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Wearable Inertial Sensors for Human Movement Analysis

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

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 10488

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Special Issue Editors

Dr. Britta Berg-Johansen

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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

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Keywords

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

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Published Papers (6 papers)

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Research

15 pages, 317 KB

Open AccessArticle

Integrating Inertial Sensors to Assess Physical Performance and In-Match Demands for the International Selection of Cerebral Palsy Football Players

by Juan F. Maggiolo, Raúl Reina, Manuel Moya-Ramón and Iván Peña-González

Sensors 2025, 25(18), 5787; https://doi.org/10.3390/s25185787 - 17 Sep 2025

Viewed by 729

Abstract

This study analyzed the physical performance (via field tests) and in-match physical responses (via wearable inertial sensors) of national and international cerebral palsy (CP) football players competing in Spain’s First Division. A total of 80 players (FT1: n = 22; FT2: n = 48; FT3: n = 10) completed sprinting, change of direction, and dribbling tests. In-match data from 74 players were collected across 56 official matches. Players were classified as “international” (candidates for the national team) or “national” (non-candidates). Statistical analyses identified performance differences and predictors of international selection using multiple discriminant analysis. International players outperformed national ones in sprinting, agility, and dribbling, especially in FT1 and FT2 classes (p < 0.05; large effect sizes). In-match data (analyzed for FT2 only) showed that international players covered more distance at all intensities and executed more high-intensity actions (e.g., maximal velocity, ball contacts). High-intensity running was the strongest predictor of international status (74.5%, Wilks’ λ = 0.86, p = 0.01). Change of direction and dribbling were key discriminators in FT1 and FT2, while no clear predictor emerged in FT3. These findings support the use of physical tests and wearable technology for evidence-based talent identification and selection in CP football. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)

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18 pages, 1645 KB

Open AccessArticle

Validation of Inertial Measurement Units for Measuring Lower-Extremity Kinematics During Squat–Pivot and Stoop–Twist Lifting Tasks

by Rutuja A. Kulkarni, Rajit Banerjee, Vicki Z. Wang, Marcel Oliart, Verity Rampulla, Prithvi Das and Alicia M. Koontz

Sensors 2025, 25(18), 5673; https://doi.org/10.3390/s25185673 - 11 Sep 2025

Cited by 1 | Viewed by 1220

Abstract

Optokinetic motion capture (OMC) is the gold standard for measuring the kinematics associated with lifting posture. Unfortunately, limitations exist, including cost, portability, and marker occlusion. The purpose of this study is to evaluate the agreement between OMC and inertial measurement units (IMUs) for quantifying joint kinematics during squat–pivot and stoop–twist lifting tasks. Ten unimpaired adults wearing both IMUs and OMC markers performed 24 lifting trials. Correlation coefficients and Root Mean Square Error (RMSE) between IMU and OMC time-series signals were computed for trunk and lower-extremity joints. Peak values obtained from each system during each trial were analyzed via Bland–Altman plots. Results show high correlations for trunk, knee, and ankle flexion angles (>0.9) and ankle rotation angles (>0.7). Moderate correlation was found for trunk axial rotation and lateral flexion angles (0.5–0.7). RMSE was under 9° for each angle. Biases between systems ranged from 0.3° to 16°. Both systems were able to detect statistically significant differences in peak angles between the two postures (p < 0.05). IMUs show promise for recording field data on complex lifting tasks. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)

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14 pages, 1167 KB

Open AccessArticle

REEV SENSE IMUs for Gait Analysis in Stroke: A Clinical Study on Lower Limb Kinematics

by Thibault Marsan, Sacha Clauzade, Xiang Zhang, Nicolas Grandin, Tatiana Urman, Evan Linton, Ingy Elsayed-Aly, Catherine E. Ricciardi and Robin Temporelli

Sensors 2025, 25(16), 5123; https://doi.org/10.3390/s25165123 - 18 Aug 2025

Cited by 1 | Viewed by 1292

Abstract

Human gait analysis is essential for clinical evaluation and rehabilitation monitoring, particularly in post-stroke individuals, where joint kinematics provide valuable insights into motor recovery. While optical motion capture (OMC) is the gold standard, its high cost and restricted use in laboratory settings limit its accessibility. This study aimed to evaluate the accuracy of REEV SENSE, a novel magnetometer-free inertial measurement unit (IMU), in capturing knee and ankle joint angles during overground walking in post-stroke individuals using assistive devices. Twenty participants with chronic stroke walked along a 10-m walkway with their usual assistive device (cane or walker), while joint kinematics were simultaneously recorded using OMC and IMUs. Agreement between the systems was assessed using the mean absolute error, root mean square error, 95% confidence intervals, and Pearson’s correlation coefficient. Knee angles measured with the IMUs showed a strong correlation with the OMC (r > 0.9) and low errors (MAE < 5°), consistent with clinical acceptability. Ankle angle accuracy was lower for participants using walkers, while knee measurements remained stable regardless of the assistive device. These findings demonstrate that REEV SENSE IMUs provide clinically relevant kinematic data and support their use as a practical wearable tool for gait analysis in real-world or remote clinical settings. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)

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26 pages, 19598 KB

Open AccessArticle

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 3113

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 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 KB

Open AccessArticle

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 2079

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, 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1071

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. 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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