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Physical Human Activity Recognition Using 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 2020) | Viewed by 3163

Special Issue Editors


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Guest Editor
Department of Engineering, Macquarie University, NSW 2109, Australia
Interests: smart sensors; sensing technology; wireless sensor networks; Internet of Things; healthcare and environmental monitoring
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor

Special Issue Information

Human activity recognition (HAR) is dependent on wearable sensor information, and has been an alluring exploration subject because of its application across various entities. Numerous research works have considered HAR outcomes using smart wearable sensing systems. The inescapability and scope of capacities of today’s smart systems have empowered a wide range of versatile applications that are changing our day to day lives, using portable sensors that get physiological information. HAR systems using wearable sensors and artificial intelligence (AI) methods can realize the well-being checking of humans in real-time under varied conditions.  

Authors are invited to submit articles on how to design and implement HAR systems covering the hypothesis, basics, and utilizations of HAR systems. The creators aim to emphasize how AI and HAR devices help to decide on an apt system given a specific timeframe. The investigations should consider significant issues that lead to balanced outcomes, making it easy to evaluate the nature of sensor-based human action, forecasting an immediate correlation of past works to avoid abnormal events. The investigation should handle fundamental issues that bargain the comprehension of the presentation of human action acknowledgment dependent on wearable sensor information.

Prof. Dr. Subhas Chandra Mukhopadhyay
Prof. Dr. Nagender Kumar Suryadevara
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

  • Wearable sensors
  • Human activity recognition
  • Wearable sensor data
  • Pervasive computing
  • Human-centric sensing
  • Machine learning
  • Deep Learning
  • Context awareness
  • Image motion analysis
  • Deep learning
  • Artificial intelligence
  • Mobile computing
  • Wearable computers
  • Smart home
  • Smart environment
  • Cloud computing

Published Papers (1 paper)

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Research

21 pages, 1306 KiB  
Article
On the Impact of Biceps Muscle Fatigue in Human Activity Recognition
by Mohamed Elshafei, Diego Elias Costa and Emad Shihab
Sensors 2021, 21(4), 1070; https://doi.org/10.3390/s21041070 - 4 Feb 2021
Cited by 8 | Viewed by 2473
Abstract
Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, [...] Read more.
Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations. Full article
(This article belongs to the Special Issue Physical Human Activity Recognition Using Wearable Sensors)
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