Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Sections
Sensors and AI for Movement Analysis
sensors-logo
Submit to Topical Collection Review for Sensors

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors and AI for Movement Analysis

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Wearables".

Viewed by 13189

Editor

Dr. Stefano Rossi

E-Mail Website
Collection Editor
Department of Economics, Engineering, Society and Business Organization, University of Tuscia, 01100 Viterbo, Italy
Interests: biomechanical engineering; motion analysis; gait analysis; rehabilitation engineering; rehabilitation robotics; sensors for rehabilitation
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear colleagues,

Movement analysis is currently one of the more attractive research fields and covers several applications from clinics to sports, as well as robotics and industry. In recent decades, the introduction of sensor-based systems to quantitatively perform movement analyses represented a breaking point among researchers, allowing them to overcome the limitations associated with the previous subjective methodologies. The most widespread sensors are optoelectronic systems, force platforms, inertial sensors, physiological sensors, probes for electromyography, and others, which permit one to fully understand the mechanism related to different motor tasks. In recent years, artificial intelligence has been introduced in movement analysis as a further tool to provide useful information with an automatic approach, also thanks to the increasing availability of large open datasets obtained through quantitative human motion analysis. It is clear that constant technological improvements have led to an ever-increasing number of possible innovative studies in this field.

Thus, this Topical Collection aims to promote innovative studies based on the application of sensors and AI for movement analysis in several fields, such as clinics, sports, robotics, and industry; the implementation of innovative methodologies for data analysis; the design of innovative sensors; and the publication of open databases for motion analysis.

Prof. Dr. Stefano Rossi
Collection 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 collection 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

  • movement analysis
  • wearable sensors
  • artificial intelligence
  • experimental biomechanics
  • kinematics
  • kinetics
  • posturography
  • muscle activities
  • sensor-based system

Published Papers (5 papers)

Download All Papers

2024

Jump to: 2022, 2021

15 pages, 4018 KiB  
Article
Measurement of 3D Wrist Angles by Combining Textile Stretch Sensors and AI Algorithm
by Jae-Ha Kim, Bon-Hak Koo, Sang-Un Kim and Joo-Yong Kim
Sensors 2024, 24(5), 1685; https://doi.org/10.3390/s24051685 - 05 Mar 2024
Viewed by 528
Abstract
The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily [...] Read more.
The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily produced by immersing an E-band in a SWCNT solution. The lightweight, cost-effective, and reproducible nature of textile stretch sensors makes them well suited for practical applications in clothing. In this paper, wrist angles were measured by attaching textile stretch sensors to an arm sleeve. Three sensors were utilized to measure all three axes of the wrist. Additionally, sensor precision was heightened through the utilization of the Multi-Layer Perceptron (MLP) technique, a subtype of deep learning. Rather than fixing the measurement values of each sensor to specific axes, we created an algorithm utilizing the coupling between sensors, allowing the measurement of wrist angles in three dimensions. Using this algorithm, the error angle of wrist angles measured with textile stretch sensors could be measured at less than 4.5°. This demonstrated higher accuracy compared to other soft sensors available for measuring wrist angles. Full article
►▼ Show Figures

Figure 1

2022

Jump to: 2024, 2021

13 pages, 1540 KiB  
Article
Prediction of Stability during Walking at Simulated Ship’s Rolling Motion Using Accelerometers
by Jungyeon Choi, Brian A. Knarr, Yeongjin Gwon and Jong-Hoon Youn
Sensors 2022, 22(14), 5416; https://doi.org/10.3390/s22145416 - 20 Jul 2022
Cited by 2 | Viewed by 1226
Abstract
Due to a ship’s extreme motion, there is a risk of injuries and accidents as people may become unbalanced and be injured or fall from the ship. Thus, individuals must adjust their movements when walking in an unstable environment to avoid falling or [...] Read more.
Due to a ship’s extreme motion, there is a risk of injuries and accidents as people may become unbalanced and be injured or fall from the ship. Thus, individuals must adjust their movements when walking in an unstable environment to avoid falling or losing balance. A person’s ability to control their center of mass (COM) during lateral motion is critical to maintaining balance when walking. Dynamic balancing is also crucial to maintain stability while walking. The margin of stability (MOS) is used to define this dynamic balancing. This study aimed to develop a model for predicting balance control and stability in walking on ships by estimating the peak COM excursion and MOS variability using accelerometers. We recruited 30 healthy individuals for this study. During the experiment, participants walked for two minutes at self-selected speeds, and we used a computer-assisted rehabilitation environment (CAREN) system to simulate the roll motion. The proposed prediction models in this study successfully predicted the peak COM excursion and MOS variability. This study may be used to protect and save seafarers or passengers by assessing the risk of balance loss. Full article
►▼ Show Figures

Figure 1

16 pages, 2534 KiB  
Article
Measuring Kinematic Response to Perturbed Locomotion in Young Adults
by Juri Taborri, Alessandro Santuz, Leon Brüll, Adamantios Arampatzis and Stefano Rossi
Sensors 2022, 22(2), 672; https://doi.org/10.3390/s22020672 - 16 Jan 2022
Cited by 4 | Viewed by 2086
Abstract
Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed [...] Read more.
Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed three tasks on a treadmill consisting of one unperturbed and two perturbed walking tests. Inertial measurement units were used to gather lower limb kinematics. Parameters related to joint angles, as the range of motion (ROM) and its variability (CoV), as well as the inter-joint coordination in terms of continuous relative phase (CRP) were computed. The AP perturbation seemed to be more challenging causing differences with respect to normal walking in both the variability of the ROM and the CRP amplitude and variability. As ML, only the ankle showed different behavior in terms of joint angle and CRP variability. In both tasks, a shortening of the stance was found. The findings should be considered when implementing perturbed rehabilitative protocols for falling reduction. Full article
►▼ Show Figures

Figure 1

2021

Jump to: 2024, 2022

22 pages, 8990 KiB  
Article
Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data
by Pu Yanan, Yan Jilong and Zhang Heng
Sensors 2021, 21(19), 6685; https://doi.org/10.3390/s21196685 - 08 Oct 2021
Cited by 5 | Viewed by 2830
Abstract
Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions [...] Read more.
Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training. Full article
►▼ Show Figures

Figure 1

17 pages, 1797 KiB  
Article
A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players
by Juri Taborri, Luca Molinaro, Adriano Santospagnuolo, Mario Vetrano, Maria Chiara Vulpiani and Stefano Rossi
Sensors 2021, 21(9), 3141; https://doi.org/10.3390/s21093141 - 30 Apr 2021
Cited by 27 | Viewed by 5185
Abstract
Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with [...] Read more.
Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury. Full article
►▼ Show Figures

Figure 1

Back to TopTop