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Biomechanical Analysis of Motion and Postural Control: Sensor-Based Classical and AI Methods

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 706

Special Issue Editors

Dr. Leandro 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 the Sensors journal is to publish articles on the thematic of human motion, balance, and postural control. Articles covering a wide range of applications and situations where sensors can be employed, whether on concrete sensor use and development or on processing of sensor generated data, in the context of biomechanics are welcome:

- From walking or gardening to running or climbing.

- From day-to-day activities to sports motions.

- For people with and without impairments.

- For children, adults, or elderly.

- From wearable sensors to external sensors.

- From local to global data integration and data analysis.

- From IMUs to EMG.

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

- How to overcome the spurious motions of wearable sensors (IMUs, EMG, etc.)?

- Should we employ Artificial Intelligence capabilities in the sensor device or just in the app?

- How to get the system to evaluate in real life conditions, at home, at work, outdoors?

- Can wearable sensors systems help prevent falls or assist balance or gait?

- How to increase wearable sensors’ systems reliability?

Dr. Leandro 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 250 words) can be sent to the Editorial Office for assessment.

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
  • artificial intelligence

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Published Papers (1 paper)

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Research

17 pages, 23401 KB  
Article
Prediction of Center-of-Mass Kinematics of Sensopro Exercises with Neural Network Models
by Heinz Hegi, Michael Single, Tobias Nef and Ralf Kredel
Sensors 2026, 26(10), 3051; https://doi.org/10.3390/s26103051 - 12 May 2026
Viewed by 427
Abstract
Monitoring center-of-mass is crucial for assessing postural control, but field measurements are often impractical or cost-prohibitive. This study investigates the feasibility of predicting center-of-mass kinematics from the motion of an unstable base—the Sensopro Luna—using deep learning, eliminating the need for wearable sensors. We [...] Read more.
Monitoring center-of-mass is crucial for assessing postural control, but field measurements are often impractical or cost-prohibitive. This study investigates the feasibility of predicting center-of-mass kinematics from the motion of an unstable base—the Sensopro Luna—using deep learning, eliminating the need for wearable sensors. We conducted a cross-sectional study in which 64 participants were recorded performing three coordination exercises (Single-Leg Stance, Stepping, and Waves). Marker-based motion capture and auxiliary inertial sensors were used to record reference and tape kinematics. The model inputs consisted of IMU- and motion-capture-derived tape segment orientations, IMU accelerations and angular velocities, and algorithmic estimates of the lowest tape positions. Nine axis-specific exercise models were developed using a hybrid Encoder–LSTM–Decoder architecture and compared against linear regression baselines. Our results indicate that the deep learning models successfully predicted horizontal center-of-mass displacements (DNN Mean Absolute Errors of 16.1–23.7 mm for X-axis and 4.4–31.3 mm for Y-axis) and exhibited descriptively lower errors than linear models in mean absolute error and signal morphology. However, vertical predictions were less reliable, likely due to the physical constraints inherent to the kinematics of the unstable base. Error analysis revealed that prediction accuracy was highest within common postural ranges, but decreased for extreme displacements. These findings provide a proof-of-concept for wearable-free postural monitoring, particularly for movement along the mediolateral and sagittal axes. Such a system could facilitate automated, cost-effective postural feedback and performance tracking in rehabilitation and fitness environments, supporting autonomous coordination training without the practical constraints of traditional measurement systems. Full article
(This article belongs to the Special Issue Biomechanical Analysis of Motion and Postural Control: Sensor-Based Classical and AI Methods)
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