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García-Luna MA, Ruiz-Fernández D, Tortosa-Martínez J, Manchado C, García-Jaén M, Cortell-Tormo JM. Transparency as a Means to Analyse the Impact of Inertial Sensors on Users during the Occupational Ergonomic Assessment: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:298. [PMID: 38203160 PMCID: PMC10781389 DOI: 10.3390/s24010298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/19/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The literature has yielded promising data over the past decade regarding the use of inertial sensors for the analysis of occupational ergonomics. However, despite their significant advantages (e.g., portability, lightness, low cost, etc.), their widespread implementation in the actual workplace has not yet been realized, possibly due to their discomfort or potential alteration of the worker's behaviour. This systematic review has two main objectives: (i) to synthesize and evaluate studies that have employed inertial sensors in ergonomic analysis based on the RULA method; and (ii) to propose an evaluation system for the transparency of this technology to the user as a potential factor that could influence the behaviour and/or movements of the worker. A search was conducted on the Web of Science and Scopus databases. The studies were summarized and categorized based on the type of industry, objective, type and number of sensors used, body parts analysed, combination (or not) with other technologies, real or controlled environment, and transparency. A total of 17 studies were included in this review. The Xsens MVN system was the most widely used in this review, and the majority of studies were classified with a moderate level of transparency. It is noteworthy, however, that there is a limited and worrisome number of studies conducted in uncontrolled real environments.
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Affiliation(s)
- Marco A. García-Luna
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Daniel Ruiz-Fernández
- Department of Computer Science and Technology, University of Alicante, 03690 Alicante, Spain;
| | - Juan Tortosa-Martínez
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Carmen Manchado
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Miguel García-Jaén
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Juan M. Cortell-Tormo
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
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2
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Chen H, Schall MC, Martin SM, Fethke NB. Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist. SENSORS (BASEL, SWITZERLAND) 2023; 23:7053. [PMID: 37631592 PMCID: PMC10458653 DOI: 10.3390/s23167053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively.
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Affiliation(s)
- Howard Chen
- Industrial & Systems Engineering and Engineering Management Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA
| | - Mark C. Schall
- Department of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA;
| | - Scott M. Martin
- Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA;
| | - Nathan B. Fethke
- Department of Occupational & Environmental Health, The University of Iowa, Iowa City, IA 52242, USA;
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3
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Fang Z, Woodford S, Senanayake D, Ackland D. Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6535. [PMID: 37514829 PMCID: PMC10386307 DOI: 10.3390/s23146535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion-extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction-retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.
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Affiliation(s)
- Zhou Fang
- Department of Biomedical Engineering, The University of Melbourne, Melbourne 3052, Australia; (Z.F.); (S.W.); (D.S.)
| | - Sarah Woodford
- Department of Biomedical Engineering, The University of Melbourne, Melbourne 3052, Australia; (Z.F.); (S.W.); (D.S.)
| | - Damith Senanayake
- Department of Biomedical Engineering, The University of Melbourne, Melbourne 3052, Australia; (Z.F.); (S.W.); (D.S.)
- Department of Mechanical Engineering, The University of Melbourne, Melbourne 3052, Australia
| | - David Ackland
- Department of Biomedical Engineering, The University of Melbourne, Melbourne 3052, Australia; (Z.F.); (S.W.); (D.S.)
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4
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Chen H, Schall MC, Fethke NB. Gyroscope vector magnitude: A proposed method for measuring angular velocities. APPLIED ERGONOMICS 2023; 109:103981. [PMID: 36739779 DOI: 10.1016/j.apergo.2023.103981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/07/2022] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
High movement velocities are among the primary risk factors for work-related musculoskeletal disorders (MSDs). Ergonomists have commonly used two methods to calculate angular movement velocities of the upper arms using inertial measurement units (accelerometers and gyroscopes). Generalized velocity is the speed of movement traveled on the unit sphere per unit time. Inclination velocity is the derivative of the postural inclination angle relative to gravity with respect to time. Neither method captures the full extent of upper arm angular velocity. We propose a new method, the gyroscope vector magnitude (GVM), and demonstrate how GVM captures angular velocities around all motion axes and more accurately represents the true angular velocities of the upper arm. We use optical motion capture data to demonstrate that the previous methods for calculating angular velocities capture 89% and 77% relative to our proposed method.
