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Wearable Inertial Sensors for Human Movement Analysis
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Wearable Inertial Sensors for Human Movement Analysis

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1095

Special Issue Editors

Dr. Britta Berg-Johansen

E-Mail Website
Guest Editor
Biomedical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
Interests: Orthopedic biomechanics; digital health; wearables devices; human motion biomechanics
Prof. Dr. Stephen Klisch

E-Mail Website
Guest Editor
Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
Interests: orthopedic biomechanics; injury prevention; sports biomechanics; gait and balance biomechanics; wearable sensors

Special Issue Information

Dear Colleagues,

Developments in wearable sensing technologies, such as inertial measurement units (IMUs), have opened up new horizons for more accessible, continuous, and non-invasive health monitoring. Traditional human movement analysis requires a visit to a clinic or motion analysis lab, and often requires expensive equipment and trained personnel. This Special Issue aims to gather original research studies that explore the use of wearable sensors to revolutionize the field of personalized human movement analysis. The long-term goal of these studies should be to enhance accessible prevention and/or treatment strategies for musculoskeletal injuries and abnormalities in every-day real-world settings. Research may address the use of wearable sensors in human movement analysis, including analyses of gait, balance, sports activities, fall risk, rehabilitation, and more. This Special Issue is well within the scope of “Sensors”, as it explores the specific application of sensors in remote human movement monitoring.

Dr. Britta Berg-Johansen
Prof. Dr. Stephen Klisch
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.

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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

  • sensors
  • wearable devices
  • digital health
  • musculoskeletal
  • movement
  • motion analysis
  • inertial measurement units

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Published Papers (3 papers)

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Research

26 pages, 19598 KiB  
Article
Validation of Smartphones in Arbitrary Positions Against Force Plate Standard for Balance Assessment
by German Jack Ellsworth, Stephen M. Klisch, Britta Berg-Johansen and Eric Ocegueda
Sensors 2025, 25(9), 2639; https://doi.org/10.3390/s25092639 - 22 Apr 2025
Viewed by 203
Abstract
Balance assessment is a key metric for tracking the health and fall risk of individuals with balance impairment. Leveraging wearable sensors and mobile devices can increase clinical accessibility to objective balance metrics. Previous work has been conducted validating center of mass (COM) acceleration [...] Read more.
Balance assessment is a key metric for tracking the health and fall risk of individuals with balance impairment. Leveraging wearable sensors and mobile devices can increase clinical accessibility to objective balance metrics. Previous work has been conducted validating center of mass (COM) acceleration metrics from mobile devices against the gold standard force plate center of pressure (COP) position; however, most studies have been restricted to devices being placed close to the subject’s COM. In this study, rigid body kinematics and the inverted pendulum model were used to develop a novel methodology for calculating COM acceleration using mobile devices in arbitrary positions, as well as an approach for conversion of COM measurements to COP position for direct validation with force plate measurements. Validation of this methodology included a direct comparison of smartphone and force plate results for COM accelerations and COP positions, as well as statistical comparisons using Spearman’s rank correlation. The results show strong analysis performance for both approaches during a subject’s intentional swaying, with more limited results in cases of little motion. The strong performance warrants future work to further improve accessibility by removing dependence on motion capture systems or replacing them with cost-effective alternatives. The accurate tracking of COM acceleration and COP position information for mobile devices at arbitrary positions increases the flexibility for future mobile or at-home balance assessments. Full article
(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)
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26 pages, 18812 KiB  
Article
ErgoReport: A Holistic Posture Assessment Framework Based on Inertial Data and Deep Learning
by Diogo R. Martins, Sara M. Cerqueira, Ana Pombeiro, Alexandre Ferreira da Silva, Ana Maria A. C. Rocha and Cristina P. Santos
Sensors 2025, 25(7), 2282; https://doi.org/10.3390/s25072282 - 3 Apr 2025
Viewed by 396
Abstract
Awkward postures are a significant contributor to work-related musculoskeletal disorders (WRMSDs), which represent great social and economic burdens. Various posture assessment tools assess WRMSD risk but fall short in providing an elucidating risk breakdown to expedite the typical time-consuming ergonomic assessments. Quantifying, automating, [...] Read more.
Awkward postures are a significant contributor to work-related musculoskeletal disorders (WRMSDs), which represent great social and economic burdens. Various posture assessment tools assess WRMSD risk but fall short in providing an elucidating risk breakdown to expedite the typical time-consuming ergonomic assessments. Quantifying, automating, but also complementing posture risk assessment become crucial. Thus, we developed a framework for a holistic posture assessment, able to, through inertial data, quantify the ergonomic risk and also qualitatively identify the posture leading to it, using Deep Learning. This innovatively enabled the generation of a report in a graphical user interface (GUI), where the ergonomic score is intuitively associated with the postures adopted, empowering workers to learn which are the riskiest postures, and helping ergonomists and managers to redesign critical work tasks. The continuous posture assessment also considered the previous postures’ impact on joint stress through a kinematic wear model. As use case, thirteen subjects replicated harvesting and bricklaying, work tasks of the two activity sectors most affected by WRMSDs, agriculture and construction, and a posture assessment was conducted. Three ergonomists evaluated this report, considering it very useful in improving ergonomic assessments’ effectiveness, expeditiousness, and ease of use, with the information easily understandable and reachable. Full article
(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)
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11 pages, 1224 KiB  
Article
The Influence of the Inertial Motor Unit Location (Lumbosacral vs. Thoracic Regions) on the External Load Registered During Badminton Matches
by Juan García-López, José Pino-Ortega, Jaime Fernández-Fernández and José Vicente García-Tormo
Sensors 2025, 25(6), 1910; https://doi.org/10.3390/s25061910 - 19 Mar 2025
Viewed by 290
Abstract
The use of inertial motor units (IMUs) to monitor external training loads during training and competition has grown, particularly in racket sports like badminton. Previous studies highlighted the influence of sensor location on external load measurements, with the lumbosacral region identified as optimal. [...] Read more.
The use of inertial motor units (IMUs) to monitor external training loads during training and competition has grown, particularly in racket sports like badminton. Previous studies highlighted the influence of sensor location on external load measurements, with the lumbosacral region identified as optimal. However, IMUs are often placed dorsally between the scapulae. This study examined the impact of IMU placement (lumbosacral vs. thoracic) on external load recordings during two simulated badminton matches. Sixteen junior international-level players (10 males, 6 females) participated in matches designed to replicate worst-case scenarios (2 × 35 min, 15 min rest). IMUs located on the lumbosacral joint (L) and thoracic area (T) recorded data combining Ultra-Wideband and acceleration technologies. The results showed higher total and sprint distances in T than L (1.0–3.6%, pη2 = 0.089–0.182). Small differences were noted for accelerations and decelerations (1.5%, pη2 = 0.057) with no significant differences in speed. Conversely, L showed higher values for total impacts and player load (34.6–49.8%, pη2 = 0.861–0.868). The findings reveal slight discrepancies in distance and speed based on placement but significant differences in impacts and player loads, warranting further investigation. Full article
(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)
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