A comparative evaluation of the wearable augmented reality-based data presentation interface and traditional methods for data entry tasks

Data entry is a ubiquitous task performed in today's offices. Persistent data entry is linked with high workload and fatigue due to poor ergonomic workplace design and poor posture. This study aims to alleviate data entry operators' workload and improve data entry performance by applying wearable augmented reality (AR) technology to data entry tasks. An AR-based interface was developed and used to present data to the participants, who entered the data in web-based data entry forms. A total of eighteen participants performed data entry tasks to evaluate the AR interface with traditional methods – extra desktop monitor and paper-based data presentation methods. Each method's performance was judged on the task completion time, typing error rate, workload, and usability. The usability and overall perceived workload while using an AR interface for data entry were similar to the traditional way of using paper, despite the additional burden due to the weight of the AR headset. AR interface did not perform better than the extra desktop monitor interface for usability and overall perceived workload. The results from this study can be utilized to design AR devices that are suited for data entry tasks.     

Ergonomic postural assessment using a new open-source human pose estimation technology (OpenPose)

Observational ergonomic postural assessment methods have been commonly used to evaluate the risks of musculoskeletal disorders. Researchers have proposed semi-automatic methods using Kinect, known for limitations with body occlusions and non-frontal tracking. Meanwhile, new human pose estimation methods have been actively developed, and a popular open-source technology is OpenPose. This study aims to propose the OpenPose-based system for computing joint angles and RULA/REBA scores and validate against the reference motion capture system, and compare its performance to the Kinect-based system. Recordings of 10 participants performing 12 experimental tasks under different conditions: with/without body occlusions and tracked from frontal/non-frontal views were analyzed. OpenPose showed good performance under all task conditions, whereas Kinect performed significantly worse than OpenPose especially at cases with body occlusions or non-frontal tracking. The findings suggested that OpenPose could be a promising technology to measure joint angles and conduct semi-automatic ergonomic postural assessments in the real workspace where the conditions are often non-ideal.     

Comparison of OWAS,RULA and REBA for assessing potential work-related musculoskeletal disorders

This study compared three representative observational methods for assessing musculoskeletal loadings: Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on 209 cases of upper-body musculoskeletal disorders (MSDs) diagnosed by medical doctors. The most awkward/stressful posture in each participant's tasks was assessed using these techniques. Postural loadings were rated more highly by the RULA than by the OWAS and REBA (p < 0.01). The chi-square test and logistic regression analysis showed that only RULA grand score and action level, and REBA action level were associated with MSD work-relatedness (p < 0.01, p < 0.05, and p < 0.05, respectively). The percentage concordant values of the logistic model for the RULA grand score and action level were 52.4% and 44.8%, respectively, while the percentage concordant value for the REBA action level was 22.1%. Therefore, the RULA may be the best system for estimating the postural loads and work-relatedness of MSDs.Relevance to industryWork-related musculoskeletal disorders are the leading cause of workplace disability in the developed countries. For preventing the disorders, quantification of musculoskeletal loads is required.     

Accuracy improvement of a 3D passive laser tracker for the calibration of industrial robots

The laser tracker has been used as the mainstream instrument for the position accuracy calibration of industrial robots for quite a long time. However, due to the complexity of the built-in dual-axis active servo tracking system, its cost is high and the target reflector has to adjust its pose frequently, so it cannot be popularized in the production and application sites of industrial robots. Based on this drawback, a 3D passive laser tracker (3DPLT) with high precision, simple structure, easy operation and low cost is proposed in this paper. Firstly, the overall structure of the system is designed, and its position error model based on the principle of spherical coordinate measurement and vector transfer method is established. Then, the error parameters are identified by experiments to formulate the error compensation model. Finally, the multi-pose and large-range spatial error compensation verification experiments of the system are carried out on a commercial coordinate measuring machine. The results show that the spatial volumetric errors of the 3DPLT can achieve within 40 μm after compensation with a good repeatability of ±4 μm. A comparison contouring test with a commercial ballbar is also carried out to validate its applicability of robot calibration.     

Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?