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Affiliation(s)
- Howard Chen
- Industrial & Systems Engineering and Engineering Management Department, The University of Alabama in Huntsville, Huntsville, AL, USA; Department of Mechanical Engineering, Auburn University, Auburn, AL, USA.
| | - Mark C Schall
- Department of Industrial & Systems Engineering, Auburn University, Auburn, AL, USA
| | - Nathan B Fethke
- Department of Occupational & Environmental Health, The University of Iowa, Iowa City, IA, USA
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5
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Lind CM, Abtahi F, Forsman M. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094259. [PMID: 37177463 PMCID: PMC10181376 DOI: 10.3390/s23094259] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/14/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.
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Affiliation(s)
- Carl Mikael Lind
- IMM Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Farhad Abtahi
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 141 57 Huddinge, Sweden
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, 141 86 Huddinge, Sweden
| | - Mikael Forsman
- IMM Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 141 57 Huddinge, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, 113 65 Stockholm, Sweden
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6
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Hoareau D, Fan X, Abtahi F, Yang L. Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements. SENSORS (BASEL, SWITZERLAND) 2023; 23:3969. [PMID: 37112309 PMCID: PMC10142577 DOI: 10.3390/s23083969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth-skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth-skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.
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Affiliation(s)
- Damien Hoareau
- Department of Mechatronics, École Normale Supérieure de Rennes, 35170 Bruz, France
- Laboratoire SATIE, CNRS UMR 8029, École Normale Supérieure de Rennes, 35170 Bruz, France
| | - Xuelong Fan
- Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 4, SE-171 77 Stockholm, Sweden
| | - Farhad Abtahi
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, SE-141 57 Huddinge, Sweden
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, SE-141 57 Huddinge, Sweden
| | - Liyun Yang
- Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 4, SE-171 77 Stockholm, Sweden
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, SE-141 57 Huddinge, Sweden
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7
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Bai L, Pepper MG, Wang Z, Mulvenna MD, Bond RR, Finlay D, Zheng H. Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 23:360. [PMID: 36616958 PMCID: PMC9823748 DOI: 10.3390/s23010360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/19/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.
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Affiliation(s)
- Lu Bai
- School of Computing, Ulster University, Belfast BT15 1ED, UK
| | - Matthew G. Pepper
- School of Engineering, University of Kent, Canterbury CT2 7NZ, UK
- Department of Medical Physics, East Kent Hospitals University NHS Foundation Trust, Canterbury CT1 3NG, UK
| | - Zhibao Wang
- School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
| | | | - Raymond R. Bond
- School of Computing, Ulster University, Belfast BT15 1ED, UK
| | - Dewar Finlay
- School of Engineering, Ulster University, Belfast BT15 1ED, UK
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast BT15 1ED, UK
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8
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Roeder SK, Wilder DG, Fethke NB. Novel methods to detect impacts within whole-body vibration time series data. ERGONOMICS 2022; 65:1609-1620. [PMID: 35148664 DOI: 10.1080/00140139.2022.2041735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
We present three candidate mathematical models for detecting impacts within time series accelerometer data in the context of whole-body vibration (WBV). In addition to WBV, data included recordings of erector spinae muscle activity and trunk posture collected during use of agricultural machines in a previous study. For each model, we evaluated associations between several mechanical and biomechanical variables at the time of predicted impact onset and the odds of subsequently observing a bilateral response of the erector spinae muscles. For all models, trunk posture at the time of impact onset was strongly associated with an observed bilateral muscle response; these associations were not observed when impacts were randomly assigned. Results provide a framework for describing the number and magnitudes of impacts that may help overcome ambiguities in current exposure metrics, such as the vibration dose value, and highlight the importance of considering posture in the evaluation of occupational WBV exposures. Practitioner summary: Common metrics of exposure to whole-body vibration do not quantify the number or magnitudes of impacts within time series accelerometer data. Three candidate impact detection methods are presented and evaluated using real-world data collected during use of agricultural machines. Results highlight the importance of considering posture when evaluating vibration exposure.
