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Biosensors
Special Issues
Advanced Applications in Wearable Biosensors
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Advanced Applications in Wearable Biosensors

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7928

Special Issue Editor

Dr. Daniel T.P. Fong

E-Mail Website1 Website2
Guest Editor
National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
Interests: ankle sprain; sports injury; orthopaedics; gait; sports medicine; sports biomechanics; clinical biomechanics; motion sensing; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors have been used in human motion analysis and monitoring since the 1990s. They are highly transportable and do not require stationary units like receivers and cameras and therefore can be used outside the traditional laboratories or clinics equipped with force plates, motion capture systems with multiple cameras, and a lot of other equipment. These devices allow the measurement and monitoring of human motion and a variety of biological and biomedical signals to be conducted anywhere and can further allow practical applications in training, telemedicine, and rehabilitation monitoring.

The common types of wearable sensors used in human movement analysis and biomedical signal monitoring are accelerometers, gyroscopes, inertial measurement units, micro-electromechanical systems (MEMS), optical sensors, electrodes, force or pressure sensors, foot switch, stretch sensors, temperature sensors, global position system (GPS) sensors, etc.

Some of these sensors can also harvest and store energy generated by human movement. These sensors allow the measurement, monitoring, and analysis of biological, biomedical, and biomechanical signals to quantify the kinematics of the movement, position of the users, muscle activity, reaction and fatigue, gait event identification, breathing rhythm monitoring, exercise intensity monitoring, concussion impact monitoring, as well as biological vital sign monitoring.

This Special Issue seeks papers related to the development and use of wearable sensors for human gait and motion analysis. We accept original, technical or review papers on (but not limited to) the following topics:

  • Concussion impact monitoring
  • Applications in musculoskeletal system
  • Biological vital signs monitoring
  • Sports biomechanics analysis
  • Applications in orthopaedics
  • Markerless motion analysis
  • Movement analysis in individual or team sports
  • Telemedicine
  • Muscle fatigue monitoring
  • Practical applications in training and rehabilitation
  • Validation against traditional motion capture system

You may choose our Joint Special Issue in Sensors.

Dr. Daniel T.P. Fong
Guest 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 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. Biosensors is an international peer-reviewed open access monthly 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 2700 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

  • Motion sensor
  • Accelerometer
  • Gyroscope
  • Biomechanics
  • Gait
  • Kinematics
  • Motion analysis

Published Papers (2 papers)

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Research

12 pages, 1965 KiB  
Article
Transient Effect at the Onset of Human Running
by Christian Weich, Manfred M. Vieten and Randall L. Jensen
Biosensors 2020, 10(9), 117; https://doi.org/10.3390/bios10090117 - 08 Sep 2020
Cited by 4 | Viewed by 2353
Abstract
While training and competing as a runner, athletes often sense an unsteady feeling during the first meters on the road. This sensation, termed as transient effect, disappears after a short period as the runners approach their individual running rhythm. The foundation of this [...] Read more.
While training and competing as a runner, athletes often sense an unsteady feeling during the first meters on the road. This sensation, termed as transient effect, disappears after a short period as the runners approach their individual running rhythm. The foundation of this work focuses on the detection and quantification of this phenomenon. Thirty athletes ran two sessions over 60 min on a treadmill at moderate speed. Three-dimensional acceleration data were collected using two MEMS sensors attached to the lower limbs. By using the attractor method and Fourier transforms, the transient effect was isolated from noise and further components of human cyclic motion. A substantial transient effect was detected in 81% of all measured runs. On average, the transient effect lasted 5.25 min with a range of less than one minute to a maximum of 31 min. A link to performance data such as running level, experience and weekly training hours could not be found. The presented work provides the methodological basis to detect and quantify the transient effect at moderate running speeds. The acquisition of further physical or metabolic performance data could provide more detailed information about the impact of the transient effect on athletic performance. Full article
(This article belongs to the Special Issue Advanced Applications in Wearable Biosensors)
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18 pages, 3977 KiB  
Article
Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units
by Binbin Su, Christian Smith and Elena Gutierrez Farewik
Biosensors 2020, 10(9), 109; https://doi.org/10.3390/bios10090109 - 27 Aug 2020
Cited by 26 | Viewed by 4819
Abstract
Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must [...] Read more.
Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance. Full article
(This article belongs to the Special Issue Advanced Applications in Wearable Biosensors)
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