Human Activity Recognition with Convolutional Neural Networks

  • Antonio BevilacquaEmail author
  • Kyle MacDonald
  • Aamina Rangarej
  • Venessa Widjaya
  • Brian Caulfield
  • Tahar Kechadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR). There exist several techniques to measure motion characteristics during these physical activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone position in this context, and are characterized by usage flexibility, low cost, and reduced privacy impact. With the use of inertial sensors, it is possible to sample some measures such as acceleration and angular velocity of a body, and use them to learn models that are capable of correctly classifying activities to their corresponding classes. In this paper, we propose to use Convolutional Neural Networks (CNNs) to classify human activities. Our models use raw data obtained from a set of inertial sensors. We explore several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures. We also compare the performance of different groups of sensors, investigating the classification potential of single, double and triple sensor systems. The experimental results obtained on a dataset of 16 lower-limb activities, collected from a group of participants with the use of five different sensors, are very promising.


Human activity recognition CNN Deep learning Classification IMU 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonio Bevilacqua
    • 1
    Email author
  • Kyle MacDonald
    • 2
  • Aamina Rangarej
    • 2
  • Venessa Widjaya
    • 2
  • Brian Caulfield
    • 1
  • Tahar Kechadi
    • 1
  1. 1.Insight Centre for Data AnalyticsUCDDublinIreland
  2. 2.School of Public Health, Physiotherapy and Sports ScienceUCDDublinIreland

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