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Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 603

Special Issue Editors


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Guest Editor
National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Interests: ambient assisted living; active & healthy ageing technologies; wearable sensors; signal processing; image processing; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Interests: ambient assisted living; active & healthy ageing technologies; wearable sensors; signal processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human motion analysis and gait analysis have traditionally been performed in laboratories under controlled conditions using expensive equipment. Wearable sensors present an easy-to-use and cheap way to perform both gait and human motion analysis, including in healthcare scenarios, where monitoring is vital.

Wearable sensors are an increasingly popular method for the quantification of performance and workload with mechanical and physiological parameters. A wide range of wearable sensors are commercially available, and when applied to gait analysis or motion analysis, they can provide kinetic and kinematic features, thus representing useful tools for clinicians, researchers and caregivers in real-life contexts.

Wearable smart devices and services can be applied in microelectronics, new sensing technologies and materials, transducers, signal processing, big data, cloud computing and artificial intelligence tools, making them attractive in biomechanics contexts for both real-life and real-time analysis.

This Special Issue will study the design, implementation, testing, benchmarking and use of wearable sensors and related infrastructures and services, including ambient assisted living, ambient intelligence and IoT paradigms, and reframe the sense of “Smart Living” to ensure inclusion, safety, comfort, high-quality care and healthcare and environmental sustainability. The Special Issue will cover technological issues related to the integration of hardware and processing aspects into wearable smart devices for motion analysis and health monitoring, including mobile, edge, fog and cloud computing.

We invite papers that include, among others, the following topics:

  • Posture and gait analysis;
  • Human daily motion analysis;
  • Gait analysis of elderly and disabled people;
  • Home care motion sensing and analysis;
  • Wearable sensors and related techniques for medical decision-making;
  • Wearable sensors and related techniques for motor diagnosis;
  • Wearable sensors and related techniques for human gait recognition;
  • Sensing technologies for ambulatory human motion analysis;
  • Advanced sensor signal processing;
  • Health monitoring systems;
  • Industry-related wearable sensors;
  • Innovative applications of wearable sensor systems.

Dr. Alessandro Leone
Dr. Gabriele Rescio
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. 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

  • wearable sensors
  • gait analysis
  • human motion
  • biomechanics
  • health monitoring
  • home care
  • assisted living
  • signal processing
  • cloud computing

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Published Papers (1 paper)

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Research

18 pages, 4517 KiB  
Article
Running Parameter Analysis in 400 m Sprint Using Real-Time Kinematic Global Navigation Satellite Systems
by Keisuke Onodera, Naoto Miyamoto, Kiyoshi Hirose, Akiko Kondo, Wako Kajiwara, Hiroshi Nakano, Shunya Uda and Masaki Takeda
Sensors 2025, 25(4), 1073; https://doi.org/10.3390/s25041073 - 11 Feb 2025
Abstract
Accurate measurement of running parameters, including the step length (SL), step frequency (SF), and velocity, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and [...] Read more.
Accurate measurement of running parameters, including the step length (SL), step frequency (SF), and velocity, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and practicality. This study introduces and evaluates two methods for estimating running parameters using real-time kinematic global navigation satellite systems (RTK GNSS) with 100 Hz sampling. Method 1 identifies mid-stance phases via vertical position minima, while Method 2 aligns with the initial contact (IC) events through vertical velocity minima. Two collegiate sprinters completed a 400 m sprint under controlled conditions, with RTK GNSS measurements validated against 3D video analysis and IMU data. Both methods estimated the SF, SL, and velocity, but Method 2 demonstrated superior accuracy, achieving a lower RMSE (SF: 0.205 Hz versus 0.291 Hz; SL: 0.143 m versus 0.190 m) and higher correlation with the reference data. Method 2 also exhibited improved performance in curved sections and detected stride asymmetries with higher consistency than Method 1. These findings highlight RTK GNSS, particularly the velocity minima approach, as a robust, drift-free, single-sensor solution for detailed per-step sprint analysis in outdoor conditions. This approach offers a practical alternative to IMU-based methods and enables training optimization and performance evaluation. Full article
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