Physical Ergonomics and Human Factors最新文献

{"title":"What is the repercussions of the introduction of a cobot on productivity and biomechanical constraints on operators in a collaborative task?","authors":"Kévin Bouillet, S. Lemonnier, F. Clanché, G. Gauchard","doi":"10.54941/ahfe1003042","DOIUrl":"https://doi.org/10.54941/ahfe1003042","url":null,"abstract":"Collaborative robots, or cobots, are robots designed to closely collaborate with a human in a shared workplace. Introducing a cobot in a collaborative work situation aims to preserve productivity without impair the operator’s health, even improve them. Musculoskeletal disorders (MSDs), main occupation diseases in Industry, are pathologies of multifactorial origin, as biomechanical solicitations are one of them (e.g., posture, repetitiveness).This paper evaluates the repercussions of the introduction of a cobot in a collaborative task with two studies: first to compare a task in collaboration with a cobot or a human co-worker and second to analyze the impact of pace (i.e., rhythm and leader); both on productivity, quality of interactions, operator’s posture and attentional demand.Thirty-four participants in Study 1 and twenty in Study 2 performed a collaborative task inspired by assembly lines in factories, in collaboration with a co-worker. In Study 1, this co-worker was either a human or a YuMi cobot, participants were always the leader; in Study 2, the co-worker was always the YuMi cobot; the leader was either the participants or the cobot, and in this last case, different paces were imposed. Productivity was measured with the number of products manufactured, quality interactions with the rate of idleness and activity of participant and co-worker and interactions rate between them, participant’s posture with joints angles and RULA evaluation and attentional demand with performance at a second task.In Study 1, productivity was less important with the cobot than with a human, with less interactions and with higher attentional demand. However, posture was less risky with the cobot for operator health in terms of MSDs. In Study 2, productivity and attentional demand increased with the pace until a threshold at the mean-imposed pace. Posture was riskier for operator health in terms of MSDs only for the fastest imposed pace.In Study 1, deterioration of productivity and quality of the interactions with the cobot co-worker was mainly due to the limited capabilities of the cobot. Results about posture were also linked with pace, but differences were also observed during operator’s activity with less biomechanical solicitations working with the cobot than with the human for the same actions. Leading or following the pace seemed to not influence these variables in Study 2. Thus, the results of Study 2 seemed to indicate that the differences between human-human and human-cobot interactions observed in Study 1 were mainly due to the slower pace due to the cobot, except for the better posture which could be linked with the introduction of the cobot.Even though the experiment took place in a laboratory, the task was strongly inspired by the field and the results are consistent with those in the literature. These results therefore allow us to establish solid hypotheses that can be generalized to real situations in a factory, especially concerning the improveme","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130733344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Muscle Fatigue During Dynamic Exercises based on Surface Electromyography Signals Using Gaussian Classifier","authors":"Yeok Tatt Cheah, Ka Wing Frances Wan, J. Yip","doi":"10.54941/ahfe1002597","DOIUrl":"https://doi.org/10.54941/ahfe1002597","url":null,"abstract":"Muscle fatigue is shown to be associated with incidence of musculoskeletal injuries found with sports training and competition. The real-time detection of fatigue onset allows preventative measures to be taken in time to minimize injuries. In this paper, we aim to provide a framework that classifies muscle fatigue based on surface electromyography (sEMG) features extracted during dynamic exercises. This includes the use of data segmentation, real-time-compatible data normalization, a principal component analysis (PCA) based feature reduction and Gaussian classifier methods.An experiment has been carried out to acquire the sEMG signals of the upper two pairs of rectus abdominis muscles of four healthy adult volunteers during weighted decline and bench-assisted sit-ups. The collected sEMG signals are then segmented into concentric and eccentric segments by using the inertial measurement unit (IMU) data. Eight commonly used sEMG features are extracted from each segment. We fit two Gaussian models (GMs) on the distribution of fatigued and non-fatigued data samples