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Affiliation(s)
- Shamus K Roeder
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - David G Wilder
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
| | - Nathan B Fethke
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
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9
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Forsman M, Fan X, Rhen IM, Lind CM. Mind the gap - development of conversion models between accelerometer- and IMU-based measurements of arm and trunk postures and movements in warehouse work. APPLIED ERGONOMICS 2022; 105:103841. [PMID: 35917697 DOI: 10.1016/j.apergo.2022.103841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/09/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
Sensor type (accelerometers only versus inertial measurement units, IMUs) and angular velocity computational method (inclination versus generalized velocity) have been shown to affect the measurements of arm and trunk movements. This study developed models for conversions between accelerometer and IMU measurements of arm and trunk inclination and between accelerometer and IMU measurements of inclination and generalized (arm) velocities. Full-workday recordings from accelerometers and IMUs of arm and trunk postures and movements from 38 warehouse workers were used to develop 4 angular (posture) and 24 angular velocity (movement) conversion models for the distributions of the data. A power function with one coefficient and one exponent was used, and it correlated well (r2 > 0.999) in all cases to the average curves comparing one measurement with another. These conversion models facilitate the comparison and merging of measurements of arm and trunk movements collected using the two sensor types and the two computational methods.
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Affiliation(s)
- Mikael Forsman
- School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-141 57, Huddinge, Sweden; IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Stockholm County Council, SE-113 65, Stockholm, Sweden
| | - Xuelong Fan
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden.
| | - Ida-Märta Rhen
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Stockholm County Council, SE-113 65, Stockholm, Sweden; Department of Industrial and Materials Science, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Carl Mikael Lind
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden
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10
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Schall MC, Chen H, Cavuoto L. Wearable inertial sensors for objective kinematic assessments: A brief overview. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2022; 19:501-508. [PMID: 35853137 DOI: 10.1080/15459624.2022.2100407] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Mark C Schall
- Department of Industrial and Systems Engineering, Auburn University, Auburn, Alabama
| | - Howard Chen
- Department of Mechanical Engineering, Auburn University, Auburn, Alabama
| | - Lora Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
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11
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Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies. SENSORS 2022; 22:s22041690. [PMID: 35214592 PMCID: PMC8874503 DOI: 10.3390/s22041690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 11/26/2022]
Abstract
Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMUnorm and IMUflex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMUnorm and <4.1°/s for IMUflex. During wrist deviation and pronation/supination, all methods showed errors, where the IMUnorm method had the largest overestimations. For simulated work tasks, the IMUflex method had small bias and better accuracy than the IMUnorm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMUflex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures.
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12
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Zhang X, Schall MC, Chen H, Gallagher S, Davis GA, Sesek R. Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts. APPLIED ERGONOMICS 2022; 98:103579. [PMID: 34507084 DOI: 10.1016/j.apergo.2021.103579] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
Wearable inertial sensors may be used to objectively quantify exposure to some physical risk factors associated with musculoskeletal disorders. However, concerns regarding their potential negative effects on user safety and satisfaction remain. This study characterized the self-reported daily discomfort, distraction, and burden associated with wearing inertial sensors on the upper arms, trunk, and dominant wrist of 31 manufacturing workers collected over 15 full work shifts. Results indicated that the workers considered the devices as generally comfortable to wear, not distracting, and not burdensome to use. Exposure to non-neutral postures (discomfort, right arm, beta = 0.02; trunk, beta = -0.01), non-cyclic tasks (distraction, beta = -0.26), and higher body mass indices (discomfort, beta = 0.05; distraction, beta = 0.02) contributed to statistically significant (p < 0.05), albeit practically small increases in undesirable ratings. For instance, for each additional percentage of time working with the right arm elevated ≥60°, self-reported discomfort ratings increased 0.02 cm on a standard 10 cm visual analog scale. Female workers reported less discomfort and distraction while wearing the sensors at work than males (discomfort, beta = -0.93; distraction, beta = -0.3). In general, the low ratings of discomfort, distraction, and burden associated with wearing the devices during work suggests that inertial sensors may be suitable for extended use among manufacturing workers.
