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Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8571

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Dr. Yi-Chung Lin

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Guest Editor

Sports Performance, Recovery, Injury & New Technologies (SPRINT) Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC 4014, Australia
Interests: biomechanics; musculoskeletal modeling; human locomotion; muscle and joint function; predictive simulation; sports science; orthopedic research

Special Issue Information

Dear Colleagues,

Three-dimensional motion capture has been widely used for quantifying human movement during various activities. While the motion capture technique is arguably the gold standard for conducting a detailed analysis of human motion, the cost of equipment and the requirement of specialized laboratories prevent it from having a greater impact. Wearable sensors, such as inertial measurement units (IMU), have increasingly attracted researchers’ attention because of their simplicity and ability to collect data in the field.

The goal of this Special Issue is to show how wearable sensors can be used to advance our knowledge in human movement analysis. With the increase in technology and computational power, the potential of wearable sensors for human motion is yet to be unlocked. The emphasis of this Special Issue is placed on real-world applications of wearable sensors in the areas of human movement. Applications of interest include (but are not limited to) the following:

Inertial sensor data validation during various activities (e.g., walking, running, jumping, and cycling);
Clinical applications (e.g., elderly fall prevention, rehabilitation, and movement abnormalities, such as stroke and cerebral palsy);
Workplace musculoskeletal injury prevention;
Sport performance and injury;
Developments of advanced sensor fusion algorithms.

Dr. Yi-Chung Lin
Guest Editor

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Keywords

wearable sensors
human movement
rehabilitation
workplace injury
posture
sport performance
sport injury

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Human Movement Monitoring Using Wearable Sensor Technology in Sensors (17 articles)

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Research

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12 pages, 681 KB

Open AccessArticle

Temporal Patterns of Wearable Accelerometer-Measured Physical Activity and Symptom Worsening in Knee Osteoarthritis: A 2-Year Longitudinal Study from the Osteoarthritis Initiative

by Junichi Kushioka, Ruopeng Sun and Matthew Smuck

Sensors 2026, 26(3), 982; https://doi.org/10.3390/s26030982 - 3 Feb 2026

Viewed by 478

Abstract

This study investigates the link between changes in physical activity (PA) measured by wearable accelerometers and the worsening of knee osteoarthritis (KOA) symptoms over two years. Using data from 782 participants in the Osteoarthritis Initiative accelerometer sub-study, PA was tracked with hip-worn ActiGraphs. Participants were classified as “worsening” if their Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score increased by >10 points and as “stable” otherwise. PA was categorized into daily counts and minutes spent in various intensity levels, and analyzed in 3 h intervals across the day. Of the participants, 123 (15.7%) experienced worsening symptoms. At baseline, both groups had similar characteristics aside from slower sit-to-stand times in the worsening group. Over two years, the worsening group had a greater decline in total daily activity counts (−18% vs. −10%) and more significant reductions during late afternoon and evening (15:00–21:00; −21% vs. −6%). This group also showed a notable decrease in gait speed, longer sit-to-stand times, and a trend towards greater medial joint space narrowing. These findings suggest that larger declines in PA, especially in activities in the late afternoon and evening, are associated with worsening KOA symptoms, although causality cannot be established. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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10 pages, 1219 KB

Open AccessArticle

Using an Electronic Goniometer to Assess the Influence of Single-Application Kinesiology Taping on Unstable Shoulder Proprioception and Function

by Ewa Bręborowicz, Izabela Olczak, Przemysław Lubiatowski, Piotr Ogrodowicz, Marta Ślęzak, Maciej Bręborowicz and Leszek Romanowski

