Special Issue "Human Activity Recognition and Movement Analysis on Smartphones and Personal Devices"

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Dr. Andrea Mannini
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Guest Editor
The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
Interests: wearable sensors; machine learning; activity recognition; inertial sensors; movement analysis; gait parameters estimation; automatic early detection of gait alterations; sports bioengineering; mobile health
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Dr. Raffaele Gravina
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Guest Editor
Department of Informatics, Electronics, Modelling and Systems, University of Calabria, Via P. Bucci, 41C, Arcavacata di Rende, Rende 87036, Italy
Interests: high-level programming methodologies and frameworks for body sensor networks; collaborative and cloud-assisted body sensor networks; pattern recognition and knowledge discovery algorithms on physiological signals; human activity recognition; ECG analysis; emotion recognition; interoperability on the Internet-of-Things
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Dr. Angelo Maria Sabatini
Website
Guest Editor
Scuola Superiore Sant'Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: wearable sensor systems for human motion capture; magneto-inertial measurement units; computational methods for wearable sensor systems; multisensor fusion
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the widespread availability of personal devices including smartphones and smartwatches with embedded sensing sources and high-performance computational units is driving the development of solutions for the automatic recognition and analysis of human movement. This opens scenarios for the large-scale self-assessment of natural movement for everyday life activities at every age, including sport and fitness applications. Additional applications could span from the early detection of movement disorders using personal devices to human–robot interactions, passing through fall detection/fall risk assessment in the elderly, rehabilitation tracking and treatment assessment using smartphone-embedded sensors or sensing devices connected to it and to the cloud.

Activity recognition and activity analysis methodologies, after two decades of continuous improvements, are now capable of extracting reliable information about the activity being performed at different levels of detail, but the great advantage of using personal devices lies in the capability to track fully naturalistic conditions, and ecological ways of moving.

In such a context, there are still many challenges to face in order to make activity recognition and analysis capable of reliably dealing with the huge variability of out-of-the-lab conditions. Challenges span from the definition of new computational strategies for inertial pre-processing sensors to adaptive and personalized classification tools. In addition, the challenging concept of group/multi-user activity is emerging, which requires novel collaborative, context-aware strategies to achieve high recognition results.

Potential topics include, but are not limited to, the following:

  • Activity recognition methods
  • Activity annotation tools for naturalistic conditions
  • Personalized or adaptive solutions for activity recognition
  • Computational methods for movement analysis
  • Sensor fusion and machine learning methods for movement recognition and analysis
  • Collaborative approaches for multi-user activity recognition
  • Smart device based implementation of movement recognition and analysis methods
  • Health self-assessment using mobile and wearable technologies
  • Recognize/analyze gestures using smart devices (activities of daily living, sports, body language)
  • Activity recognition and analysis to track rehabiltation or therapy treatment in everyday life
  • Early detection of movement alterations
  • New hardware connected to portable devices for quantified-self applications
  • Big data and cloud-based solutions for movement recognition and analysis
  • Information management for mobile device based movement recognition and analysis

Dr. Andrea Mannini
Dr. Raffaele Gravina
Prof. Angelo Maria Sabatini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart and personal devices
  • Activity recognition
  • Movement analysis
  • Machine learning
  • Sensor fusion
  • m-Health

Published Papers (2 papers)

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Research

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Open AccessArticle
A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit
Information 2019, 10(10), 290; https://doi.org/10.3390/info10100290 - 21 Sep 2019
Cited by 1
Abstract
The purpose of activity recognition is to identify activities through a series of observations of the experimenter’s behavior and the environmental conditions. In this study, through feature selection algorithms, we researched the effects of a large number of features on human activity recognition [...] Read more.
The purpose of activity recognition is to identify activities through a series of observations of the experimenter’s behavior and the environmental conditions. In this study, through feature selection algorithms, we researched the effects of a large number of features on human activity recognition (HAR) assisted by an inertial measurement unit (IMU), and applied them to smartphones of the future. In the research process, we considered 585 features (calculated from tri-axial accelerometer and tri-axial gyroscope data). We comprehensively analyzed the features of signals and classification methods. Three feature selection algorithms were considered, and the combination effect between the features was used to select a feature set with a significant effect on the classification of the activity, which reduced the complexity of the classifier and improved the classification accuracy. We used five classification methods (support vector machine [SVM], decision tree, linear regression, Gaussian process, and threshold selection) to verify the classification accuracy. The activity recognition method we proposed could recognize six basic activities (BAs) (standing, going upstairs, going downstairs, walking, lying, and sitting) and postural transitions (PTs) (stand-to-sit, sit-to-stand, stand-to-lie, lie-to-stand, sit-to-lie, and lie-to-sit), with an average accuracy of 96.4%. Full article
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Review

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Open AccessReview
Human Activity Recognition for Production and Logistics—A Systematic Literature Review
Information 2019, 10(8), 245; https://doi.org/10.3390/info10080245 - 24 Jul 2019
Abstract
This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding [...] Read more.
This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner’s perspective. Full article
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