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Inertial Sensing System for Motion Monitoring
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Inertial Sensing System for Motion Monitoring

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8049

Special Issue Editors

Prof. Dr. Andy Stamm

E-Mail Website
Guest Editor
1. Griffith School of Engineering, Griffith University, Nathan, QLD 4111, Australia
2. Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Cleve, Germany
Interests: inertial sensing system; sport monitoring
Prof. Dr. Ronny Hartanto

E-Mail Website
Guest Editor
Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Cleve, Germany
Interests: real-time feedback; IMU
Dr. Thomas H. Fickenscher

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Helmut Schmidt University-University of the Federal Armed Forces Hamburg, Hamburg, Germany
Interests: image guide technology; photosensitive semiconductor material

Special Issue Information

Dear Colleagues,

There has been growing interest in wearable sensor technologies over the past decade, with these technologies being used in sports to monitor and measure athletes during training, competition and recovery. Athletes and coaches not only use this technology to measure their current performance, but also to show areas of potential improvements and to aid with recovery phases after an injury occurs.

Inertial sensing technologies can provide athletes and coaches with accurate biometrics such as motion patterns, orientation in accordance with gravity, impacts, accelerations and decelerations, and velocities.

Therefore, this Special Issue aims to collect original research and review articles on the recent advances, technologies, solutions, applications, and new challenges in the field of “Inertial Sensing System for Motion Monitoring”.

Potential topics include, but are not limited to:

  • Inertial measurement system development;
  • Technology advancements in motion monitoring;
  • GPS;
  • Acceleration and rotation sensors;
  • Real-time feedback;
  • Technology-aided injury recovery;
  • Athlete performance analysis;
  • Sensor fusion;
  • Algorithm development and validation;
  • Reliability studies in motion sensing;
  • Data analytics;
  • Model-based analysis of motion;
  • Communication of inertial sensing systems;
  • On-sensor computations;
  • Live data streaming;
  • Inertial sensing systems in medicine;
  • Inertial sensing system manufacturing.

Prof. Dr. Andy Stamm
Prof. Dr. Ronny Hartanto
Dr. Thomas H. Fickenscher
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

  • IMU
  • inertial measurement unit
  • inertial sensing system
  • acceleration sensor
  • gyroscopes
  • magnetometer
  • sport monitoring
  • real-time feedback

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Published Papers (6 papers)

