Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Wearable Sensing and Computing Technologies for Health and Sports

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7174

Share This Special Issue

Special Issue Editors

Prof. Dr. Yuxin Peng

E-Mail Website
Guest Editor

College of Education, Zhejiang University, Hangzhou 310058, China
Interests: wearable sensors; flexible sensors; soft robotics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Li Liu

E-Mail Website
Guest Editor

School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
Interests: big data analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of modern technologies has been key to the development of health and sports industries in recent years. In order to fulfill the increasing demands, researchers have devoted themselves to information acquisition and analysis, with excellent performance. In the data collection, various sensing systems have been widely explored by researchers with the merits of distinguished accuracy, integration, conformability, or stretchability. On the other hand, advanced computing technologies including machine learning, data visualization, finite element analysis, etc. have also found extensive application.

This Special Issue is dedicated to the presentation of novel and creative methods related to wearable sensing and computing technologies in the health and sports area. The Special Issue topics include, but are not limited to:

Wearable sensor design and application;
Wearable-sensor-based machine learning;
Sensor signal processing;
Analysis of the data collected from wearable sensors;
Health and sports status assessment;
Mechanism, modeling, and simulation on health/sports instruments.

Prof. Dr. Yuxin Peng
Prof. Dr. Li Liu
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. Applied Sciences 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 2400 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 design and application
signal processing and analysis
advanced algorithms
machine learning

Published Papers (3 papers)

Download All Papers

Research

Jump to: Review

18 pages, 12067 KiB

Open AccessArticle

A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios

by Xubo Fu, Wenbin Huang, Yaoran Sun, Xinhua Zhu, Julian Evans, Xian Song, Tongyu Geng and Sailing He

Appl. Sci. 2023, 13(9), 5361; https://doi.org/10.3390/app13095361 - 25 Apr 2023

Cited by 1 | Viewed by 1864

Abstract

Localization and tracking in multi-player sports present significant challenges, particularly in wide and crowded scenes where severe occlusions can occur. Traditional solutions relying on a single camera are limited in their ability to accurately identify players and may result in ambiguous detection. To overcome these challenges, we proposed fusing information from multiple cameras positioned around the field to improve positioning accuracy and eliminate occlusion effects. Specifically, we focused on soccer, a popular and representative multi-player sport, and developed a multi-view recording system based on a

1 + N

strategy. This system enabled us to construct a new benchmark dataset and continuously collect data from several sports fields. The dataset includes 17 sets of densely annotated multi-view videos, each lasting 2 min, as well as 1100+ min multi-view videos. It encompasses a wide range of game types and nearly all scenarios that could arise during real game tracking. Finally, we conducted a thorough assessment of four multi-view multi-object tracking (MVMOT) methods and gained valuable insights into the tracking process in actual games. Full article

(This article belongs to the Special Issue Wearable Sensing and Computing Technologies for Health and Sports)

► Show Figures

Figure 1

19 pages, 4837 KiB

Open AccessArticle

Machines Perceive Emotions: Identifying Affective States from Human Gait Using On-Body Smart Devices

by Hamza Ali Imran, Qaiser Riaz, Muhammad Zeeshan, Mehdi Hussain and Razi Arshad

Appl. Sci. 2023, 13(8), 4728; https://doi.org/10.3390/app13084728 - 9 Apr 2023

Cited by 1 | Viewed by 2244

Abstract

Emotions are a crucial part of our daily lives, and they are defined as an organism’s complex reaction to significant objects or events, which include subjective and physiological components. Human emotion recognition has a variety of commercial applications, including intelligent automobile systems, affect-sensitive systems for customer service and contact centres, and the entertainment sector. In this work, we present a novel deep neural network of the Convolutional Neural Network - Bidirectional Gated Recurrent Unit (CNN-RNN) that can classify six basic emotions with an accuracy of above 95%. The deep model was trained on human gait data captured with body-mounted inertial sensors. We also proposed a reduction in the input space by utilizing 1D magnitudes of 3D accelerations and 3D angular velocities (

\hat{m a g_{a}}

\hat{m a g_{ω}}

), which not only minimizes the computational complexity but also yields better classification accuracies. We compared the performance of the proposed model with existing methodologies and observed that the model outperforms the state-of-the-art. Full article

(This article belongs to the Special Issue Wearable Sensing and Computing Technologies for Health and Sports)

► Show Figures

Figure 1

Review

Jump to: Research

20 pages, 463 KiB

Open AccessReview

Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey

by Md Ayshik Rahman Khan, Marat Rostov, Jessica Sharmin Rahman, Khandaker Asif Ahmed and Md Zakir Hossain

Appl. Sci. 2023, 13(1), 387; https://doi.org/10.3390/app13010387 - 28 Dec 2022

Cited by 1 | Viewed by 2354

Abstract

Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we subdivided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the individual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring. Full article

(This article belongs to the Special Issue Wearable Sensing and Computing Technologies for Health and Sports)

► Show Figures