and show that the GM can utilize this information to predict the number of repetitions possible before task failure. We fit another set of GM on a reduced feature space by projecting the data onto principal component axes obtained through singular value decomposition (SVD). By projecting the features onto the first two principal axes, we achieve similar accuracy and f1-scores compared to the GM by using 6 handpicked features. This reduction in the feature space greatly reduces the training samples necessary for such class-imbalanced datasets. This classifier can also be directly used in the real-time detection of muscle fatigue during dynamic movements, which can be adopted in applications in sports, workplaces, and rehabilitation sciences. These frequency-time characteristics also provide insight into the function of low-level feature extractors when developing deep learning models to identify muscle fatigue.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122542988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plantar Soft Tissue Stiffness Automatic Estimation in Ultrasound Imaging using Deep learning","authors":"Yori Pusparani, B. Liau, Yih-Kuen Jan, Hsu-Tang Cheng, Peter Ardhianto, Fityanul Akhyar, Chi-Wen Lung, Chih-Yang Lin","doi":"10.54941/ahfe1002612","DOIUrl":"https://doi.org/10.54941/ahfe1002612","url":null,"abstract":"\u0000 Preventing diabetic foot ulcers (DFU) is critical for diabetes mellitus (DM) patients. Increased stiffness of plantar foot may cause higher plantar pressure leading to a higher risk of DFU. Soft tissue stiffness can be determined by measuring the soft tissue thickness with indentation depth and stress. Therefore, we hypothesized that the deep learning model could detect the ultrasound image pixel change under soft tissue compression. This study aimed to apply the deep learning model to analyze the ultrasound image pixel thickness of plantar foot, then predict the soft tissue indentation depth and loading force for estimating the stiffness. This study has developed a motor-driven ultrasound indentation system to apply programmable compression and simultaneously assess soft tissue mechanical properties and responses in indentation depth and loading force. In addition, the effective Young's modulus was calculated to characterize mechanical properties of soft tissues in the first metatarsal head. The deep learning method employed the YOLOv5x model to train and detect the small object in the indentation depth, such as ultrasound image pixel changes. Finally, the dataset images were processed with labeling annotation from the soft tissue indentation depth and loading force. The deep learning results showed 0.995 in mean Average Precision (mAP), 0.999 in precision, 1.000 in Recall, and 0.013 in Loss. A significant correlation was found between the ultrasound image pixel changes and soft tissue indentation depth (r = 0.98, p < 0.05). Furthermore, a significant correlation was observed between the ultrasound image pixel changes and the loading force in the first metatarsal head (r = 0.85, p < 0.05). The validation and prediction models were lower than the training models in the effective Young's modulus results. However, the results of the initial modulus were similar between the three models. Our findings recommend that applying deep learning in the ultrasound image can predict soft tissue indentation depth and loading force to calculate the stiffness of the plantar foot.\u0000","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"564 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of risk factors of upper limb musculoskeletal disorders in a meat processing plant","authors":"Diogo Cunha dos Reis, Antônio Renato Pereira Moro","doi":"10.54941/ahfe1003043","DOIUrl":"https://doi.org/10.54941/ahfe1003043","url":null,"abstract":"The aim of this study was to evaluate the risks associated with repetitive movements of the upper limbs in different meat processing tasks of a pig slaughterhouse, using the OCRA Checklist. The study was conducted in a Brazilian pig slaughterhouse with 1,000 workers, divided into two work shifts. To evaluate the risks associated with repetitive movements of the upper limbs, 10% of the workforce was assessed while carrying out their work tasks, using the checklist proposed by the OCRA method. Descriptive statistics and the Student t-test (SPSS 17.0) were applied to compare the risks between both sides of the workers’ bodies (p≤0.05). There were 39 work activities analyzed from the productive sectors. The average of occupational repetitive actions performed by workers was 54.5±20.8 per minute, representing 7 points on the OCRA scale (0 to 10 points). The average score of the OCRA Checklist was 18.8±6.0 (medium risk). The scores for the right upper limb (18.6 - medium