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Sensors and Data Analysis for Biomechanics and Physical Activity
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Sensors and Data Analysis for Biomechanics and Physical Activity

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1733

Special Issue Editor

Prof. Dr. Francisco Pinho

E-Mail Website
Guest Editor
H²M—Health and Human Movement Unit, Polytechnic University of Health, CESPU, CRL, 4760-409 Vila Nova de Famalicão Portugal
Interests: biomedical engineering; physiotherapy

Special Issue Information

Dear Colleagues,

The integration of sensors and data analytics in the field of biomechanics and dynamic physiology has proven to be a transformative field of research and real-life application that has revealed new ways of understanding human movement and performance. This discipline combines engineering and biology to create interactive relationships that change how we perceive and optimize physical activity.

Sensors, from accelerometers to gyroscopes, electromyography, machine vision and advanced wearable devices, act as vigilant observers, capturing intricate details of biomechanical processes during physical exertion. Through a data analysis perspective, patterns and anomalies are revealed in these data, allowing researchers and practitioners to understand the subtle nuances of movement, dynamics and posture.

This mixture of sensors and data analytics can be applied in a variety of fields, from sports performance enhancement to rehabilitation strategies. Athletes can benefit from real-time feedback, tuning their machines based on accurate biomechanical insights. Physical therapists can provide targeted interventions tailored to individual needs and optimize recovery strategies.

Specifically, the topic of sensors and data analytics in biomechanics and physical activity is an issue of power, where technology becomes an indispensable ally in unlocking the complexities of human movement, providing a new era of accuracy, performance and well-being outcomes.

Prof. Dr. Francisco Pinho
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. 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

  • sensors
  • biomechanics
  • EMG
  • IMU
  • non-linear analysis
  • machine learning
  • sports
  • kinetics
  • kinematics

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Published Papers (2 papers)

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Research

9 pages, 2389 KiB  
Article
The Reliability of 20 m Sprint Time Using a Novel Assessment Technique
by Patrick M. Holmberg, Mico H. Olivier and Vincent G. Kelly
Sensors 2025, 25(7), 2077; https://doi.org/10.3390/s25072077 - 26 Mar 2025
Viewed by 512
Abstract
Sprint acceleration is critical for success in team sports. This study aimed to (a) establish the test–retest reliability of a novel method for assessing 20 m sprint performance and (b) determine the magnitude of meaningful change in 20 m sprint times. Thirty highly [...] Read more.
Sprint acceleration is critical for success in team sports. This study aimed to (a) establish the test–retest reliability of a novel method for assessing 20 m sprint performance and (b) determine the magnitude of meaningful change in 20 m sprint times. Thirty highly trained male team sport athletes completed sprint testing (2 × 20 m [separated by 5 min]) on two separate occasions, separated by 7 days. Sprint times (0–20, 0–10, 10–20 m) were recorded using infrared timing gates (Brower Timing Systems, West Valley City, UT, USA) connected to a motion start sensor positioned at the participant’s rear leg while in a 2-point starting stance. 0–20, 0–10, and 10–20 m sprint times demonstrated acceptable reliability (CV = 0.52–1.36%, ICC = 0.89–0.95). Additionally, the smallest worthwhile change (SWC) was greater than the typical error (TE [95% CI]) for 0–20 (0.025 s) and 0–10 m (0.016 s) sprint times, indicating that meaningful changes can be reliably detected between testing sessions. However, the SWC was less than the TE for 10–20 m sprint times. This suggests the method may not reliably detect meaningful changes in sprint performance over this distance. As such, the minimal detectable change (95% CI) should be considered the threshold for meaningful change (0.033 s). The consistent and low TE across sprint distances highlights the test–retest reliability of the method for assessing 0–20 m sprint times in this population of highly trained male team sport athletes. Full article
(This article belongs to the Special Issue Sensors and Data Analysis for Biomechanics and Physical Activity)
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17 pages, 4718 KiB  
Article
Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks
by Lorenzo Pitto, Frédéric R. Simon, Geoffrey N. Ertel, Gérome C. Gauchard and Guillaume Mornieux
Sensors 2025, 25(1), 279; https://doi.org/10.3390/s25010279 - 6 Jan 2025
Viewed by 790
Abstract
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most [...] Read more.
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most commonly employed systems being PowerLine and BioRow. This procedure can be both expensive and time-consuming, thus limiting trainers’ ability to monitor athletes. In this study, we developed an easier-to-install and cheaper method for estimating rowers’ forces and powers based only on cable position sensors for ergometer rowing and inertial measurement units (IMUs) and GPS for scull rowing. We used data from 12 and 11 rowers on ergometer and on boat, respectively, to train a long short-term memory (LSTM) network. The LSTM was able to reconstruct the forces and power at the gate with an overall mean absolute error of less than 5%. The reconstructed forces and power were able to reveal inter-subject differences in technique, with an accuracy of 93%. Performing leave-one-out validation showed a significant increase in error, confirming that more subjects are needed in order to develop a tool that could be generalizable to external athletes. Full article
(This article belongs to the Special Issue Sensors and Data Analysis for Biomechanics and Physical Activity)
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