sensors-logo

Journal Browser

Journal Browser

Data Analytics and Applications of the Wearable Sensors

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6891

Special Issue Editors

Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: data analytics; wearable sensors; software engineering; data mining
Research and Development Department, Motsai, Saint Bruno, QC J3V 6B7, Canada
Interests: Internet of Things; smart sensors; sensor fusion; sensor data analytics

Special Issue Information

Dear Colleagues,

Wearable sensors are transforming the way we live our lives. These sensors are playing an increasingly important role in numerous applications, including health care, sports, health, and transportation. The successful application of wearable sensors hinges on the ability to extract, interpret, and present the data they generate in a useful manner.

Hence, there is an increasing need for methods, techniques, applications, and studies that advance the harnessing of useful information from wearable sensors. The goal of this Special Issue is to bring together scientists, researchers, practitioners, and providers in order to publish high-quality manuscripts related to data analytics and the application of wearable sensors. Topics of interest include

  • Data curation and preprocessing techniques to support applications of wearable sensors;
  • Algorithms for applications of wearable sensors;
  • Novel wearable sensor platforms;
  • Frameworks that support effective deployment of wearable sensor solutions;
  • Novel application-specific and general data analytics for wearable sensors;
  • Case studies using data analytics in combination with wearable sensors;
  • Reproducing and revisiting common beliefs and studies using data analytics with wearable sensors.

Submitted articles should not have been previously published or currently under review by other journals or conferences/symposia/workshops. Papers previously published as part of conference/workshop proceedings can be considered for publication in the Special Issue provided that they are modified to contain at least 50% new content. Authors of such submissions must clearly indicate how the submitted article extends their prior publication in a separate letter to the Guest Editors at the time of submission. Moreover, authors must acknowledge their previous paper in the manuscript and resolve any potential copyright issues prior to submission.

Dr. Emad Shihab
Dr. Omid Omid Sarbishei
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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • Sensor data analytics
  • Wearable sensor applications
  • Case studies of wearable sensors.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 1268 KiB  
Article
Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning
by Sahand Hajifar, Saeb Ragani Lamooki, Lora A. Cavuoto, Fadel M. Megahed and Hongyue Sun
Sensors 2021, 21(19), 6677; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196677 - 08 Oct 2021
Cited by 3 | Viewed by 1801
Abstract
Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among [...] Read more.
Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors)
Show Figures

Figure 1

22 pages, 5675 KiB  
Article
Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms
by Uzma Abid Siddiqui, Farman Ullah, Asif Iqbal, Ajmal Khan, Rehmat Ullah, Sheroz Paracha, Hassan Shahzad and Kyung-Sup Kwak
Sensors 2021, 21(10), 3319; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103319 - 11 May 2021
Cited by 22 | Viewed by 4386
Abstract
Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild [...] Read more.
Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child’s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors’ data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors)
Show Figures

Figure 1

Back to TopTop