risk) differed statistically (p=0.016) from the left upper limb (13.8 - medium risk). Five work tasks were considered high risk, 29 were classified as medium risk, one as low risk, one as very low risk and three as acceptable risk. Performing simulations in 32 of the 35 activities made it possible to reduce the UL-WMSD risk to very low levels, by only decreasing the work pace. In three of the activities, a very low risk level could not be achieved by only reducing the work pace, due to the high demand for strength required to perform these tasks. These results suggest that most pig processing tasks, classified as high (13%) and medium risk (74%), predispose workers to a greater probability of developing upper limb work-related musculoskeletal disorders (>21.5% probability for high risk and 10.8 to 21.5% for medium risk). Simulations of decreasing the work pace showed the effectiveness of this organizational measure to reduce the risk of UL-WMSDs.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing semiautomatic Rapid Upper Limb Assessments (RULA): Azure Kinect versus RGB-based machine vision algorithm","authors":"Antonio Maria Coruzzolo, Francesco Lolli, Nazareno Amicosante, Hrishikesh Kumar, P. Thupaki, S. Agarwal","doi":"10.54941/ahfe1002596","DOIUrl":"https://doi.org/10.54941/ahfe1002596","url":null,"abstract":"Correctly using a rapid upper limb assessment for working postures is crucial to avoid musculoskeletal disorders. Although motion capture technologies and in particular depth cameras are widely used, they cannot be used in large-scale industrial environments due to their high cost and their performance greatly impacted by the surrounding environment. We thus compared the effectiveness of a commercial machine vision algorithm (named ErgoEdge) based on an RGB camera against an application here developed based on the depth camera Microsoft Azure Kinect for the RULA evaluation (AzKRULA). We conducted an experiment where fifteen static postures were evaluated with Microsoft Azure Kinect and ErgoEdge, and the results were also compared with those of an expert in ergonomics. This experiment showed a substantial agreement between the solutions provided by the semi-automatic RULA evaluation and the ergonomic expert and between AzKRULA and ErgoEdge. At the same time, it showed that the RGB camera must be placed on the side of the worker due to the difficulties of the machine vision algorithm in reconstructing from a frontal view, important joint angles in 2D space (e.g., to evaluate the neck and trunk), which can invalidate the RULA evaluation provided by ErgoEdge. Moreover, the RULA evaluation with AzKRULA and ErgoEdge highlighted the need for an in-depth study into the thresholds of the secondary factors (i.e., all the factors for the RULA evaluation that are not computed from the thresholds of joint angles) as the highest differences between the two evaluations and the ergonomist one arises on them.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126182409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of Back Exoskeleton’s Effectiveness on Transportation Maintenance Workers during Lifting Activities","authors":"Xingzhou Guo, Xinran Hu, Yunfeng Chen","doi":"10.54941/ahfe1003044","DOIUrl":"https://doi.org/10.54941/ahfe1003044","url":null,"abstract":"Safety statistics from Indiana Department of Labor showed that the transportation and warehousing industry has the second highest number of reported occupational fatalities (26) in 2020. One major cause of occupational fatalities is ergonomic issues including excessive force, repetitive motion, and awkward posture. These ergonomic issues have already been extensively studied and corresponding solutions were developed for the building construction activities. However, transportation activities are different from building construction activities in duration, intensity, and frequency. In addition, there lacks studies exploring whether the proposed solutions to ergonomic issues of building construction could also solve the ergonomic issues of transportation activities. To this end, field experiments were conducted with 29 transportation maintenance workers between August 9th 2023 and September 23rd 2023 at a transportation maintenance unit of Indiana Department of Transportation (INDOT). Lifting bags of dry concrete mix was identified as the activity of top concern, according to (1) the perception of which activity most likely to cause an injury to back or shoulder, (2) the frequency of performing the activity, and (3) the number of historical sprain injuries caused by the activity. Therefore, the participants were asked to lift 12 bags of three different weight of dry concrete mix with and without a back exoskeleton. Specifically, three different weights of bags include: 