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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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Aqueveque P, Gutierrez M, Retamal G, Germany E, Pena G, Gomez B, Ortega-Bastidas P. Development of a Platform to Assess the Risk of Musculoskeletal Disorders in Manual Load Handling Activities - Preliminary Results. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082705 DOI: 10.1109/embc40787.2023.10340262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Risk identification on workstations is a crucial step to prevent the occurrence of musculoskeletal disorders (MSD) in workers. The available methods and tools used by ergonomists to assess and estimate the risk related to manual handling of loads under repetitive work cycles are usually biased by the inter-evaluator error that can lead to a subjective determination of work-related risks due to the application of, mainly, observational methods. This paper shows the preliminary results of a platform to assess the risk of musculoskeletal disorders during manual load-handling tasks using an instrumented system and using the National Institute for Occupational Safety & Health (NIOSH) method. Eight healthy subjects were measured during lifting activities using an optical-based and inertial-based motion capture systems. The developed software implements a semi-automated instrumented version of the NIOSH method, helping the evaluator with automated calculations of body segment locations, displacements and joint angles making it possible to obtain a objective risk classification. Also, we achieved a reduction of 85% in the time for the estimation of the necessary factors for the digital evaluation methodology, making the proposed platform a promising and attractive alternative for its application in real environments for risk assessments.Occupational health relevance- This work proposes an assistance tool for the detection of musculoskeletal disorders in activities related to manual handling of loads, essential to initiate modification strategies in the workplace, reduce the occurrence of occupational diseases and reduce the time of risk classification.
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Gurumoorthy KB, Rajasekaran AS, Kalirajan K, Gopinath S, Al-Turjman F, Kolhar M, Altrjman C. Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:4924. [PMID: 37430838 DOI: 10.3390/s23104924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/02/2023] [Accepted: 05/08/2023] [Indexed: 07/12/2023]
Abstract
Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.
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Affiliation(s)
- Kambatty Bojan Gurumoorthy
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Arun Sekar Rajasekaran
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Kaliraj Kalirajan
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Samydurai Gopinath
- Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore 641105, Tamilndu, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Manjur Kolhar
- Department Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al Kharj 11990, Saudi Arabia
| | - Chadi Altrjman
- Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Vahedi Z, Kheiri SK, Hajifar S, Lamooki SR, Sun H, Megahed FM, Cavuoto LA. The relationship between ratings of perceived exertion (RPE) and relative strength for a fatiguing dynamic upper extremity task: A consideration of multiple cycles and conditions. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2023; 20:136-142. [PMID: 36799881 PMCID: PMC11063909 DOI: 10.1080/15459624.2023.2180512] [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] [Indexed: 05/03/2023]
Abstract
The goal of this study was to evaluate the relationship between ratings of perceived exertion (RPE) and relative strength with respect to baseline for a fatiguing free dynamic task targeting the upper extremity, namely simulated order picking, and determine whether the relationship remains the same for different conditions (i.e., pace and weight) and with fatigue. Fourteen participants (seven males, seven females) performed four sessions that included two 45-min work periods separated by 15 min of rest. The work periods involved picking weighted bottles from shoulder height and packaging them at waist height for four combinations of bottle mass and picking rate: 2.5 kg-15 bottles per minute (bpm), 2.5 kg-10 bpm, 2.5 kg-5 bpm, and 1.5 kg-15 bpm. Participants reported their RPEs every 5 min and performed a maximum isometric shoulder flexion exertion every 9 min. Pearson product-moment correlation was used to evaluate the linear relationship between RPE and relative strength for each subject and work period. Then, the effects of condition and work period on the average relationship were assessed using a repeated-measures analysis of variance (ANOVA). For the first 45-min period, there were no significantly different correlations between RPE and relative strength across conditions (average r = -0.62 (standard deviation = 0.38); p = 0.57). There was a significant decrease in average correlation for the second work period (r = -0.39 (0.53)). These results suggest that individual subjective responses consistently increase while relative strength declines when starting from a non-fatigued state. However, correlations are weaker when re-engaging in work following incomplete recovery. Thus, starting fatigue levels should be accounted for when considering the expected relationship between RPE and relative strength.
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Affiliation(s)
- Zahra Vahedi
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
| | - Setareh Kazemi Kheiri
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
| | - Sahand Hajifar
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
| | - Saeb Ragani Lamooki
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York
| | - Hongyue Sun
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
| | | | - Lora A. Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
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Chatzis T, Konstantinidis D, Dimitropoulos K. Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture. SENSORS (BASEL, SWITZERLAND) 2022; 22:6051. [PMID: 36015812 PMCID: PMC9416453 DOI: 10.3390/s22166051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/03/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods.
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