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Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 61193

Special Issue Editors

Prof. Dr. Bjoern Eskofier

E-Mail Website
Guest Editor
Department of Computer Science, Friedrich-Alexander University Erlangen-Nuernberg, Immerwahrstr. 2a, 91058 Erlangen, Germany
Interests: wearable computing; human–machine–interaction; machine learning; data mining; biomedical engineering; sports engineering
Prof. Dr. Sunghoon Ivan Lee

E-Mail Website
Guest Editor
College of Information and Computer Sciences, University of Massachusetts Amherst 140 Governors Drive, Amherst, MA 01003, USA
Interests: mobile and personalized health; wearable sensors; machine learning; data analytics; physical and rehabilitation engineering

Special Issue Information

Dear Colleagues,

The application areas of sports, health, and medical engineering are experiencing a remarkable transformation that is driven by digital technologies. Among the most important drivers of this transformation are novel wearable computing systems, as well as machine learning and signal processing algorithms.

Wearable sensors play an increasingly important role in these application areas of sports, health, and medicine. Wearable computing systems can provide real-time feedback and coaching advice. They also allow to instrument studies outside the lab, facilitating the assessment of the real, “in-the-wild”, situation.

Machine learning and signal processing algorithms provide data-driven methods for analyzing the considerable amount of data that is generated in the mentioned application areas. The methods can deal with large data sets, analyze multiple dimensions simultaneously, and provide valuable insights into processes like training effects, health benefits, and chronic disease progression.

The purpose of this Special Issue is to address key topics in wearable computing and machine learning for applications in sports, health, and medical engineering. We are looking forward to impactful manuscripts that address key methodological issues in the aforementioned application fields.

Dr. Bjoern Eskofier
Dr. Sunghoon Ivan Lee
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. 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

  • Wearable Computing
  • Body Sensor Network
  • Human-Machine-Interaction
  • Machine Learning
  • Data Mining
  • Big Data
  • Biomechanics
  • Digital Health
  • Biomedical Engineering
  • Sports Engineering

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review, Other

3 pages, 153 KiB  
Editorial
Special Issue on Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering
by Sunghoon I. Lee and Bjoern M. Eskofier
Appl. Sci. 2018, 8(2), 167; https://doi.org/10.3390/app8020167 - 25 Jan 2018
Cited by 3 | Viewed by 3432
Abstract
Recent advancement in digital technologies is driving a remarkable transformation in sports, health, and medical engineering, aiming to achieve the accurate quantification of performance, well-being, and disease condition, and the optimization of sports, clinical, and therapeutic training and treatment programs.[...] Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)

Research

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15 pages, 6927 KiB  
Article
Wearable Current-Based ECG Monitoring System with Non-Insulated Electrodes for Underwater Application
by Stefan Gradl, Tobias Cibis, Jasmine Lauber, Robert Richer, Ruslan Rybalko, Norman Pfeiffer, Heike Leutheuser, Markus Wirth, Vinzenz Von Tscharner and Bjoern M. Eskofier
Appl. Sci. 2017, 7(12), 1277; https://doi.org/10.3390/app7121277 - 8 Dec 2017
Cited by 13 | Viewed by 8671
Abstract
The second most common cause of diving fatalities is cardiovascular diseases. Monitoring the cardiovascular system in actual underwater conditions is necessary to gain insights into cardiac activity during immersion and to trigger preventive measures. We developed a wearable, current-based electrocardiogram (ECG) device in [...] Read more.
The second most common cause of diving fatalities is cardiovascular diseases. Monitoring the cardiovascular system in actual underwater conditions is necessary to gain insights into cardiac activity during immersion and to trigger preventive measures. We developed a wearable, current-based electrocardiogram (ECG) device in the eco-system of the FitnessSHIRT platform. It can be used for normal/dry ECG measuring purposes but is specifically designed to allow underwater signal acquisition without having to use insulated electrodes. Our design is based on a transimpedance amplifier circuit including active current feedback. We integrated additional cascaded filter components to counter noise characteristics specific to the immersed condition of such a system. The results of the evaluation show that our design is able to deliver high-quality ECG signals underwater with no interferences or loss of signal quality. To further evaluate the applicability of the system, we performed an applied study with it using 12 healthy subjects to examine whether differences in the heart rate variability exist between sitting and supine positions of the human body immersed in water and outside of it. We saw significant differences, for example, in the RMSSD and SDSD between sitting outside the water (36 ms) and sitting immersed in water (76 ms) and the pNN50 outside the water (6.4%) and immersed in water (18.2%). The power spectral density for the sitting positions in the TP and HF increased significantly during water immersion while the LF/HF decreased significantly. No significant changes were found for the supine position. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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10 pages, 2840 KiB  
Article
A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors
by Gongbing Shan, Xiang Zhang, Mingliang Meng and Brandie Wilde
Appl. Sci. 2017, 7(8), 771; https://doi.org/10.3390/app7080771 - 30 Jul 2017
Cited by 7 | Viewed by 6177
Abstract
As the population ages, falls are becoming a major health problem, not only for those with some degree of balance or mobility impairment, but also among healthy active seniors. Previous studies suggest that the degradation of human sensorimotor function related to age contributes [...] Read more.
As the population ages, falls are becoming a major health problem, not only for those with some degree of balance or mobility impairment, but also among healthy active seniors. Previous studies suggest that the degradation of human sensorimotor function related to age contributes to falls. Hip bones are among the most frequently fractured body parts resulting from falls. Hip fractures are a frequent cause of early death, functional dependence, and high medical care costs. The current prevention method is to use hip protectors. Unfortunately, it often fails to do so because the pocket containing the pad can move away from the area during falls. Additionally, some seniors refuse to use hip protectors because they find them constraining. Hence, a new protector that is only activated during a fall is much desired. The current study explored the possibility via biomechanical analyses for building a wearable sensor system that triggers a mini-airbag system during a fall, i.e., the air-pad is only present for protection when a fall occurs. The results have revealed that two sensors placed on the left and right shoulder would be best for a detection of any-direction fall and could be applied for building a wearable sensor system for prevention of hip fractures resulting from falls. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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14 pages, 1793 KiB  
Article
Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
by Albano Carrera, Alonso Alonso, Ramón De la Rosa and Evaristo J. Abril
Appl. Sci. 2017, 7(4), 317; https://doi.org/10.3390/app7040317 - 24 Mar 2017
Cited by 13 | Viewed by 8174
Abstract
This paper presents a glove designed to assess the viability of communication between a deaf-blind user and his/her interlocutor through a vibrotactile device. This glove is part of the TactileCom system, where communication is bidirectional through a wireless link, so no contact is [...] Read more.
This paper presents a glove designed to assess the viability of communication between a deaf-blind user and his/her interlocutor through a vibrotactile device. This glove is part of the TactileCom system, where communication is bidirectional through a wireless link, so no contact is required between the interlocutors. Responsiveness is higher than with letter by letter wording. The learning of a small set of concepts is simpler and the amount learned can be increased at the user’s convenience. The number of stimulated fingers, the keying frequencies and finger response were studied. Message identification rate was 97% for deaf-blind individuals and 81% for control subjects. Identification by single-finger stimulation was better than by multiple-finger stimulation. The interface proved suitable for communication with deaf-blind individuals and can also be used in other conditions, such as multilingual or noisy environments. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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Review