80-pound bags (weight of bags that INDOT maintenance workers mostly use), 50-pound bags (weight of bags that INDOT maintenance workers sometimes use and which is recommended by Recommended Weight Limit equation under the ideal condition), and 31.5-pound bags (recommended weight of bags based on applying values from real lifting practice of INDOT maintenance workers into the Recommended Weight Limit equation). The participants need to lift bags from a pallet to a truck with liftgate, have a five-minute short break, offload those bags from the truck with liftgate to the ground, and then have a 20-minute long break. Skin conductance and heart rate, as the key indicators of physical fatigue, were measured during the lifting activity. In addition, perceived level of muscle exertion was also collected by using a Borg 6-20 scale during two experiment breaks of each trial for indicating level of fatigue risk level from low to very high. After performing a paired t-test of collected data, it shows that the back exoskeleton does not significantly help workers reduce physical fatigue risks while transportation maintenance workers lift 31.5-pound bags. However, the back exoskeleton can significantly lower the physical fatigue risks when transportation maintenance workers lift 50-pound bags and 80-pound bags. This study not only fills the gap of exploring the effectiveness of back exoskeleton implementation in transportation maintenance activities, but also provides evidence and practical recomm","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Detective Gloves (PROSAFE) for Reducing Carpal Tunnel Syndrome Injuries","authors":"Saed Amer, Asmaa Alsereidi, M. Aldhanhani","doi":"10.54941/ahfe1002595","DOIUrl":"https://doi.org/10.54941/ahfe1002595","url":null,"abstract":"Carpal Tunnel Syndrome is a common health issue that targets the Median Nerve in the Carpal Tunnel Area, causing severe damage that affects the health of the patient and the overall performance of the originations. In this project, we came up with a new innovative smart detective glove that would be able to reduce the effects of CTS using special types of sensors and other supporting tools. The glove is a customer need-driven product that has some important features including measuring and detecting bending angles of the hand, analyzing the hand postures, and warning the users, in addition to that it has to measure the amount of pressure applied to the Carpal Tunnel Area specifically to the Median Nerve. It is cost-effective, light in weight, environmentally friendly, adjustable, and an easy-to-use device. Our approach for designing the glove was the double diamond theory which consists of four main stages starting with discovering our goals and defining the main components of the design followed up with the development of the design concept and its working principle and delivering ourPROSAFE smart glove at the end. Cost Analysis was used to check the feasibility of the design and how effective it is in terms of cost-saving. This glove will be able to predict, follow up CTS progression, warn the users and suggest the best hand posture for specific repetitive work.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121746105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Comprehensive Human Factor and Ergonomics Checklist for Workplace Inspections Using a Macroergonomics Approach","authors":"Shalinda Shafie, Shamsul Bahri Mohd Tamrin, Ng Yee Guan, Dayana Hazwani Mohd Suadi Nata","doi":"10.54941/ahfe1003028","DOIUrl":"https://doi.org/10.54941/ahfe1003028","url":null,"abstract":"Lack of time and manpower as well as a fragmented inspection process are some of the challenges faced by industrial hygiene inspectors from the Malaysian Department of Occupational Safety and Health (DOSH) in carrying out their duties especially regarding industrial hygiene and ergonomics related workplace inspections. To streamline the inspection process and ensure a comprehensive assessment of the workplace, a study was carried out to develop a comprehensive Human Factors and Ergonomics (HFE) checklist. To our knowledge, there is no publicly available checklist covering all three ergonomics domains (physical, cognitive and organizational) that is designed to facilitate workplace inspection and regulatory enforcement process by industrial hygienists and inspectors.The study was divided into several phases. During the first phase, a literature review was carried out looking at existing checklists, assessment methods and industrial hygiene and ergonomics standards that fell within the scope of our study. During this phase, the applicable local regulations and current enforcement methods by inspectors were also analyzed. Based on this a draft checklist was developed. The checklist utilizes a