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Application Wearables in Motor Behavior Monitoring and Sport

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

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 9125

Special Issue Editor


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Guest Editor
Department of Physical Education and Sport Science, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: motor learning; motor control; movement disorders; cerebral laterality; eye-hand-foot preference

Special Issue Information

Dear Colleagues,

Wearable devices are a wide range of electronic and computing technology products integrated into clothing and accessories comfortably worn on or close to human skin to continuously sense, store and analyze quantified biological, physical, behavioral, or environmental self-data. This monitoring is conducted through applications either installed on these devices or on other intelligent systems constantly connected to the internet that have the capacity to provide feedback communication of key metrics to allow the user to view/access personalized information in real time. The ultimate goal of wearable technology is to provide users with digital personalized services that build insights and highlight actions that enhance understanding of their performance parameters and predict their status.

State-of-the art wearable devices, with friendly user interface and ergonomic design, incorporate diverse and powerful sensors or sensor suites which have small size and low cost, consume little energy, and feature high performance, making them increasingly sophisticated and advanced. These devices have widely been used as a useful and powerful tool for laboratory and field research and have, also, opened up new possibilities for human performance and health research. A particular advantage of utilizing this technology is its ability to improve testing schemes, influence measurement strategies, and replace more expensive and well-established scientific instruments, at least under certain circumstances.

Invention and innovation are in all facets of human life and society. Ground-breaking inventions lead to sustainable innovations that shape the course of human evolution. Human kind is always trying to understand and reinvent itself and at the same time competes against human-kind in a struggle for dominance or in a challenge to set and to smash personal goals. Here comes the role of electronic and computing technology. The use of wearable devices in motor behavior was, is, and will be not only promising, but also a step forward in any kind of movement, in any part of the body, in any physical, social and sport context.

This Special Issue aims to bring together the current state-of-the-art research and future directions in the growing niche of “Application Wearables in Motor Behavior Monitoring and Sport”. For this goal, we cordially invite researchers and engineers from both academia and industry to submit their original and novel work for inclusion in this Special Issue.

The potential topics of interest include, but are not limited to the following areas:

  • Application of wearable devices in motor behavior;
  • Application of wearable devices in sports;
  • Athlete’s biometrics for signs of exhaustion or injury while on the field;
  • Body sensor networks and wearable technology;
  • Internet of things, internet of bodies, and internet of everything;
  • Human activity recognition and movement classification using wearable sensors;
  • Monitor athletic training, performance and recovery after an injury;
  • Motor behavior change through wearable technology;
  • Monitor cognitive, emotional, and social aspects of sports;
  • Smart textiles and wearable technologies for sportswear;
  • The future of wearable technology in sports;
  • The quantified athlete;
  • Wearable sensors for monitoring the internal and external workload of the athlete;
  • Wearable technology and big data in sports;
  • Wearable technology and situation awareness;
  • Wearable technology in injury prevention;
  • Wearable technology to support performance enhancement;
  • Wearable technology to support skill acquisition and improvement.

Prof. Dr. George Grouios
Guest Editor

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Published Papers (3 papers)