IntroductionThe main purpose of this cross-sectional study was to investigate whether visual discomfort acts as a mediating factor between perceived visual ergonomic working conditions and self-rated visual performance among office workers who carry out administrative tasks and computer-based work at the Swedish Tax Agency.MethodsA questionnaire was sent to 94 office workers addressing: 1) perceived visual quality of the visual display units; 2) prevalence of eye symptoms; and 3) self-rated visual performance. Eighty-six persons (54 women (63%), 31 men (36%), and 1 of unspecified sex) answered the questionnaire. Multiple regression analysis investigated the association between visual ergonomic working conditions and visual performance, both with and without visual discomfort as a mediator.ResultsThe group mean of the Indexed survey questions indicated a reasonably good quality of visual ergonomic working conditions, a relative absence of eye symptoms, and acceptable self-rated visual performance. Results from multiple regression analysis showed a significant association between perceived visual ergonomic working conditions and self-rated visual performance (r² = 0.30, β = 0.327, p < 0.01). When visual discomfort was used as a mediator, the association between perceived visual ergonomic working conditions and self-rated visual performance remained the same (r² = 0.32, β = 0.315, p < 0.01).DiscussionIt was remarkable to discover that self-rated visual performance was independent of visual discomfort. Possible explanations include exposure factors not included in the current study, such as dry air and sensory irritation in the eyes, psychosocial stress, time spent performing near work activities, or time exposed to visually deficient working conditions.Relevance to industryThe strong connection between satisfaction with visual ergonomic working conditions and productivity in this study has implications for workplace profitability and staff satisfaction. If productivity is enhanced by better visual ergonomic working conditions, then managers of workplaces may be able to improve work outcomes by optimizing the physical work environment.     

Novel thoraco-lumbo-sacral corset design increases Biering-Sorensen back endurance and alters knee and ankle angles during a box lifting task

Research investigating lumbosacral corset designs and their effects are limited and conflicting. The objective was to compare thoraco-lumbo-sacral support corsets (polyester/nylon: TLSSC-poly and neoprene: TLSSC-neo) with a traditional model (TRAD) and Control. Twenty male, university-aged, healthy, recreationally active, participants performed Biering-Sorensen back endurance (BS) test and box lifting tasks (BL:30 repetitions using 20% body mass). Lower and upper erector spinae and hamstrings electromyography (EMG); trunk-hip, knee, and ankle kinematics as well as endurance time were monitored. With BL, the TLSSC-poly (121.4°±17.9) exhibited 1.9% (p = 0.01), 2.7% (p = 0.003), and 3.7% (p = 0.0003) greater knee flexion than TRAD (119.1°±17.5), TLSSC-neo (116.8°±17.4) and Control (120.1°±17.6) respectively. The TLSSC-poly (101.9°± 8.9) demonstrated significant 3.5% (p = 0.005), 2.2% (p = 0.002) and 1.4% (p = 0.01) greater dorsiflexion than TRAD (103.4°±8.7), TLSSC-neo (104.2°±9.8) and Control (105.7°±7.2) respectively. With BS, TLSSC-poly (137.4-s±31.2, 9.7%, p = 0.018) and TLSSC-neo (133.8-s±32.3, 9.2%, p = 0.006) exhibited significantly longer durations than Control (124.8-s±29.8). Relevance to industry: The TLSSC increased BS endurance and TLSSC-poly increased BL knee and ankle angles, possibly providing benefits for workers, with repeated actions over a full work day.     

Member combination selection for product collaborative design under the open innovation model

A temporary product collaborative design team (PCDT) formed by customers and candidate service providers is the main organization form required to complete the task of product collaborative design (PCD) under the open innovation model. Therefore, the aim of this study was to implement synergy effect-based member combination selection (SE-MCS) while ensuring customer participation in the PCD. First, the conceptual framework of SE-MCS method was developed to characterise the SE-MCS process that includes the customer. Second, SE-MCS indicators were determined by analysing the characteristics of PCD under the open innovation model, and the quantitative calculation methods for these indicators were provided. Subsequently, the mathematical model for SE-MCS considering customer participation was established, and a multi-objective optimisation algorithm was adopted to identify the optimal scheme. Finally, the formation of a design team for a beach waste collection vehicle was performed to verify the proposed method. The results showed that the proposed method is more suitable to implement SE-MCS of PCD under the open innovation model. It can facilitate the smooth operation of PCD tasks and improve the quality and efficiency of teamwork, thereby increasing customer satisfaction.     