macroergonomics approach where the different socio-technical components of the work system are assessed: the design of each task, the personnel carrying out the task, the physical environment the task is being carried out as well as the management systems and culture of the organization. To encourage a more holistic approach to assessing HFE, the checklist also borrows techniques from diverse fields outside of industrial hygiene such as human error engineering, organizational psychology, and industrial design.The second phase of the study involved multiple discussion sessions with DOSH inspectors, academics and professionals in various fields and industry to further refine the checklist so that it can be used both by inspectors carrying out workplace inspections as well industrial hygienists and safety practitioners to identify areas for improvement in their own workplaces. The checklist was also tested at by safety practitioners at 2 workplaces and by DOSH inspectors at 7 workplaces. During the tests, both regulatory inspectors and industry safety practitioners gave positive feedback on the approach and scope of the checklist. The inclusion of lesser-known elements related to cognitive ergonomics and organizational psychology were well-received even though these are not explicitly regulated by local law. However, since a macroergonomics approach was used in the checklist, there were concerns raised regarding the level of detail and time required to complete the checklist.The checklist is still being developed and the next phase of the project which will start in 2023 includes a Pilot Program where in-depth testing of the checklist will be undertaken at multiple workplaces in different industries throughout the country.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134264949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expansion of the System for Collecting Information on Hospital Incidents - Aiming to revitalizing on-site management","authors":"Yuriko Imura, Yusaku Okada","doi":"10.54941/ahfe1003034","DOIUrl":"https://doi.org/10.54941/ahfe1003034","url":null,"abstract":"In medical facilities and general hospitals, a variety of information regarding incidents in patient care is collected and analysed. However, due to the large differences in knowledge and experience of on-site risk managers, previous studies have shown several problems with the data collected. Underlying these problems is the analyst's limited knowledge of human factors, IT, and management. However, it is very difficult to give more time and cost for safety training to on-site risk managers. Therefore, in this study, we decided to organize the results of past research on human error factor analysis from the perspective of on-site management. As a result, we were able to obtain a set of elements [elements for activation of on-site activities] for improving the ability of on-site risk managers to recognize, the willingness of on-site risk managers to participate in medical safety activities, and the management level of on-site risk managers. Based on the \"Elements for Activation of On-Site Activities\", we developed a prototype of an incident reporting support system.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131774537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cycling Stability and Symmetry using a Corrective Bib Short","authors":"Senne Henderieckx, Alexander Van Gastel, J. Vleugels, Sam Smets, S. Verwulgen","doi":"10.54941/ahfe1002594","DOIUrl":"https://doi.org/10.54941/ahfe1002594","url":null,"abstract":"In cycling, biomechanical posture optimization strives to improve core stability and symmetry in the lower and upper extremities to raise power output. In addition, the shape and pose of the cyclists determine the projected frontal area, which is the major factor influencing drag during cycling. In this study, a high-fidelity prototype garment was developed that includes kinetic bands and proprioceptive devices to adjust biomechanical posture during cycling. The aim is to measure improved projected frontal area, stability, and symmetry as a result of wearing a corrective cycling garment. Thirty participants were gathered under strict exclusion criteria to ensure a representative sample of the population. Two exploratory studies were conducted: experimental and reference measurements of 1) 11 cyclists’ pedal balance and projected frontal area, and 2) 5 cyclists’ biomechanical movements through an optical motion tracking system. The results indicate an improved pedal balance and deteriorated stability and symmetry for the corrective bib short.","PeriodicalId":130337,"journal":{"name":"Physical Ergonomics and Human Factors","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123836512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}