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Advanced Wearable, Vision-Based, and Intelligent Systems for Movement and Gait...
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Advanced Wearable, Vision-Based, and Intelligent Systems for Movement and Gait Analysis

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

Deadline for manuscript submissions: 20 December 2026 | Viewed by 2393

Editors

Dr. Behnaz Ghoraani

E-Mail Website
Guest Editor
College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431-0991, USA
Interests: gait and movement analysis; wearable and camera-based sensing; digital biomarkers; machine learning and AI in healthcare; neurological disorder detection
Special Issues, Collections and Topics in MDPI journals
Dr. Yunfeng Wu

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: gait analysis; biosignal processing; machine learning; biomedical data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in wearable sensors, vision-based systems, and intelligent data analytics have transformed the assessment of human movement and gait, enabling objective, scalable, and clinically meaningful evaluation in both laboratory and real-world environments. This Special Issue aims to highlight cutting-edge sensing technologies and intelligent systems for movement and gait analysis, with applications spanning healthcare, rehabilitation, aging, sports, and human–computer interaction. Topics of interest include, but are not limited to, wearable inertial and physiological sensors, camera-based and video-derived gait analysis, multimodal sensing platforms, and AI-driven modeling techniques such as machine learning, deep learning, and explainable AI. Particular emphasis is placed on innovative methodologies that improve sensitivity to subtle motor and cognitive changes, enable remote and home-based assessment, and provide clinically actionable insights. Applications may include early detection of neurological and musculoskeletal disorders, fall-risk assessment, rehabilitation monitoring, and performance optimization.

Dr. Behnaz Ghoraani
Dr. Yunfeng Wu
Guest Editors

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Keywords

  • gait analysis
  • wearable sensors
  • video-based sensing
  • inertial measurement units
  • movement analysis
  • artificial intelligence
  • machine learning
  • digital biomarkers
  • rehabilitation monitoring

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Published Papers (3 papers)

