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Sensors and Artificial Intelligence in Gait and Posture Analysis
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Sensors and Artificial Intelligence in Gait and Posture Analysis

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 12323

Special Issue Editor

Dr. Moataz Eltoukhy

E-Mail Website
Guest Editor
1. Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL, USA
2. Department of Physical Therapy, Miller School of Medicine, University of Miami, Coral Gables, FL, USA
3. Department of Industrial & Systems Engineering, College of Engineering, University of Miami, Coral Gables, FL, USA
Interests: gait analysis; musculoskeletal modeling; marker-less motion capture; machine learning

Special Issue Information

Dear Colleagues,

A variety of technologies have been developed to obtain spatiotemporal or kinematic parameters during gait and posture analysis, including integrated optical systems, inertial measurement units (IMUs), markerless systems, and instrumented walkways. Additionally, the use of AI utilizing video inputs for 2D and 3D human pose estimation has expanded rapidly in the computer vision community from early pose estimation frameworks to contemporary deep-learning-based approaches.

This Special Issue of the journal Sensors, entitled “Sensors and Artificial Intelligence in Gait and Posture Analysis”, will focus on publishing research works related to the use of sensors and/or artificial intelligence (AI) in gait and posture analysis and its wide applications in young and elderly populations, including both healthy patients and those with injuries (such as ACL injury) or various neurological disorders (such as Parkinson’s disease, stroke, and cerebral palsy).

Therefore, this Special Issue aims to shed the light on the significant impact of the innovative use of sensors and AI in areas such as gait analysis and classification, fall detection, and injury prevention.

Dr. Moataz Eltoukhy
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

  • gait
  • biomechanics
  • marker-less motion capture
  • machine learning
  • Parkinson’s disease
  • stroke
  • ACL
  • balance
  • posture
  • IMU

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Published Papers (5 papers)

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Research

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23 pages, 3776 KiB  
Article
Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population
by Md Farhan Alam, Saima Zaki, Saurabh Sharma and Shibili Nuhmani
Sensors 2024, 24(21), 6884; https://doi.org/10.3390/s24216884 - 26 Oct 2024
Cited by 1 | Viewed by 2321
Abstract
Background: Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. [...] Read more.
Background: Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. Methods: A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. Results: The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. Conclusion: The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)
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12 pages, 1419 KiB  
Article
Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning
by Julian Decker, Lukas Boborzi, Roman Schniepp, Klaus Jahn and Max Wuehr
Sensors 2024, 24(19), 6442; https://doi.org/10.3390/s24196442 - 4 Oct 2024
Cited by 2 | Viewed by 1416
Abstract
Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet [...] Read more.
Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables the determination of temporal and spatial gait cycle characteristics (among others, stride time and stride length) with good to excellent validity at a precision sufficient to monitor clinically relevant changes in walking speed, stride-to-stride variability, and side asymmetry. This study highlights the ear as a viable site for monitoring gait and proposes its potential integration with in-ear vital-sign monitoring. Such integration offers a practical approach to comprehensive health monitoring and telemedical applications, by integrating multiple sensors in a single anatomical location. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)
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14 pages, 2130 KiB  
Article
Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques
by Jiefei Liu, Derek W. Bailey, Huiping Cao, Tran Cao Son and Colin T. Tobin
Sensors 2024, 24(13), 4067; https://doi.org/10.3390/s24134067 - 22 Jun 2024
Cited by 5 | Viewed by 1800
Abstract
Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using [...] Read more.
Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)
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13 pages, 1888 KiB  
Article
Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability
by Federico Roggio, Sarah Di Grande, Salvatore Cavalieri, Deborah Falla and Giuseppe Musumeci
Sensors 2024, 24(9), 2929; https://doi.org/10.3390/s24092929 - 4 May 2024
Cited by 6 | Viewed by 4264
Abstract
Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, while also exploring the underlying structure of the data through [...] Read more.
Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, while also exploring the underlying structure of the data through principal component and cluster analyses. A cohort of 200 healthy individuals with a mean age of 24.4 ± 4.2 years was photographed from the frontal, dorsal, and lateral views. We used Student’s t-test and Cohen’s effect size (d) to identify gender-specific postural differences and used the Intraclass Correlation Coefficient (ICC) to assess the reliability of this method. Our findings demonstrate distinct sex differences in shoulder adduction angle (men: 16.1° ± 1.9°, women: 14.1° ± 1.5°, d = 1.14) and hip adduction angle (men: 9.9° ± 2.2°, women: 6.7° ± 1.5°, d = 1.67), with no significant differences in horizontal inclinations. ICC analysis, with the highest value of 0.95, confirms the reliability of the approach. Principal component and clustering analyses revealed potential new patterns in postural analysis such as significant differences in shoulder–hip distance, highlighting the potential of unsupervised ML for objective posture analysis, offering a promising non-invasive method for rapid, reliable screening in physical therapy, ergonomics, and sports. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)
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Review

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24 pages, 2730 KiB  
Review
The Future of Clinical Active Shoulder Range of Motion Assessment, Best Practice, and Its Challenges: Narrative Review
by Wolbert van den Hoorn, Arthur Fabre, Giacomo Nardese, Eric Yung-Sheng Su, Kenneth Cutbush, Ashish Gupta and Graham Kerr
Sensors 2025, 25(3), 667; https://doi.org/10.3390/s25030667 - 23 Jan 2025
Cited by 1 | Viewed by 1528
Abstract
Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy [...] Read more.
Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy varies due to the differences in gold standards, anatomical definitions, and deep learning techniques. Despite some biases, the studies report a high consistency, emphasising that methods should not be used interchangeably if they do not agree with each other. Smartphone cameras perform well in capturing 2D planar movements but struggle with that of rotational movements and forward flexion, particularly when thoracic compensations are involved. Proper camera positioning, orientation, and distance are key, highlighting the importance of standardised protocols in mobile phone-based ROM evaluations. Although 3D motion capture, per the International Society of Biomechanics recommendations, remains the gold standard, advancements in LiDAR/depth sensing, smartphone cameras, and deep learning show promise for reliable ROM assessments in clinical settings. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Gait and Posture Analysis)
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