1
|
Baklouti S, Chaker A, Rezgui T, Sahbani A, Bennour S, Laribi MA. A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment. SENSORS (BASEL, SWITZERLAND) 2024; 24:3419. [PMID: 38894211 PMCID: PMC11174619 DOI: 10.3390/s24113419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/18/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024]
Abstract
This study introduces a novel wearable Inertial Measurement Unit (IMU)-based system for an objective and comprehensive assessment of Work-Related Musculoskeletal Disorders (WMSDs), thus enhancing workplace safety. The system integrates wearable technology with a user-friendly interface, providing magnetometer-free orientation estimation, joint angle measurements, and WMSDs risk evaluation. Tested in a cable manufacturing facility, the system was evaluated with ten female employees. The evaluation involved work cycle identification, inter-subject comparisons, and benchmarking against standard WMSD risk assessments like RULA, REBA, Strain Index, and Rodgers Muscle Fatigue Analysis. The evaluation demonstrated uniform joint patterns across participants (ICC=0.72±0.23) and revealed a higher occurrence of postures warranting further investigation, which is not easily detected by traditional methods such as RULA. The experimental results showed that the proposed system's risk assessments closely aligned with the established methods and enabled detailed and targeted risk assessments, pinpointing specific bodily areas for immediate ergonomic interventions. This approach not only enhances the detection of ergonomic risks but also supports the development of personalized intervention strategies, addressing common workplace issues such as tendinitis, low back pain, and carpal tunnel syndrome. The outcomes highlight the system's sensitivity and specificity in identifying ergonomic hazards. Future efforts should focus on broader validation and exploring the relative influence of various WMSDs risk factors to refine risk assessment and intervention strategies for improved applicability in occupational health.
Collapse
Affiliation(s)
- Souha Baklouti
- Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, University of Sousse, Sousse 4023, Tunisia; (S.B.); (A.C.); (S.B.)
- ENOVA Robotics S.A., Novation City, Sousse 4023, Tunisia;
| | - Abdelbadia Chaker
- Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, University of Sousse, Sousse 4023, Tunisia; (S.B.); (A.C.); (S.B.)
| | - Taysir Rezgui
- Applied Mechanics, and Systems Research Laboratory (LASMAP), Tunisia Polytechnic School, University of Carthage, Tunis 2078, Tunisia;
| | - Anis Sahbani
- ENOVA Robotics S.A., Novation City, Sousse 4023, Tunisia;
- Institute for Intelligent Systems and Robotics (ISIR), CNRS, Sorbonne University, 75006 Paris, France
| | - Sami Bennour
- Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, University of Sousse, Sousse 4023, Tunisia; (S.B.); (A.C.); (S.B.)
| | - Med Amine Laribi
- Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, University of Sousse, Sousse 4023, Tunisia; (S.B.); (A.C.); (S.B.)
- Department of GMSC, Pprime Institute CNRS, University of Poitiers, UPR 3346, 86073 Poitiers, France
| |
Collapse
|
2
|
Mexi A, Kafetzakis I, Korontzi M, Karagiannakis D, Kalatzis P, Mandalidis D. Effects of Load Carriage on Postural Control and Spatiotemporal Gait Parameters during Level and Uphill Walking. SENSORS (BASEL, SWITZERLAND) 2023; 23:609. [PMID: 36679405 PMCID: PMC9863443 DOI: 10.3390/s23020609] [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: 07/30/2022] [Revised: 12/24/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Load carriage and uphill walking are conditions that either individually or in combination can compromise postural control and gait eliciting several musculoskeletal low back and lower limb injuries. The objectives of this study were to investigate postural control responses and spatiotemporal parameters of gait during level and uphill unloaded (UL), back-loaded (BL), and front-loaded (FL) walking. Postural control was assessed in 30 asymptomatic individuals by simultaneously recording (i) EMG activity of neck, thoracic and lumbar erector spinae, and rectus abdominis, (ii) projected 95% ellipse area as well as the anteroposterior and mediolateral trunk displacement, and (iii) spatiotemporal gait parameters (stride/step length and cadence). Measurements were performed during level (0%) and uphill (5, 10, and 15%) walking at a speed of 5 km h-1 without and with a suspended front pack or a backpack weighing 15% of each participant's body weight. The results of our study showed that postural control, as indicated by increased erector spinae EMG activity and changes in spatiotemporal parameters of gait that manifested with decreased stride/step length and increased cadence, is compromised particularly during level and uphill FL walking as opposed to BL or UL walking, potentially increasing the risk of musculoskeletal and fall-related injuries.
