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Advances in Motion Monitoring System
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Advances in Motion Monitoring System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 3864

Special Issue Editor

Dr. Tianming Zhao

E-Mail Website
Guest Editor
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Interests: invasive brain–computer interfaces; implantable electronics; biosensing; human–computer intelligent interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Motion data collection during sports is crucial for athletes, coaches, and healthcare professionals to gather valuable information. Detailed data on athletes and their workout habits is vital for improving training programs and optimizing athletic performance. The recent surge in the use of motion monitoring devices has driven technological advancements in this field, offering innovative solutions. Motion monitoring systems integrate these devices with advanced back-end processing solutions to expedite the analysis and identification of exercise data, streamlining the motion monitoring process. This Special Issue aims to present a comprehensive examination of the latest developments in motion monitoring systems, encompassing technological innovations, practical applications, and future prospects. The Editorial Board welcomes researchers from various fields and disciplines to submit original research, reviews, case studies, and technical reports.

Dr. Tianming Zhao
Guest Editor

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

  • motion monitoring system
  • data analysis
  • sports science
  • training optimization
  • triboelectric nanogenerator
  • self-powered
  • flexible
  • intelligent system

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Published Papers (3 papers)

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Research

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13 pages, 1495 KiB  
Article
Accuracy of Measurement Tools for Ocular-Origin Anomalous Head Posture and the Cervical Range of Motion Kinematics in Children with an Anomalous Head Position
by Serena Cerfoglio, Francesco Bonsignore, Giuseppina Bernardelli, Lucia Donno, Fabiana Aili, Manuela Galli, Francesca Nucci, Edoardo Villani, Paolo Nucci and Veronica Cimolin
Appl. Sci. 2025, 15(7), 3642; https://doi.org/10.3390/app15073642 - 26 Mar 2025
Viewed by 203
Abstract
The accurate assessment of anomalous head posture (AHP) is crucial for diagnosing, treating, and monitoring postural changes in individuals with ocular impairments. This study evaluated the accuracy of a digital goniometer and an iOS-based application by comparing their measurements to a gold-standard motion [...] Read more.
The accurate assessment of anomalous head posture (AHP) is crucial for diagnosing, treating, and monitoring postural changes in individuals with ocular impairments. This study evaluated the accuracy of a digital goniometer and an iOS-based application by comparing their measurements to a gold-standard motion capture (MoCap) system. Additionally, it assessed cervical range of motion (ROM) limitations in children with AHP versus healthy controls. Fifteen pediatric patients with ocular-origin AHP and 20 age-matched controls participated. Head rotation and inclination were measured using a goniometer, the iOS app, and MoCap under static and dynamic conditions. Pearson’s correlation coefficient (PCC), root mean square error (RMSE), and Bland–Altman plots assessed inter-system agreement, while MoCap analyzed cervical ROM. The results showed strong agreement between the ophthalmological tools and MoCap for head rotation (PCC = 0.86, RMSE = 3.43°) and inclination (PCC = 0.82, RMSE = 5°), with no significant inter-system differences (p > 0.05). AHP patients exhibited reduced head flexion (p < 0.05), suggesting long-term postural adaptations. Digital goniometers and smartphone applications provide accurate, cost-effective AHP assessment alternatives, particularly in resource-limited settings. Future research should expand cohorts and integrate multidisciplinary approaches to refine assessment and treatment strategies. Full article
(This article belongs to the Special Issue Advances in Motion Monitoring System)
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15 pages, 13255 KiB  
Article
AI-Based Analysis of Archery Shooting Time from Anchoring to Release Using Pose Estimation and Computer Vision
by Seungkeon Lee, Ji-Yeon Moon, Jinman Kim and Eui Chul Lee
Appl. Sci. 2024, 14(24), 11838; https://doi.org/10.3390/app142411838 - 18 Dec 2024
Viewed by 1517
Abstract
This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase [...] Read more.
This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete’s facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an R2 score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an R2 score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement. Full article
(This article belongs to the Special Issue Advances in Motion Monitoring System)
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Review

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16 pages, 5337 KiB  
Review
Advances in Wearable Smart Chemical Sensors for Health Monitoring
by Ning Ba, Wen Yue, Chunmei Cao, Wei Wu and Panpan Cheng
Appl. Sci. 2024, 14(23), 11199; https://doi.org/10.3390/app142311199 - 1 Dec 2024
Cited by 1 | Viewed by 1875
Abstract
The advancement of wearable technology has entered a new phase, leading to the creation of various wearable sensors due to the rise of technologies like IoT and AI. Wearable chemical sensors are essential components of wearable electronics and hold significant promise in monitoring [...] Read more.
The advancement of wearable technology has entered a new phase, leading to the creation of various wearable sensors due to the rise of technologies like IoT and AI. Wearable chemical sensors are essential components of wearable electronics and hold significant promise in monitoring health. This review reports on the recent achievements and advantages of portable smart chemical sensing for health monitoring and discusses portable chemical sensing using frictional/piezoelectric electrochemical generators, photovoltaics and thermal power accumulators. This paper also evaluates the potential of wearable chemical sensors for health monitoring. Full article
(This article belongs to the Special Issue Advances in Motion Monitoring System)
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