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Biomechanical Analysis of Motion and Postural Control: Sensor Methods and Data Analytics—Third Edition

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 6306

Special Issue Editors

Dr. Leandro José Rodrigues Machado

E-Mail Website
Guest Editor
Department of Biomechanics, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
Interests: biomechanics; data analytics; gait analysis; swimming analysis; para-sports analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Correia

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: sensors; wearables; medical devices; biomedical instrumentation; smart textiles; motion analysis; gait analysis; perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue of Sensors is to publish articles on the theme of human motion, balance, and postural control. Articles covering a wide range of applications and situations where sensors can be employed are welcome, whether they cover concrete sensor use or the processing of sensor-generated data in the context of biomechanics:

  • From walking to running.
  • From swimming to jumping.
  • From day-to-day activities to sports motions.
  • For people with and without impairments.
  • From wearable sensors to external sensors.
  • From local to global data integration and data analysis.
  • From IMUs to EMG.

Articles published in this Special Issue may contribute to a better understanding of questions such as:

  • How to determine the correct positioning of wearable sensors? Should more than one sensor be used per body segment? Should complementary types of sensors be used? Should larger sensors be used?
  • How smart does a sensor or a local group of sensors need to be?
  • How can submillimetre precision be obtained with markerless setups for 3D motion analysis? How many people can be followed simultaneously?
  • How can a system be created that evaluates real-life conditions at home, at work, or outdoors?
  • Can wearable sensor systems help prevent falls or assist with balance or gait?
  • How can we increase sensor systems’ reliability?

Dr. Leandro José Rodrigues Machado
Dr. Miguel Correia
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. 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

  • biomechanics
  • sports
  • day-to-day activities
  • impairments
  • data processing
  • wearable
  • markerless

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Published Papers (4 papers)

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Research

12 pages, 1198 KiB  
Article
Visual Deprivation’s Impact on Dynamic Posture Control of Trunk: A Comprehensive Sensing Information Analysis of Neurophysiological Mechanisms
by Anna Sasaki, Honoka Nagae, Yukio Furusaka, Kei Yasukawa, Hayato Shigetoh, Takayuki Kodama and Junya Miyazaki
Sensors 2024, 24(17), 5849; https://doi.org/10.3390/s24175849 - 9 Sep 2024
Viewed by 755
Abstract
Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk [...] Read more.
Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion–extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future. Full article
(This article belongs to the Special Issue Biomechanical Analysis of Motion and Postural Control: Sensor Methods and Data Analytics—Third Edition)
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27 pages, 9553 KiB  
Article
A Step Forward Understanding Directional Limitations in Markerless Smartphone-Based Gait Analysis: A Pilot Study
by Pavol Martiš, Zuzana Košutzká and Andreas Kranzl
Sensors 2024, 24(10), 3091; https://doi.org/10.3390/s24103091 - 13 May 2024
Viewed by 1414
Abstract
The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system—Vicon. Our [...] Read more.
The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system—Vicon. Our focus was on its performance in capturing walking both toward and away from two iPhone cameras in the same setting, which allowed capturing the Timed Up and Go (TUG) test. The performance of the OpenCap system was compared to that of a standard marker-based system by comparing spatial-temporal and kinematic parameters in 10 participants. The study focused on identifying potential discrepancies in accuracy and comparing results using correlation analysis. Case examples further explored our results. The OpenCap system demonstrated good accuracy in spatial-temporal parameters but faced challenges in accurately capturing kinematic parameters, especially in the walking direction facing away from the cameras. Notably, the two walking directions observed significant differences in pelvic obliquity, hip abduction, and ankle flexion. Our findings suggest areas for improvement in markerless technologies, highlighting their potential in clinical settings. Full article
(This article belongs to the Special Issue Biomechanical Analysis of Motion and Postural Control: Sensor Methods and Data Analytics—Third Edition)
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21 pages, 4113 KiB  
Article
Simulation of Human Movement in Zero Gravity
by Adelina Bärligea, Kazunori Hase and Makoto Yoshida
Sensors 2024, 24(6), 1770; https://doi.org/10.3390/s24061770 - 9 Mar 2024
Viewed by 1815
Abstract
In the era of expanding manned space missions, understanding the biomechanical impacts of zero gravity on human movement is pivotal. This study introduces a novel and cost-effective framework that demonstrates the application of Microsoft’s Azure Kinect body tracking technology as a motion input [...] Read more.
In the era of expanding manned space missions, understanding the biomechanical impacts of zero gravity on human movement is pivotal. This study introduces a novel and cost-effective framework that demonstrates the application of Microsoft’s Azure Kinect body tracking technology as a motion input generator for subsequent OpenSim simulations in weightlessness. Testing rotations, locomotion, coordination, and martial arts movements, we validate the results’ realism under the constraints of angular and linear momentum conservation. While complex, full-body coordination tasks face limitations in a zero gravity environment, our findings suggest possible approaches to device-free exercise routines for astronauts and reveal insights into the feasibility of hand-to-hand combat in space. However, some challenges remain in distinguishing zero gravity effects in the simulations from discrepancies in the captured motion input or forward dynamics calculations, making a comprehensive validation difficult. The paper concludes by highlighting the framework’s practical potential for the future of space mission planning and related research endeavors, while also providing recommendations for further refinement. Full article
(This article belongs to the Special Issue Biomechanical Analysis of Motion and Postural Control: Sensor Methods and Data Analytics—Third Edition)
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17 pages, 3788 KiB  
Article
Adaptive Lifting Index (aLI) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results
by Alberto Ranavolo, Arash Ajoudani, Giorgia Chini, Marta Lorenzini and Tiwana Varrecchia
Sensors 2024, 24(5), 1474; https://doi.org/10.3390/s24051474 - 24 Feb 2024
Cited by 2 | Viewed by 1133
Abstract
When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve [...] Read more.
When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment. Full article
(This article belongs to the Special Issue Biomechanical Analysis of Motion and Postural Control: Sensor Methods and Data Analytics—Third Edition)
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