Collapse
Affiliation(s)
- Asimina Mexi
- Sports Physical Therapy Laboratory, Department of Physical Education and Sports Science, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
| | - Ioannis Kafetzakis
- Sports Physical Therapy Laboratory, Department of Physical Education and Sports Science, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
| | - Maria Korontzi
- Sports Physical Therapy Laboratory, Department of Physical Education and Sports Science, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
| | - Dimitris Karagiannakis
- Sports Physical Therapy Laboratory, Department of Physical Education and Sports Science, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
| | - Perikles Kalatzis
- Section of Informatics 1st Vocational Lyceum of Vari, Directorate of Secondary Education of East Attica, Hellenic Ministry of Education and Religious Affairs, 16672 Athens, Greece
| | - Dimitris Mandalidis
- Sports Physical Therapy Laboratory, Department of Physical Education and Sports Science, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
| |
Collapse
|
3
|
Jabri S, Carender W, Wiens J, Sienko KH. Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection. J Neuroeng Rehabil 2022; 19:132. [PMID: 36456966 PMCID: PMC9713134 DOI: 10.1186/s12984-022-01099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Vestibular deficits can impair an individual's ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers. METHODS Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task. RESULTS Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed. CONCLUSIONS These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.
Collapse
Affiliation(s)
- Safa Jabri
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
| | - Wendy Carender
- grid.412590.b0000 0000 9081 2336Department of Otolaryngology, Michigan Medicine, Ann Arbor, MI 48109 USA
| | - Jenna Wiens
- grid.214458.e0000000086837370Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - Kathleen H. Sienko
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
| |
Collapse
|
4
|
Gait Analysis in a Box: A System Based on Magnetometer-Free IMUs or Clusters of Optical Markers with Automatic Event Detection. SENSORS 2020; 20:s20123338. [PMID: 32545515 PMCID: PMC7348770 DOI: 10.3390/s20123338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/06/2020] [Accepted: 06/10/2020] [Indexed: 11/16/2022]
Abstract
Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it is necessary to overcome certain limitations, such as the need for magnetically controlled environments, which affect IMU systems, or the need for additional instrumentation to detect gait events, which affects IMUs and optical systems. We present a MoCap gait analysis system called Move Human Sensors (MH), which incorporates proposals to overcome both limitations and can be configured via magnetometer-free IMUs (MH-IMU) or clusters of optical markers (MH-OPT). Using a test-retest reliability experiment with thirty-three healthy subjects (20 men and 13 women, 21.7 ± 2.9 years), we determined the reproducibility of both configurations. The assessment confirmed that the proposals performed adequately and allowed us to establish usage considerations. This study aims to enhance gait analysis in daily clinical practice.
Collapse
|
5
|
Marin J, Blanco T, Marin JJ. Octopus: A Design Methodology for Motion Capture Wearables. SENSORS 2017; 17:s17081875. [PMID: 28809786 PMCID: PMC5580045 DOI: 10.3390/s17081875] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 08/09/2017] [Accepted: 08/09/2017] [Indexed: 02/04/2023]
Abstract
Human motion capture (MoCap) is widely recognised for its usefulness and application in different fields, such as health, sports, and leisure; therefore, its inclusion in current wearables (MoCap-wearables) is increasing, and it may be very useful in a context of intelligent objects interconnected with each other and to the cloud in the Internet of Things (IoT). However, capturing human movement adequately requires addressing difficult-to-satisfy requirements, which means that the applications that are possible with this technology are held back by a series of accessibility barriers, some technological and some regarding usability. To overcome these barriers and generate products with greater wearability that are more efficient and accessible, factors are compiled through a review of publications and market research. The result of this analysis is a design methodology called Octopus, which ranks these factors and schematises them. Octopus provides a tool that can help define design requirements for multidisciplinary teams, generating a common framework and offering a new method of communication between them.
Collapse
Affiliation(s)
- Javier Marin
- IDERGO (Research and Development in Ergonomics) Research Group, I3A (Aragon Institute of Engineering Research), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain.
| | - Teresa Blanco
- HOWLab (Human Openware Research Lab) Research Group, I3A (Aragon Institute of Engineering Research), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain.
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain.
| | - Jose J Marin
- IDERGO (Research and Development in Ergonomics) Research Group, I3A (Aragon Institute of Engineering Research), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain.
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain.
| |
Collapse
|