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Affiliation(s)
- Xuanxuan Zhang
- Department of Applied Science and Technology, College of Engineering and Computer Sciences, Marshall University, Huntington, WV, USA; Department of Industrial and Systems Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
| | - Mark C Schall
- Department of Industrial and Systems Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
| | - Howard Chen
- Department of Mechanical Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
| | - Sean Gallagher
- Department of Industrial and Systems Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
| | - Gerard A Davis
- Department of Industrial and Systems Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
| | - Richard Sesek
- Department of Industrial and Systems Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.
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13
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Akhavanhezaveh A, Abbasi‐Kesbi R. Diagnosing gait disorders based on angular variations of knee and ankle joints utilizing a developed wearable motion sensor. Healthc Technol Lett 2021; 8:118-127. [PMID: 34584746 PMCID: PMC8450179 DOI: 10.1049/htl2.12015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/20/2021] [Accepted: 05/28/2021] [Indexed: 12/16/2022] Open
Abstract
Here, a sensory motion system is developed to diagnose gait disorders using the estimation of angular variations in the knee and ankle joints. The sensory system includes two transmitter sensors and a central node, where each transmitter comprises three sensors of accelerometer, gyroscopes, and magnetometer to estimate the angular movements in the ankle and knee joints. By using a proposed filter, the angular variation is estimated in a personal computer employing the raw data of the motion sensors that are sent by the central node. The obtained results of the presented filter in comparison to an actual reference illustrate that the root mean square error is less than 1.01, 1.34, and 1.61 degrees, respectively, for the angles of ϕ and θ and ψ that illustrate an improvement of 40% than the previous work. Moreover, a quantity value is defined based on the correlation between knee and ankle angles that show the amount of correctness in gating. Thus, the proposed system can be utilized for people who suffer problems in gait and help them to improve their movements.
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Affiliation(s)
| | - Reza Abbasi‐Kesbi
- MEMS & NEMS DepartmentFaculty of New Sciences and TechnologiesUniversity of TehranTehranIran
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Fan X, Lind CM, Rhen IM, Forsman M. Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements. SENSORS 2021; 21:s21165527. [PMID: 34450967 PMCID: PMC8401405 DOI: 10.3390/s21165527] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 12/15/2022]
Abstract
Accelerometer-based inclinometers have dominated kinematic measurements in previous field studies, while the use of inertial measurement units that additionally include gyroscopes is rapidly increasing. Recent laboratory studies suggest that these two sensor types and the two commonly used angular velocity computational methods may produce substantially different results. The aim of this study was, therefore, to evaluate the effects of sensor types and angular velocity computational methods on the measures of work postures and movements in a real occupational setting. Half-workday recordings of arm and trunk postures, and movements from 38 warehouse workers were compared using two sensor types: accelerometers versus accelerometers with gyroscopes-and using two angular velocity computational methods, i.e., inclination velocity versus generalized velocity. The results showed an overall small difference (<2° and value independent) for posture percentiles between the two sensor types, but substantial differences in movement percentiles both between the sensor types and between the angular computational methods. For example, the group mean of the 50th percentiles were for accelerometers: 71°/s (generalized velocity) and 33°/s (inclination velocity)-and for accelerometers with gyroscopes: 31°/s (generalized velocity) and 16°/s (inclination velocity). The significant effects of sensor types and angular computational methods on angular velocity measures in field work are important in inter-study comparisons and in comparisons to recommended threshold limit values.
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Affiliation(s)
- Xuelong Fan
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (X.F.); (I.-M.R.); (M.F.)
| | - Carl Mikael Lind
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (X.F.); (I.-M.R.); (M.F.)
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, SE-141 57 Huddinge, Sweden
- Correspondence:
| | - Ida-Märta Rhen
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (X.F.); (I.-M.R.); (M.F.)
- Centre for Occupational and Environmental Medicine, Stockholm County Council, SE-113 65 Stockholm, Sweden
- Department of Industrial and Materials Science, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Mikael Forsman
- IMM Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden; (X.F.); (I.-M.R.); (M.F.)
