracy of activity recognition, especially stair-related activities. The F1-score for identifying walking, ascending stairs, and descending stairs increased by 6.7%, 7.39%, and 2.99% respectively.

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  1. PracticalHAR: A Practical Method of Human Activity Recognition Based on Embedded Sensors of Mobile Phone

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    ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security
    December 2023
    363 pages
    ISBN:9798400707964
    DOI:10.1145/3638782
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 18 April 2024

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    Author Tags

    1. accelerometer
    2. barometer
    3. convolutional neural network
    4. embedded sensor
    5. human activity recognition
    6. smartphone

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