Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10808

Special Issue Editors

Dr. Sándor Miklós Szilágyi

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Str. Nicolae Iorga, Nr. 1, 540088 Targu Mures, Romania
Interests: sensors; sports safety; digital image processing; patterns recognition
Dr. László Szilágyi

E-Mail Website
Guest Editor
Physiological Controls Research Center, Óbuda University, 1034 Budapest, Hungary
Interests: computer science; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Ignacio Cuesta Vargas

E-Mail Website
Guest Editor
1. Department of Physiotherapy, University of Malaga, 29071 Malaga, Spain
2. Biomedical Research Institute of Malaga (IBIMA), 29590 Malaga, Spain
3. Faculty of Health Science, School of Clinical Science, Queensland University Technology, 4000 Brisbane, Australia
Interests: aquatic therapy; exercise; psychometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors, sensing networks, artificial intelligence, and other new and advanced technologies determine the constant and quick changes of sport performances. Sensors can help to predict and prevent injuries in athletes by giving real-time data that can be used to find possible injury risks and take the proper steps to prevent or minimize injuries. Sensors and AI-based models predict and prevent injuries by providing real-time data that can be used to control fatigue, identify injury risks, track recovery progress, and change training programs to reduce the risk of injury. Nowadays, artificial intelligence is becoming a more disruptive technology in sports safety, sports physiotherapy, injury prevention, fatigue, and recovery control by providing performance sports personnel (coaches, trainers, and medical staff) with the tools they need to monitor athletes in real time, identify potential injury risks, and develop personalized training programs and treatment plans to maximize sports safety in a supportive but invisible manner. For this Special Issue, we are looking for articles on monitoring of workload, detection of movement patterns and irregularities, methods of identifying muscle imbalances, approaches to keeping track of the recovery phase, use of next-generation physiotherapy methods, models, and sensors, and concussions from wearable sensors or remote solutions.

Dr. Sándor Miklós Szilágyi
Dr. László Szilágyi
Prof. Dr. Antonio Ignacio Cuesta Vargas
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.

Dr. László Szilágyi
Prof. Dr. Antonio Ignacio Cuesta Vargas
Dr. Sándor Miklós Szilágyi
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

  • predictive modeling
  • injury diagnosis
  • fatigue management
  • biomechanical sensors
  • virtual rehabilitation
  • wearable sensors
  • radar sensors
  • concussion detection
  • smart insoles
  • EMG sensors
  • movement sensors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 4976 KiB  
Article
Advanced Millimeter-Wave Radar System for Real-Time Multiple-Human Tracking and Fall Detection
by Zichao Shen, Jose Nunez-Yanez and Naim Dahnoun
Sensors 2024, 24(11), 3660; https://doi.org/10.3390/s24113660 - 5 Jun 2024
Cited by 1 | Viewed by 2195
Abstract
This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial [...] Read more.
This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
►▼ Show Figures

Figure 1

15 pages, 3204 KiB  
Article
Instrumental Evaluation of the Effects of Vertebral Consolidation Surgery on Trunk Muscle Activations and Co-Activations in Patients with Multiple Myeloma: Preliminary Results
by Barbara Montante, Benedetta Zampa, Luca Balestreri, Rosanna Ciancia, Giorgia Chini, Alberto Ranavolo, Maurizio Rupolo, Zimi Sawacha, Martina Urbani, Tiwana Varrecchia and Mariagrazia Michieli
Sensors 2024, 24(11), 3527; https://doi.org/10.3390/s24113527 - 30 May 2024
Viewed by 657
Abstract
Multiple myeloma (MM) patients complain of pain and stiffness limiting motility. To determine if patients can benefit from vertebroplasty, we assessed muscle activation and co-activation before and after surgery. Five patients with MM and five healthy controls performed sitting-to-standing and lifting tasks. Patients [...] Read more.
Multiple myeloma (MM) patients complain of pain and stiffness limiting motility. To determine if patients can benefit from vertebroplasty, we assessed muscle activation and co-activation before and after surgery. Five patients with MM and five healthy controls performed sitting-to-standing and lifting tasks. Patients performed the task before and one month after surgery. Surface electromyography (sEMG) was recorded bilaterally over the erector spinae longissimus and rectus abdominis superior muscles to evaluate the trunk muscle activation and co-activation and their mean, maximum, and full width at half maximum were evaluated. Statistical analyses were performed to compare MM patients before and after the surgery, MM and healthy controls and to investigate any correlations between the muscle’s parameters and the severity of pain in patients. The results reveal increased activations and co-activations after vertebroplasty as well as in comparison with healthy controls suggesting how MM patients try to control the trunk before and after vertebroplasty surgery. The findings confirm the beneficial effects of vertebral consolidation on the pain experienced by the patient, despite an overall increase in trunk muscle activation and co-activation. Therefore, it is important to provide patients with rehabilitation treatment early after surgery to facilitate the CNS to correctly stabilize the spine without overloading it with excessive co-activations. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
►▼ Show Figures

Figure 1

15 pages, 3017 KiB  
Article
Examining the Effects of Altitude on Workload Demands in Professional Basketball Players during the Preseason Phase
by Sergio J. Ibáñez, Carlos D. Gómez-Carmona, Sergio González-Espinosa and David Mancha-Triguero
Sensors 2024, 24(10), 3245; https://doi.org/10.3390/s24103245 - 20 May 2024
Viewed by 1147
Abstract
Basketball involves frequent high-intensity movements requiring optimal aerobic power. Altitude training can enhance physiological adaptations, but research examining its effects in basketball is limited. This study aimed to characterize the internal/external workload of professional basketball players during preseason and evaluate the effects of [...] Read more.
Basketball involves frequent high-intensity movements requiring optimal aerobic power. Altitude training can enhance physiological adaptations, but research examining its effects in basketball is limited. This study aimed to characterize the internal/external workload of professional basketball players during preseason and evaluate the effects of altitude and playing position. Twelve top-tier professional male basketball players (Liga Endesa, ACB; guards: n = 3, forwards: n = 5, and centers: n = 4) participated in a crossover study design composed of two training camps with nine sessions over 6 days under two different conditions: high altitude (2320 m) and sea level (10 m). Internal loads (heart rate, %HRMAX) and external loads (total distances covered across speed thresholds, accelerations/decelerations, impacts, and jumps) were quantified via wearable tracking and heart rate telemetry. Repeated-measures MANOVA tested the altitude x playing position effects. Altitude increased the total distance (+10%), lower-speed running distances (+10–39%), accelerations/decelerations (+25–30%), average heart rate (+6%), time in higher-intensity HR zones (+23–63%), and jumps (+13%) across all positions (p < 0.05). Positional differences existed, with guards accruing more high-speed running and centers exhibiting greater cardiovascular demands (p < 0.05). In conclusion, a 6-day altitude block effectively overloads training, providing a stimulus to enhance fitness capacities when structured appropriately. Monitoring workloads and individualizing training by playing position are important when implementing altitude training, given the varied responses. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
►▼ Show Figures

Figure 1

17 pages, 5926 KiB  
Article
Shoulder Movement-Centered Measurement and Estimation Scheme for Underarm-Throwing Motions
by Geunho Lee, Yusuke Hayakawa, Takuya Watanabe and Yasuhiro Bonkobara
Sensors 2024, 24(10), 2972; https://doi.org/10.3390/s24102972 - 7 May 2024
Viewed by 6289
Abstract
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their [...] Read more.
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop