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Clinical and Instrumental Methods for Posture Evaluation in Upright Standing and During Motor Tasks

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 2202

Special Issue Editors

Dr. Paolo De Blasiis

E-Mail Website
Guest Editor
School of Engineering, Medicine and Surgery Degree Course, University of Basilicata, 85100 Potenza, Italy
Interests: functional anatomy; motion analysis; posture; movement disorder
Dr. Mariano Serrao

E-Mail Website
Guest Editor
Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, Italy
Interests: neurology; clinical biomechanics; machine learning; movement disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evaluation of posture plays a crucial role in clinical and scientific fields in the functional description and diagnosis of different postural disorders; in clinical decision making; and in outcome assessments after treatment, during the monitoring of the disease, and/or athletic performance.

Different clinical and instrumental methods are used to assess posture during upright standing or motor tasks (walking, sit to stand, etc.).

Clinical evaluations are easily practicable but more susceptible to inter-operator variability, while instrumental ones are more reliable, objectively quantifying postural parameters.

Various instrumental methods (e.g., Rx, 3D motion analysis, etc.) can assess posture in three planes of space while standing or during walking, sit to stand, and athletic gestures. Moreover, other postural parameters, such as postural stability and plantar pressures, can be analysed in the laboratory environment (e.g., using pressure and force plates). Postural evaluations can also be performed outside the laboratory using wearable and portable technologies (e.g., IMU sensors).

Consequently, this Special Issue (“Clinical and Instrumental Methods for Posture Evaluation in Upright Standing and During Motor Tasks”) aims to stimulate discussion and publish recent research on human movement analyses and posture assessments, collecting the latest advancements and documenting innovative clinical and instrumental approaches to the posture evaluation of healthy and pathological populations.

Original articles, reviews, and case studies will be considered for publication. 

Dr. Paolo De Blasiis
Dr. Mariano Serrao
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

  • posture
  • posture evaluation
  • 3D motion analysis
  • kinematics
  • gait analysis
  • stereophotogrammetry
  • X-ray
  • pressure plate
  • postural stability

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Published Papers (2 papers)

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17 pages, 2878 KiB  
Article
Postural Abnormalities on the Sagittal Plane in Parkinson’s Disease and Therapeutic Role of the K1 Posture Keeper Shirt Evaluated by 3D Motion Analysis
by Paolo De Blasiis, Allegra Fullin, Ciro Ivan De Girolamo, Edoardo Bianchini, Giuseppina Miele, Nunzio Setola and Mariarosa Anna Beatrice Melone
Appl. Sci. 2025, 15(5), 2255; https://doi.org/10.3390/app15052255 - 20 Feb 2025
Viewed by 594
Abstract
Postural abnormalities in the sagittal plane are common in Parkinson’s disease (PD) and increase the risk of falls. Previous studies have reported short-term benefit of corrective corsets in PD patients assessed by clinical and instrumental methods, while long-term effects on sagittal posture in [...] Read more.
Postural abnormalities in the sagittal plane are common in Parkinson’s disease (PD) and increase the risk of falls. Previous studies have reported short-term benefit of corrective corsets in PD patients assessed by clinical and instrumental methods, while long-term effects on sagittal posture in upright standing and during walking remain unexplored. Fifteen PD patients with postural abnormalities on the sagittal plane, evaluated via the NeuroPostureApp, and ten healthy subjects matched for age and BMI were assessed by 3D motion analysis in upright posture. Then, the PD patients were evaluated with and without the K1 Posture Keeper during standing and walking at baseline (T0) and after three months of use (T1). The results showed an anteriorization of the head–cervical region with respect to the trunk and a whole-body misalignment in PD patients compared to healthy controls. The use of the K1 Posture Keeper induced a back shift of the nasion with a better alignment of the head with respect to the trunk, pelvis, and feet in upright standing and during walking, underlining an improvement in the sagittal alignment of the entire body in PD. These findings showed the therapeutical role of K1 Posture Keeper on sagittal posture in static and dynamic conditions, potentially due to proprioceptive reorganization. Full article
(This article belongs to the Special Issue Clinical and Instrumental Methods for Posture Evaluation in Upright Standing and During Motor Tasks)
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16 pages, 1929 KiB  
Article
Machine Learning Approaches for Evaluating the Progress of Surgical Training on a Virtual Reality Simulator
by Konstantina Prevezanou, Ioannis Seimenis, Pantelis Karaiskos, Emmanouil Pikoulis, Panagis M. Lykoudis and Constantinos Loukas
Appl. Sci. 2024, 14(21), 9677; https://doi.org/10.3390/app14219677 - 23 Oct 2024
Viewed by 1211
Abstract
Automated assessment of surgical skills is crucial for the successful training of junior surgeons. Twenty-three medical students followed a structured training curriculum on a laparoscopic virtual reality (VR) simulator. Three surgical tasks with significant educational merit were considered (Tasks 5, 6, and 7). [...] Read more.
Automated assessment of surgical skills is crucial for the successful training of junior surgeons. Twenty-three medical students followed a structured training curriculum on a laparoscopic virtual reality (VR) simulator. Three surgical tasks with significant educational merit were considered (Tasks 5, 6, and 7). We evaluated seven machine learning (ML) models for classifying the students’ trials into two and three classes based on the progress of training (Beginning vs. End and Beginning vs. Middle vs. End). Additionally, we evaluated the same ML framework and a deep learning approach (LSTM) for predicting the remaining number of trials required to complete the training proficiently. A model-agnostic technique from the domain of explainable artificial intelligence (XAI) was also utilized to obtain interpretations of the employed black-box ML classifiers. For 2-class classification, the best model showed an accuracy of 97.1%, 96.9%, and 75.7% for Task 5, 6, and 7, respectively, whereas for 3-class classification, the corresponding accuracy was 96.3%, 95.9%, and 99.7%, respectively. The best regression algorithm was LSTM with a Mean Absolute Error of 4 (Task 5) and 3.6 trials (Tasks 6, 7). According to XAI, the kinematic parameters have a stronger impact on the classification decision than the goal-oriented metrics. Full article
(This article belongs to the Special Issue Clinical and Instrumental Methods for Posture Evaluation in Upright Standing and During Motor Tasks)
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