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Research

31 pages, 4848 KB  
Article
A Multi-Sensor, Multi-Movement Exploratory Study of Motion Tape Strain Data for Low Back Pain Classification
by Pratham Yashwante, Sara P. Gombatto, Yasmín Velázquez, Elijah Wyckoff, Aarti Lalwani, Kevin Patrick, Kenneth J. Loh, Emilia Farcas and Rose Yu
Sensors 2026, 26(13), 4187; https://doi.org/10.3390/s26134187 - 2 Jul 2026
Viewed by 562
Abstract
Objective assessment of low back pain (LBP) is challenging due to subtle, task-dependent movement impairments that are poorly captured by existing sensing technologies. Motion Tape (MT), which is a self-adhesive elastic fabric skin strain sensor, enables skin-conforming measurement of localized biomechanical strain during [...] Read more.
Objective assessment of low back pain (LBP) is challenging due to subtle, task-dependent movement impairments that are poorly captured by existing sensing technologies. Motion Tape (MT), which is a self-adhesive elastic fabric skin strain sensor, enables skin-conforming measurement of localized biomechanical strain during functional movement, but its discriminative utility for LBP remains unclear. We examine this question in a multi-sensor, multi-movement setting and analyze whether MT signals encode discriminative structure that distinguishes individuals with LBP from healthy controls. Using data from 20 participants performing 19 functional movements with six sensors, we evaluate movement-specific classification under a leave-pair-out protocol and examine which movements, sensor placements, and features are most informative. Our analysis reveals that group separation is highly selective: only a small subset of movements, most notably forward flexion, consistently supports accurate classification, while many movements remain at near-chance level. We find that temporal dynamics features help in resolving difficult cases that global strain statistics fail to separate, and that informative signals are spatially localized to the lower lumbar spine. In contrast, pretrained time-series foundation models show negligible sensitivity to participant-level structure in MT signals. Overall, the findings from this exploratory study establish when and how MT sensing can effectively differentiate individuals with LBP from healthy controls, providing a principled foundation for larger-scale validation. Full article
(This article belongs to the Special Issue Advanced Wearable, Vision-Based, and Intelligent Systems for Movement and Gait Analysis)
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16 pages, 2063 KB  
Article
Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace
by Seth Donahue, J.D. Peiffer, R. Tyler Richardson, Yishan Zhong, Shaun Q. Y. Tan, Benoit L. Marteau, Stephanie A. Russo, May D. Wang, R. James Cotton and Ross Chafetz
Sensors 2026, 26(11), 3421; https://doi.org/10.3390/s26113421 - 28 May 2026
Cited by 1 | Viewed by 634
Abstract
This study validates a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using monocular AI-driven Markerless Motion Capture (MMC). Objective validation of such techniques for clinically oriented tasks is essential to support their adoption in clinical motion analysis. Nine adults [...] Read more.
This study validates a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using monocular AI-driven Markerless Motion Capture (MMC). Objective validation of such techniques for clinically oriented tasks is essential to support their adoption in clinical motion analysis. Nine adults without impairments performed the standardized UERW task, reaching targets distributed across a virtual sphere centered on the torso and displayed via VR headset. Movements were simultaneously captured with a marker-based system and eight FLIR cameras; monocular analysis was applied to two videos representing frontal and offset camera configurations. Agreement was assessed by comparing the percentage workspacereached across six of eight workspace octants between the systems. The frontal camera demonstrated strong agreement with the marker-based reference (mean bias: 0.61±0.12% reachspace per octant), whereas the offset view underestimated workspace reached −5.66±0.45%. Depth-related errors in the frontal configuration were confined to posterior octants, whereas the offset view introduced inaccuracies in both contralateral and posterior octants. These findings support the feasibility of a frontal monocular camera for UERW assessment, particularly for anterior workspace evaluation. While posterior accuracy remains limited by depth estimation and anatomical occlusion errors, the overall results demonstrate clinical potential for practical, monocular-camera assessments. Full article
(This article belongs to the Special Issue Advanced Wearable, Vision-Based, and Intelligent Systems for Movement and Gait Analysis)
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21 pages, 2445 KB  
Article
Concurrent Validity of Two Inertial Measurement Unit Pipelines for Estimating Lumbar and Thoracic Kinematics During Lifting Tasks
by Samantha J. Snyder, Aditi Mannby and Dario Martelli
Sensors 2026, 26(9), 2639; https://doi.org/10.3390/s26092639 - 24 Apr 2026
Viewed by 531
Abstract
Lumbosacral and thoracolumbar kinematics are key risk factors for lifting-related low back pain, yet their measurement is typically restricted to motion capture laboratories. Inertial measurement units (IMUs) offer the potential to quantify spine kinematics in more naturalistic settings, but the validity of IMU-based [...] Read more.
Lumbosacral and thoracolumbar kinematics are key risk factors for lifting-related low back pain, yet their measurement is typically restricted to motion capture laboratories. Inertial measurement units (IMUs) offer the potential to quantify spine kinematics in more naturalistic settings, but the validity of IMU-based processing pipelines relative to optical motion capture (OMC) remains unclear. Nine healthy participants performed stoop, squat, free, and asymmetric lifting tasks while IMU and OMC data were simultaneously collected to evaluate the concurrent validity of two IMU pipelines: the proprietary MVN Analyze pipeline and an OpenSense pipeline using a validated OpenSim biomechanical model for lifting. Joint angles from both pipelines were compared against OMC-derived joint angles calculated using the same validated OpenSim model with one-way repeated-measures statistical parametric mapping (SPM) (α = 0.05), Bland–Altman analysis with Limits of Agreement (LoA) and 95% Confidence Intervals (CIs), and Concordance Correlation Coefficients (CCCs) with 95% CIs. Xsens MVN Analyze consistently overestimated flexion-extension at both spinal levels across all lift types (lumbosacral: RMSE ≤ 9.8°, bias ≤ −14.5°, LoA ≤ ±10°; thoracolumbar: RMSE ≤ 5.4°, bias ≤ −8.3°, LoA ≤ ±5°), with SPM confirming significant differences during the lifting and lowering phases of all lifting cycles. In contrast, processing Xsens data with OpenSense using the same biomechanical model as the OMC data yielded excellent agreement with OMC (RMSE ≤ 2.9°, bias ≤ 3°, LoA ≤ ±10°). CCC was poor to moderate, specifically in lateral bending and axial rotation planes, likely reflecting limited between-participant ROM variability. These results suggest that discrepancies are driven primarily by biomechanical model differences rather than sensor or sensor fusion limitations. Ultimately, when paired with an appropriate biomechanical model, XSens sensors show promise for practical field-based assessment of lifting biomechanics, potentially requiring only sensors at the chest and pelvis. Full article
(This article belongs to the Special Issue Advanced Wearable, Vision-Based, and Intelligent Systems for Movement and Gait Analysis)
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