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17 pages, 2421 KiB  
Review
An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring
by Bjoern M. Eskofier, Sunghoon Ivan Lee, Manuela Baron, André Simon, Christine F. Martindale, Heiko Gaßner and Jochen Klucken
Appl. Sci. 2017, 7(10), 986; https://doi.org/10.3390/app7100986 - 25 Sep 2017
Cited by 117 | Viewed by 21141
Abstract
New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well [...] Read more.
New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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Other

10 pages, 571 KiB  
Technical Note
Which Method Detects Foot Strike in Rearfoot and Forefoot Runners Accurately when Using an Inertial Measurement Unit?
by Christian Mitschke, Tobias Heß and Thomas L. Milani
Appl. Sci. 2017, 7(9), 959; https://doi.org/10.3390/app7090959 - 19 Sep 2017
Cited by 15 | Viewed by 7231
Abstract
Accelerometers and gyroscopes are used to detect foot strike (FS), i.e., the moment when the foot first touches the ground. However, it is unclear whether different conditions (footwear hardness or foot strike pattern) influence the accuracy and precision of different FS detection methods [...] Read more.
Accelerometers and gyroscopes are used to detect foot strike (FS), i.e., the moment when the foot first touches the ground. However, it is unclear whether different conditions (footwear hardness or foot strike pattern) influence the accuracy and precision of different FS detection methods when using such micro-electromechanical sensors (MEMS). This study compared the accuracy of four published MEMS-based FS detection methods with each other and the gold standard (force plate) to establish the most accurate method with regard to different foot strike patterns and footwear conditions. Twenty-three recreational runners (12 rearfoot and 11 forefoot strikers) ran on a 15-m indoor track at their individual running speed in three footwear conditions (low to high hardness). MEMS and a force plate were sampled at a rate of 3750 Hz. Individual accuracy and precision of FS detection methods were found which were dependent on running styles and footwear conditions. Most of the methods were characterized by a delay which generally increased from rearfoot to forefoot strike pattern and from high to low midsole hardness. It can be concluded that only one of the four methods can accurately determine FS in a variety of conditions. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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Graphical abstract

8 pages, 562 KiB  
Technical Note
A Single Gyroscope Can Be Used to Accurately Determine Peak Eversion Velocity during Locomotion at Different Speeds and in Various Shoes
by Christian Mitschke, Matthias Öhmichen and Thomas L. Milani
Appl. Sci. 2017, 7(7), 659; https://doi.org/10.3390/app7070659 - 27 Jun 2017
Cited by 11 | Viewed by 5236
Abstract
Gyroscopes have been used in previous studies to measure the peak angular velocity of the shoe or foot in the frontal plane (evVel). However, it is not clear whether different test conditions (footwear hardness or locomotion speed) can influence the accuracy of evVel. [...] Read more.
Gyroscopes have been used in previous studies to measure the peak angular velocity of the shoe or foot in the frontal plane (evVel). However, it is not clear whether different test conditions (footwear hardness or locomotion speed) can influence the accuracy of evVel. The purpose of the present study was to compare the accuracy of gyroscopes and electrogoniometers when measuring evVel and the time until evVel (t_evVel) in 12 different conditions using a single axis gyroscope attached to the heel cap. Twenty-four recreational runners were instructed to walk and run on a 15-m indoor track at four locomotion speeds (1.5, 2.5, and 3.5 m/s, and individual running speed) and in three footwear conditions (low to high hardness). The gyroscope data and electrogoniometer data were sampled at a rate of 1000 Hz. Comparisons between both measurement devices showed small mean differences up to 49.8 ± 46.9 deg/s for evVel and up to 5.3 ± 3.5 ms for t_evVel. Furthermore, strong relationships between gyroscope and electrogoniometer data were found for evVel as well as for t_evVel for all conditions. It can be concluded that gyroscopes can be used to accurately determine evVel and t_evVel under a variety of conditions. Full article
(This article belongs to the Special Issue Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering)
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