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Applications, Wearables and Sensors for Sports Performance Assessment

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6739

Special Issue Editor

Department of Mechanics, Politecnico di Milano, 20133 Milano, Italy
Interests: wearable sensors; sports biomechanics; clinical biomechanics; sports and exercise science; data science; machine learning; human movement analysis

Special Issue Information

Dear colleagues,

The way athletes perform and behave in complex environments, as well as the underlying cognitive control, is inherently complex, dynamic, multidimensional and highly nonlinear.

Wearable technology enables one to directly capture massive amounts of data on sports performance in ecologic (i.e., discipline- or sport-specific) conditions. The challenge is therefore to provide stakeholders (practitioners, sport scientists, coaches) with meaningful information out of this wealth of information: machine learning and artificial intelligence are a compelling choice to embrace this complexity.

This collection of articles features applications of data science to extract performance and functional information from wearable sensors within a sport and/or athletic environment. Examples are devices for physiological monitoring (heart rate and its variability, temperature, oxygen saturation), for force, motion and/or positional tracking (GPS, inertial sensors, load cells). Innovative and custom solutions are also addressed: this Special Issue features a topical viewpoint on innovative ideas, current landscapes, and new trends on the horizon.

Dr. Matteo Zago
Guest Editor

Manuscript Submission Information

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Keywords

  • wearables
  • machine learning
  • artificial intelligence
  • functional assessment
  • sports performance
  • injury prevention
  • biomechanics

Published Papers (3 papers)

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Research

17 pages, 3529 KiB  
Article
Design and Evaluation of CPR Emergency Equipment for Non-Professionals
by Jiayu Xie and Qun Wu
Sensors 2023, 23(13), 5948; https://0-doi-org.brum.beds.ac.uk/10.3390/s23135948 - 27 Jun 2023
Cited by 1 | Viewed by 1999
Abstract
Sudden cardiac death is a sudden and highly fatal condition. Implementing high-quality emergency cardiopulmonary resuscitation (CPR) early on is an effective rescue method for this disease. However, the rescue steps of CPR are complicated and difficult to remember, and the quantitative indicators are [...] Read more.
Sudden cardiac death is a sudden and highly fatal condition. Implementing high-quality emergency cardiopulmonary resuscitation (CPR) early on is an effective rescue method for this disease. However, the rescue steps of CPR are complicated and difficult to remember, and the quantitative indicators are difficult to control, which leads to a poor quality of CPR emergency actions outside the hospital setting. Therefore, we have developed CPR emergency equipment with a multisensory feedback function, aiming to guide rescuers in performing CPR through visual, auditory, and tactile interaction. This equipment consists of three components: first aid clothing, an audio-visual integrated terminal, and a vital sign detector. These three components are based on a micro-power WiFi-Mesh network, enabling the long-term wireless transmission of the multisensor data. To evaluate the impact of the multisensory feedback CPR emergency equipment on nonprofessionals, we conducted a controlled experiment involving 32 nonmedical subjects. Each subject was assigned to either the experimental group, which used the equipment, or the control group, which did not. The main evaluation criteria were the chest compression (CC) depth, the CC rate, the precise depth of the CC ratio (5–6 cm), and the precise rate of the CC ratio -(100–120 times/min). The results indicated that the average CC depth in the experimental group was 51.5 ± 1.3 mm, which was significantly better than that of the control group (50.2 ± 2.2 mm, p = 0.012). Moreover, the average CC rate in the experimental group (110.1 ± 6.2 times/min) was significantly higher than that of the control group (100.4 ± 6.6 times/min) (p < 0.001). Compared to the control group (66.37%), the experimental group showed a higher proportion of precise CC depth (82.11%), which is closer to the standard CPR rate of 100%. In addition, the CC ratio of the precise rate was 93.75% in the experimental group, which was significantly better than that of 56.52% in the control group (p = 0.024). Following the experiment, the revised System Availability Scale (SUS) was utilized to evaluate the equipment’s usability. The average total SUS score was 78.594, indicating that the equipment’s acceptability range was evaluated as ‘acceptable’, and the overall adjective rating was ‘good’. In conclusion, the multisensory feedback CPR emergency equipment significantly enhances the CC performance (CC depth, CC rate, the precise depth of CC ratio, the precise rate of CC ratio) of nonprofessionals during CPR, and the majority of participants perceive the equipment as being easy to use. Full article
(This article belongs to the Special Issue Applications, Wearables and Sensors for Sports Performance Assessment)
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24 pages, 2745 KiB  
Article
Utility and Usability of Two Forms of Supplemental Vibrotactile Kinesthetic Feedback for Enhancing Movement Accuracy and Efficiency in Goal-Directed Reaching
by Ramsey K. Rayes, Rachel N. Mazorow, Leigh A. Mrotek and Robert A. Scheidt
Sensors 2023, 23(12), 5455; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125455 - 09 Jun 2023
Cited by 1 | Viewed by 1014
Abstract
Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience [...] Read more.
Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience for two biologically inspired ways to encode movement-related information into supplemental feedback for the real-time control of goal-directed reaching in healthy, neurologically intact adults. One encoding scheme mimicked visual feedback encoding by converting real-time hand position in a Cartesian frame of reference into supplemental kinesthetic feedback provided by a vibrotactile display attached to the non-moving arm and hand. The other approach mimicked proprioceptive encoding by providing real-time arm joint angle information via the vibrotactile display. We found that both encoding schemes had objective utility in that after a brief training period, both forms of supplemental feedback promoted improved reach accuracy in the absence of concurrent visual feedback over performance levels achieved using proprioception alone. Cartesian encoding promoted greater reductions in target capture errors in the absence of visual feedback (Cartesian: 59% improvement; Joint Angle: 21% improvement). Accuracy gains promoted by both encoding schemes came at a cost in terms of temporal efficiency; target capture times were considerably longer (1.5 s longer) when reaching with supplemental kinesthetic feedback than without. Furthermore, neither encoding scheme yielded movements that were particularly smooth, although movements made with joint angle encoding were smoother than movements with Cartesian encoding. Participant responses on user experience surveys indicate that both encoding schemes were motivating and that both yielded passable user satisfaction scores. However, only Cartesian endpoint encoding was found to have passable usability; participants felt more competent using Cartesian encoding than joint angle encoding. These results are expected to inform future efforts to develop wearable technology to enhance the accuracy and efficiency of goal-directed actions using continuous supplemental kinesthetic feedback. Full article
(This article belongs to the Special Issue Applications, Wearables and Sensors for Sports Performance Assessment)
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19 pages, 5176 KiB  
Article
Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
by Serena Cerfoglio, Manuela Galli, Marco Tarabini, Filippo Bertozzi, Chiarella Sforza and Matteo Zago
Sensors 2021, 21(22), 7709; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227709 - 19 Nov 2021
Cited by 7 | Viewed by 2687
Abstract
Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments [...] Read more.
Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method. Full article
(This article belongs to the Special Issue Applications, Wearables and Sensors for Sports Performance Assessment)
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