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11 pages, 2511 KiB  
Article
Estimation of Foot Trajectory and Stride Length during Level Ground Running Using Foot-Mounted Inertial Measurement Units
by Yuta Suzuki, Michael E. Hahn and Yasushi Enomoto
Sensors 2022, 22(19), 7129; https://doi.org/10.3390/s22197129 - 20 Sep 2022
Cited by 4 | Viewed by 3019
Abstract
Zero-velocity assumption has been used for estimation of foot trajectory and stride length during running from the data of foot-mounted inertial measurement units (IMUs). Although the assumption provides a reasonable initialization for foot trajectory and stride length estimation, the other source of errors [...] Read more.
Zero-velocity assumption has been used for estimation of foot trajectory and stride length during running from the data of foot-mounted inertial measurement units (IMUs). Although the assumption provides a reasonable initialization for foot trajectory and stride length estimation, the other source of errors related to the IMU’s orientation still remains. The purpose of this study was to develop an improved foot trajectory and stride length estimation method for the level ground running based on the displacement of the foot. Seventy-nine runners performed running trials at 5 different paces and their running motions were captured using a motion capture system. The accelerations and angular velocities of left and right feet were measured with two IMUs mounted on the dorsum of each foot. In this study, foot trajectory and stride length were estimated using zero-velocity assumption with IMU data, and the orientation of IMU was estimated to calculate the mediolateral and vertical distance of the foot between two consecutive midstance events. Calculated foot trajectory and stride length were compared with motion capture data. The results show that the method used in this study can provide accurate estimation of foot trajectory and stride length for level ground running across a range of running speeds. Full article
(This article belongs to the Special Issue Application Wearables in Motor Behavior Monitoring and Sport)
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15 pages, 3121 KiB  
Article
Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In Sensors
by Ye Tian, Dulmini Hettiarachchi and Shunsuke Kamijo
Sensors 2022, 22(17), 6453; https://doi.org/10.3390/s22176453 - 26 Aug 2022
Cited by 5 | Viewed by 1990
Abstract
Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD, yet they support a limited number [...] Read more.
Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD, yet they support a limited number of modes and do not consider similar transportation modes and holding places, limiting further applications. In this paper, we propose a new network framework to realize TMD, which combines structural and spatial interaction features, and considers the weights of multiple sensors’ contributions, enabling the recognition of eight transportation modes with four similar transportation modes and four holding places. First, raw data is segmented and transformed into a spectrum image and then ResNet and Vision Transformers (Vit) are used to extract structural and spatial interaction features, respectively. To consider the contribution of different sensors, the weights of each sensor are recalibrated using an ECA module. Finally, Multi-Layer Perceptron (MLP) is introduced to fuse these two different kinds of features. The performance of the proposed method is evaluated on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset, and is found to outperform the baselines by at least 10%. Full article
(This article belongs to the Special Issue Application Wearables in Motor Behavior Monitoring and Sport)
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23 pages, 2205 KiB  
Systematic Review
Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review
by Janina Helwig, Janik Diels, Mareike Röll, Hubert Mahler, Albert Gollhofer, Kai Roecker and Steffen Willwacher
Sensors 2023, 23(2), 827; https://doi.org/10.3390/s23020827 - 11 Jan 2023
Cited by 13 | Viewed by 3724
Abstract
Micro electro-mechanical systems (MEMS) are used to record training and match play of intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise, practitioners gain insight into players’ dose–response relationship which facilitates the prescription of the training stimuli to [...] Read more.
Micro electro-mechanical systems (MEMS) are used to record training and match play of intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise, practitioners gain insight into players’ dose–response relationship which facilitates the prescription of the training stimuli to optimize performance, prevent injuries, and to guide rehabilitation processes. A systematic review on the relationship between external, wearable-based, and internal parameters in team sport athletes, compliant with the PRISMA guidelines, was conducted. The literature research was performed from earliest record to 1 September 2020 using the databases PubMed, Web of Science, CINAHL, and SportDISCUS. A total of 66 full-text articles were reviewed encompassing 1541 athletes. About 109 different relationships between variables have been reviewed. The most investigated relationship across sports was found between (session) rating of perceived exertion ((session-)RPE) and PlayerLoad™ (PL) with, predominantly, moderate to strong associations (r = 0.49–0.84). Relationships between internal parameters and highly dynamic, anaerobic movements were heterogenous. Relationships between average heart rate (HR), Edward’s and Banister’s training impulse (TRIMP) seem to be reflected in parameters of overall activity such as PL and TD for running-intensive team sports. PL may further be suitable to estimate the overall subjective perception. To identify high fine-structured loading—relative to a certain type of sport—more specific measures and devices are needed. Individualization of parameters could be helpful to enhance practicality. Full article
(This article belongs to the Special Issue Application Wearables in Motor Behavior Monitoring and Sport)
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