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Machine Learning in Human Activity Recognition
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Machine Learning in Human Activity Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 7906

Special Issue Editors

Prof. Dr. Thomas Ploetz

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Guest Editor
College of Computing, Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, USA
Interests: human activity recognition; computational behavior analysis; health informatics; machine learning; representation learning
Dr. Yu Guan

E-Mail Website
Guest Editor
School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: human activity recognition; computational behavior analysis; health informatics; machine learning
Prof. Dr. Daniel Roggen

E-Mail Website
Guest Editor
Sensor Technology Research Centre, University of Sussex, Sussex House, Brighton BN1 9RH, UK
Interests: human activity recognition; wearable computing; sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human Activity Recognition (HAR) using pervasive and body-worn sensors has become a major research field with numerous practical applications. At the heart of most HAR systems lies the automated analysis of sensor readings, for which machine learning techniques are typically applied. With the explosion of research in the core machine learning area, numerous methods have been developed that are also of value for the HAR community. However, HAR comes with its own challenges for machine learning methods, such as challenging data quality, including sensor noise, faulty sensor readings, or ambiguities; often only very small datasets come with ground truth annotation; computational challenges for performing activity recognition in real time and on severely resource constrained devices; open ended activity recognition; and continuous adaptation of recognition systems, to name but a few. This Special Issue aims to provide an overview of the state-of-the-art and latest developments in the field of machine learning for human activity recognition. 

Prof. Dr. Thomas Ploetz
Dr. Yu Guan
Prof. Dr. Daniel Roggen
Guest Editors

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Keywords

  • Machine learning
  • Pervasive and ubiquitous computing
  • Wearable computing
  • Human computer interaction
  • Time series analysis
  • Unsupervised learning
  • Deep learning
  • Model adaptation
  • Novel application
  • Recognition paradigms

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Published Papers (1 paper)

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Research

26 pages, 2717 KiB  
Article
Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables
by Hyeokhyen Kwon, Gregory D. Abowd and Thomas Plötz
Sensors 2021, 21(24), 8337; https://doi.org/10.3390/s21248337 - 13 Dec 2021
Cited by 16 | Viewed by 6573
Abstract
Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of [...] Read more.
Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR. Full article
(This article belongs to the Special Issue Machine Learning in Human Activity Recognition)
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