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Research

14 pages, 1163 KiB  
Article
Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks
by Xiongfeng Li, Limin Zou and Haojie Li
Sensors 2024, 24(13), 4208; https://doi.org/10.3390/s24134208 - 28 Jun 2024
Cited by 1 | Viewed by 801
Abstract
(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study consisted of two parts: firstly, 70 skilled [...] Read more.
(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study consisted of two parts: firstly, 70 skilled tai chi practitioners were used for movement recognition; secondly, 60 elderly males were used for an intervention study. IMU data were collected from skilled tai chi practitioners performing Bafa Wubu, and TCN models were constructed and trained to classify these movements. Elderly participants were divided into a precision intervention group and a standard intervention group, with the former receiving weekly real-time IMU feedback. Outcomes measured included balance, grip strength, quality of life, and depression. (3) Results: The TCN model demonstrated high accuracy in identifying tai chi movements, with percentages ranging from 82.6% to 94.4%. After eight weeks of intervention, both groups showed significant improvements in grip strength, quality of life, and depression. However, only the precision intervention group showed a significant increase in balance and higher post-intervention scores compared to the standard intervention group. (4) Conclusions: This study successfully employed IMU and TCN to identify Tai Chi movements and provide targeted feedback to older participants. Real-time IMU feedback can enhance health outcome indicators in elderly males. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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16 pages, 2000 KiB  
Article
Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model
by Tatsuki Koshio, Naoto Haraguchi, Takayoshi Takahashi, Yuse Hara and Kazunori Hase
Sensors 2024, 24(9), 2706; https://doi.org/10.3390/s24092706 - 24 Apr 2024
Viewed by 1583
Abstract
Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, [...] Read more.
Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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22 pages, 2365 KiB  
Article
The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®
by Mona Michaud, Alexandre Guérin, Marguerite Dejean de La Bâtie, Léopold Bancel, Laurent Oudre and Alexis Tricot
Sensors 2024, 24(8), 2413; https://doi.org/10.3390/s24082413 - 10 Apr 2024
Cited by 1 | Viewed by 1147
Abstract
The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in [...] Read more.
The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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12 pages, 2803 KiB  
Article
Obesity-Specific Considerations for Assessing Gait with Inertial Measurement Unit-Based vs. Optokinetic Motion Capture
by Julie Rekant, Scott Rothenberger and April Chambers
Sensors 2024, 24(4), 1232; https://doi.org/10.3390/s24041232 - 16 Feb 2024
Cited by 1 | Viewed by 1159
Abstract
Adults with obesity experience high rates of disability and rapid functional decline. Identifying movement dysfunction early can direct intervention and disrupt disability development; however, subtle changes in movement are difficult to detect with the naked eye. This study evaluated how a portable, inertial [...] Read more.
Adults with obesity experience high rates of disability and rapid functional decline. Identifying movement dysfunction early can direct intervention and disrupt disability development; however, subtle changes in movement are difficult to detect with the naked eye. This study evaluated how a portable, inertial measurement unit (IMU)-based motion capture system compares to a laboratory-based optokinetic motion capture (OMC) system for evaluating gait kinematics in adults with obesity. Ten adults with obesity performed overground walking while equipped with the OMC and IMU systems. Fifteen gait cycles for each participant were extracted for the 150 total cycles analyzed. Kinematics were compared between OMC and IMU across the gait cycles (coefficient of multiple correlations), at clinically significant time points (interclass correlations), and over clinically relevant ranges (Bland–Altman plots). Sagittal plane kinematics were most similar between systems, especially at the knee. Sagittal plane joint angles at clinically meaningful timepoints were poorly associated except for ankle dorsiflexion at heel strike (ρ = 0.38) and minimum angle (ρ = 0.83). All motions except for ankle dorsiflexion and hip abduction had >5° difference between systems across the range of angles measured. While IMU-based motion capture shows promise for detecting subtle gait changes in adults with obesity, more work is needed before this method can replace traditional OMC. Future work should explore standardization procedures to improve consistency of IMU motion capture performance. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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9 pages, 2050 KiB  
Article
A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer
by Joaquín Martín Marzano-Felisatti, Leonardo De Lucca, José Francisco Guzmán Luján, Jose Ignacio Priego-Quesada and José Pino-Ortega
Sensors 2024, 24(3), 988; https://doi.org/10.3390/s24030988 - 3 Feb 2024
Viewed by 1206
Abstract
Technological development has boosted the use of multi-sensor devices to monitor athletes’ performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM [...] Read more.
Technological development has boosted the use of multi-sensor devices to monitor athletes’ performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle’s handlebar—20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle’s handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00–1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00–1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00–1.00], p < 0.001). The Bland–Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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13 pages, 1822 KiB  
Article
Assessing the Effects of Mild Traumatic Brain Injury on Vestibular Home Exercise Performance with Wearable Sensors
by Kody R. Campbell, Jennifer L. Wilhelm, Prokopios Antonellis, Kathleen T. Scanlan, Natalie C. Pettigrew, Douglas N. Martini, James C. Chesnutt and Laurie A. King
Sensors 2023, 23(24), 9860; https://doi.org/10.3390/s23249860 - 16 Dec 2023
Viewed by 1465
Abstract
After a mild traumatic brain injury (mTBI), dizziness and balance problems are frequently reported, affecting individuals’ daily lives and functioning. Vestibular rehabilitation is a standard treatment approach for addressing these issues, but its efficacy in this population remains inconclusive. A potential reason for [...] Read more.
After a mild traumatic brain injury (mTBI), dizziness and balance problems are frequently reported, affecting individuals’ daily lives and functioning. Vestibular rehabilitation is a standard treatment approach for addressing these issues, but its efficacy in this population remains inconclusive. A potential reason for suboptimal outcomes is the lack of objective monitoring of exercise performance, which is crucial for therapeutic success. This study utilized wearable inertial measurement units (IMUs) to quantify exercise performance in individuals with mTBI during home-based vestibular rehabilitation exercises. Seventy-three people with mTBI and fifty healthy controls were enrolled. Vestibular exercises were performed, and IMUs measured forehead and sternum velocities and range of motions. The mTBI group demonstrated a slower forehead peak angular velocity in all exercises, which may be a compensatory strategy to manage balance issues or symptom exacerbation. Additionally, the mTBI group exhibited a larger forehead range of motion during specific exercises, potentially linked to proprioceptive deficits. These findings emphasize the usefulness of utilizing IMUs to monitor the quality of home-based vestibular exercises for individuals with mTBI and the potential for IMUs improving rehabilitation outcomes. Full article
(This article belongs to the Special Issue Inertial Sensing System for Motion Monitoring)
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