Sensors 2025, 25(7), 2326; https://doi.org/10.3390/s25072326 - 6 Apr 2025

Viewed by 1848

Abstract

Background: Glenohumeral joint instability is associated with a proprioception deficit. Joint position sense can be improved through targeted exercises and kinesiology taping (KT). While previous studies have examined the effects of KT on proprioception, most have focused on the knee joint, with limited research on unstable shoulder joints. Most studies have used commonly available equipment (e.g., the Biodex system). An electronic goniometer, the “Propriometer”, is a useful tool for assessing proprioception in shoulder joint instability; however, its application in evaluating the effects of KT on shoulder proprioception remains unexplored. This study aimed to (1) assess the usability of the Propriometer for evaluating the effects of KT on unstable shoulders and (2) determine the impact of a single KT application on joint position sense and limb function in individuals with anterior, post-traumatic shoulder joint instability. Methods and Materials: The study included 30 individuals with anterior, unilateral, post-traumatic shoulder joint instability (8 women, 22 men, mean age 26 years). A control group consisted of 35 healthy volunteers (9 women, 26 men, mean age 24 years). Proprioception assessment (active joint position reproduction evaluation) was performed in both groups using the Propriometer, which measures joint position in real time with an accuracy of 0.1° across all axes. The study methodology was validated and used to examine shoulder proprioception. The current study focused on assessing the effects of KT, which had not been previously tested with this device Assessments were conducted before KT application and three days’ post-application. Additionally, patients completed the Western Ontario Shoulder Instability Index (WOSI) self-assessment questionnaire before and three days after the therapy. Results: Results of the mean joint position reproduction error indicate a proprioceptive deficit in patients with shoulder joint instability. However, the analyzed KT application did not show a significant change in the magnitude of the active joint position reproduction error. Conversely, KT therapy significantly improved patients’ subjective assessment of shoulder function and stability as measured by the WOSI. Conclusions: The Propriometer goniometer and testing methodology are effective tools for assessing the impact of KT on proprioception in shoulder instability. While KT application did not significantly influence shoulder proprioception, it did improve patients’ perceived joint stability and function. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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23 pages, 10659 KB

Open AccessArticle

A Fast and Low-Impact Embedded Orientation Correction Algorithm for Hand Gesture Recognition Armbands

by Andrea Mongardi, Fabio Rossi, Andrea Prestia, Paolo Motto Ros and Danilo Demarchi

Sensors 2025, 25(7), 2188; https://doi.org/10.3390/s25072188 - 30 Mar 2025

Cited by 3 | Viewed by 1674

Abstract

Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human–Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving

93.36

% average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm’s efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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11 pages, 1555 KB

Open AccessArticle

Impact of Playing Position on Competition External Load in Professional Padel Players Using Inertial Devices

by Ricardo Miralles, José F. Guzmán, Jesús Ramón-Llin and Rafael Martínez-Gallego

Sensors 2025, 25(3), 800; https://doi.org/10.3390/s25030800 - 29 Jan 2025

Cited by 9 | Viewed by 3087

Abstract

Padel is a racket sport that has grown internationally, both in the number of players and in the number of competitions. Inertial measurement devices enable a comprehensive analysis of competitive load in padel by providing kinematic variables that enhance players’ performance in this discipline. This study aimed to analyse the external load variables recorded with an inertial device in elite padel players, comparing metrics based on the players’ positions (left and right sides of the court). A total of 83 players were monitored during 23 matches of the professional circuit. The results revealed specific load metrics, including distance covered, frequency of accelerations and decelerations per hour, maximum speeds reached, and acceleration profiles relative to distance covered, which were all measured using the Wimu Pro™ device. Left-side players showed more frequent accelerations and decelerations per hour compared to right-side players. The results of this study will, on one hand, enable the adjustment of new specific parameters for professional padel training, such as acceleration and deceleration profiles, player load, and distances covered at explosive speeds. On the other hand, the results will provide a more objective evaluation of padel players’ performance based on their positions. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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Review

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26 pages, 3453 KB

Open AccessReview

The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices

by Anderson Souza Oliveira, Morteza Yaserifar and Cristina-Ioana Pîrșcoveanu

Sensors 2026, 26(8), 2502; https://doi.org/10.3390/s26082502 - 18 Apr 2026

Viewed by 516

Abstract

Ground reaction forces (GRFs) are essential for assessing running biomechanics, and the combination of wearable sensors and machine learning offers an accessible alternative for estimating GRFs outside controlled environments. This scoping review summarized current methods used to predict GRFs during running. A structured search (2019–2025) identified 36 studies, from which 37% did not report participant’s training status, and 59% of all participants were males. Treadmill running was assessed in 58% of studies, which included larger samples (median N = 28) and more steps/participant (median = 65) than overground studies (median N = 14; median = 32). Deep learning models, particularly LSTM and Bi-LSTM networks, were the most applied techniques, though presenting similar accuracies compared to classical regression methods. Vertical GRF predictions were the most accurate, while mediolateral GRF predictions remain challenging. GRF-derived variables such as peak forces, impact peaks, and impulses were predicted more accurately than region-dependent metrics like loading rates. Notably, no study validated treadmill-trained models on overground running, limiting real-world generalizability. Future work should prioritize larger, sex-balanced cohorts, improving prediction of mediolateral GRFs and loading rates, and explore validating treadmill-based models in overground conditions. In conclusion, although machine learning shows promise for GRF predictions, key methodological gaps must be addressed to enable robust, real-world applications. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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