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, SE-141 57 Huddinge, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, SE-113 65 Stockholm, Sweden
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15
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Schall MC, Zhang X, Chen H, Gallagher S, Fethke NB. Comparing upper arm and trunk kinematics between manufacturing workers performing predominantly cyclic and non-cyclic work tasks. APPLIED ERGONOMICS 2021; 93:103356. [PMID: 33454432 PMCID: PMC9298156 DOI: 10.1016/j.apergo.2021.103356] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 12/30/2020] [Accepted: 01/04/2021] [Indexed: 05/27/2023]
Abstract
Musculoskeletal disorders (MSDs) are common among manufacturing workers. Exposure to non-neutral postures and high movement speeds associated with MSDs among manufacturing workers may depend on the extent of the variability in the work tasks performed (i.e., predominantly "cyclic" versus "non-cyclic" work). The objectives of this study were to (i) compare mean levels of full-shift exposure summary metrics based on both posture and movement speed between manufacturing workers performing predominantly cyclic (n = 18) and non-cyclic (n = 17) tasks, and (ii) explore patterns of between- and within-worker exposure variance and between-minute (within-shift) exposure level and variation within each group. Inertial sensors were used to measure exposures for up to 15 full shifts per participant. Results indicated (i) substantially higher upper arm and trunk movement speeds among workers performing predominantly cyclic tasks relative to workers performing non-cyclic tasks despite similar postures, and (ii) greater exposure variability both between and within workers in the non-cyclic group.
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Affiliation(s)
- Mark C Schall
- Auburn University, Department of Industrial and Systems Engineering, 3323F Shelby Center for Engineering Technology, Auburn, AL, 36849-5346, USA.
| | - Xuanxuan Zhang
- Marshall University, Department of Applied Sciences and Technology, One John Marshall Drive, Huntington, 25755, WV, USA.
| | - Howard Chen
- Auburn University, Department of Mechanical Engineering, 1418 Wiggins Hall, Auburn, AL, 36849-5346, USA.
| | - Sean Gallagher
- Auburn University, Department of Industrial and Systems Engineering, 3304 Shelby Center for Engineering Technology, Auburn, AL, 36849-5346, USA.
| | - Nathan B Fethke
- University of Iowa, Department of Occupational and Environmental Health, S347 College of Public Health Building, Iowa City, IA, 52242, USA.
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16
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A New Quaternion-Based Kalman Filter for Human Body Motion Tracking Using the Second Estimator of the Optimal Quaternion Algorithm and the Joint Angle Constraint Method with Inertial and Magnetic Sensors. SENSORS 2020; 20:s20216018. [PMID: 33113983 PMCID: PMC7660286 DOI: 10.3390/s20216018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/29/2020] [Accepted: 10/20/2020] [Indexed: 11/22/2022]
Abstract
Human body motion tracking is a key technique in robotics, virtual reality and other human–computer interaction fields. This paper proposes a novel simple-structure Kalman filter to improve the accuracy of human body motion tracking, named the Second EStimator of the Optimal Quaternion Kalman Filter (E2QKF). The new algorithm is the combination of the Second Estimator of the Optimal Quaternion (ESOQ-2) algorithm, the linear Kalman filter and the joint angle constraint method. In the proposed filter, the ESOQ-2 algorithm is used to produce an observation quaternion by preprocessing accelerometer and magnetometer measurements. The compensation for the accelerometer added in the ESOQ-2 algorithm is to eliminate the influence of human body motion acceleration included in the results. The state vector of the filter is the quaternion, which is calculated with gyroscope measurements, and the Kalman filter is to calculate the optimal quaternion by fusing the state quaternion and the observation quaternion. Therefore, the filter becomes a simple first-order linear system model, which avoids the linearization error of measurement equations and reduces the computational complexity. Furthermore, the joint angle constraint is considered in the proposed algorithm, which makes the results more accurate. To verify the accuracy of the proposed algorithm, inertial/magnetic sensors are used to perform the upper limb motion experiment, and the result of E2QKF (without joint angle constraint) is compared with an optical motion capture system and two traditional methods. Test results demonstrate the effectiveness of the proposed filter: the root mean square error (RMSE) of E2QKF is less than 2.0° and the maximum error is less than 4.6°. The result of E2QKF (with joint angle constraint) is compared with E2QKF (without joint angle constraint). Test results demonstrate the superiority of E2QKF (with joint angle constraint): the joint angle constraint method can further improve the accuracy of human body motion tracking.
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