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Sensors and Wearable Device for Gait Analysis

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1278

Special Issue Editor


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Guest Editor
Institute of Digital Technologies for Personalized Healthcare, University of Applied Sciences and Arts of Southern Switzerland, 6962 Lugano-Viganello, Switzerland
Interests: wearable sensors; digital health; motion analysis; neurodegenerative diseases; remote monitoring
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Special Issue Information

Dear Colleagues,

Recent advancements in wearable technologies and embedded sensor systems have revolutionized gait analysis, transitioning it from a laboratory-constrained practice to a mobile, continuous, and real-world activity. State-of-the-art inertial measurement units (IMUs), pressure sensors, smart textiles, and multimodal platforms now facilitate detailed assessment of gait parameters in both clinical and free-living environments. These technologies support applications ranging from early detection of mobility impairments and fall risk to rehabilitation monitoring and performance optimization in sports. This Special Issue aims to showcase the latest innovations in sensors and wearable systems for gait analysis.

We invite original research and review articles focusing on novel sensor designs, data fusion techniques, real-time gait parameter estimation, machine learning approaches, and validation studies. Submissions addressing challenges such as sensor miniaturization, energy efficiency, user compliance, and robustness in heterogeneous populations and environments are particularly welcome. Interdisciplinary contributions bridging engineering, neuroscience, kinesiology, and digital health are encouraged.

Dr. Alessandro Puiatti
Guest Editor

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Keywords

  • wearable sensors
  • gait analysis
  • inertial measurement units (IMUs)
  • pressure sensing
  • smart textiles
  • sensor fusion
  • machine learning
  • mobile health (mHealth)
  • real-world gait monitoring
  • gait disorders
  • rehabilitation technologies
  • human motion analysis
  • remote patient monitoring

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Published Papers (1 paper)

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Research

17 pages, 1760 KB  
Article
Sensor-Derived Trunk Stability and Gait Recovery: Evidence of Neuromechanical Associations Following Intensive Robotic Rehabilitation
by Hülya Şirzai, Yiğit Can Gökhan, Güneş Yavuzer and Hande Argunsah
Sensors 2026, 26(2), 573; https://doi.org/10.3390/s26020573 - 15 Jan 2026
Viewed by 758
Abstract
This quantitative observational study with pre–post design aimed to examine joint-specific kinematic adaptations and the relationship between trunk stability and spatiotemporal gait parameters following intensive robotic rehabilitation. A total of 12 neurological patients completed 16 sessions of gait training using the Tecnobody Smart [...] Read more.
This quantitative observational study with pre–post design aimed to examine joint-specific kinematic adaptations and the relationship between trunk stability and spatiotemporal gait parameters following intensive robotic rehabilitation. A total of 12 neurological patients completed 16 sessions of gait training using the Tecnobody Smart Gravity Walker. Pre- and post-training kinematic data were collected for bilateral hip and knee flexion–extension, trunk flexion–extension, trunk lateral flexion, and center-of-gravity displacement. Waveforms were normalized to 100% stride. Paired t-tests assessed pre–post differences, and correlations examined associations between trunk stability and gait performance. Significant increases were found in right hip flexion–extension (t = 3.44, p < 0.001), trunk flexion–extension (t = 9.49, p < 0.001), and center-of-gravity displacement (t = 15.15, p < 0.001), with reduced trunk lateral flexion (t = –8.64, p < 0.001). Trunk flexion–extension correlated with gait speed (r = 0.74), step length (r = 0.68), and stride length (r = 0.71); trunk lateral flexion correlated with cadence (r = 0.66) and stride length (r = 0.70). Intensive robotic rehabilitation improved trunk and hip kinematics, supporting trunk stability as an important biomechanical correlate of gait recovery. Sensor-derived metrics revealed strong neuromechanical coupling between postural control and locomotion in neurological patients. Full article
(This article belongs to the Special Issue Sensors and Wearable Device for Gait Analysis)
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