Fatigue assessment of construction equipment operators using a sweat lactate biosensor

This study investigated the accuracy and usefulness of a sweat-based biosensor for measuring the fatigue levels of construction equipment operators. Specifically, the paper first elaborated on the suitability of selecting sweat lactate as a biomarker for measuring combined fatigue (i.e., a combination of physical and mental fatigue) that construction equipment operators often experience. Then, the mechanism of a sweat-based sensor was explained. An experiment was then presented in which the sweat lactate levels of five subjects engaged in simulated equipment operating tasks were measured. The reliability of the sweat-based lactate sensor was also evaluated by conducting a between-day test-retest experiment. The lactate measurements of the sweat sensor were compared to measurements of blood lactate to assess the accuracy of the sweat sensor. The Fatigue Assessment Scale, a subjective fatigue method, validated sweat lactate as a fatigue assessment biomarker. Analytical results indicate that the lactate measurements from the sweat-based sensor do reflect the fatigue level of equipment operators, and the device has good reliability for measuring sweat lactate concentration.     

Effect of working position and cold environment on muscle activation level and fatigue in the upper limb during manual work tasks

Several occupational groups are exposed to periods of low ambient temperatures while performing manual work tasks outdoors. Work tasks typically include heavy lifting, tool handling, and overhead work. This study evaluated the effect of working position and cold environment on muscle activation level (%RMS_max) and fatigue in the upper limb during manual work tasks. Fourteen male participants (25 ± 3 years, 80.9 ± 6.4 kg, 182 ± 5 cm) completed a 2-h test protocol consisting of five test periods alternating with four work periods, wearing identical sets of clothing, under cold (−15 °C) and control (5 °C) conditions. The work periods consisted of manual work at the hip level, manual overhead work, and a lifting exercise. The test periods consisted of isometric maximal voluntary contractions (MVC) and seated rest. Skin temperatures decreased during cold exposure, especially in the extremities. %RMS_max in the forearm was higher in the cold condition both during overhead work and work at the hip level than that for the same work in the control condition, especially at the end of the test when the difference was approximately 25% (equating to 2–3 %RMS_max). For the middle deltoid muscle, the %RMS_max was approximately three times (or 10 %RMS_max) higher during overhead work than work at the hip level, but there was no additional cost of working in the cold. Signs of deltoid muscle fatigue (decrease in electromyography median power frequency and an increase in %RMS_max) were observed during the overhead work periods in both temperature conditions. No decrease in MVC, as a sign of overall muscle fatigue, was observed in either condition.Relevance to industryThis study demonstrated that when wearing suitable cold-weather protective clothing, the adverse effect of work posture is much higher than that of cold on muscle demand and physical strain.     

Engineering knowledge formalization and proposition for informatics development towards a CAD-integrated DfX system for product design

Target design methodologies (DfX) were developed to cope with specific engineering design issues such as cost-effectiveness, manufacturability, assemblability, maintainability, among others. However, DfX methodologies are undergoing the lack of real integration with 3D CAD systems. Their principles are currently applied downstream of the 3D modelling by following the well-known rules available from the literature and engineers’ know-how (tacit internal knowledge).This paper provides a method to formalize complex DfX engineering knowledge into explicit knowledge that can be reused for Advanced Engineering Informatics to aid designers and engineers in developing mechanical products. This research work wants to define a general method (ontology) able to couple DfX design guidelines (engineering knowledge) with geometrical product features of a product 3D model (engineering parametric data). A common layer for all DfX methods (horizontal) and dedicated layers for each DfX method (vertical) allow creating the suitable ontology for the systematic collection of the DfX rules considering each target. Moreover, the proposed framework is the first step for developing (future work) a software tool to assist engineers and designers during product development (3D CAD modelling).A design for assembly (DfA) case study shows how to collect assembly rules in the given framework. It demonstrates the applicability of the CAD-integrated DfX system in the mechanical design of a jig-crane. Several benefits are recognized: (i) systematic collection of DfA rules for informatics development, (ii) identification of assembly issues in the product development process, and (iii) reduction of effort and time during the design review.     

A digital twin-based multidisciplinary collaborative design approach for complex engineering product development

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.     

A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution

Conceptual design evaluation plays a crucial role in new product development (NPD) and determines the quality of downstream design activities. Currently, most existing methods focus on fuzzy quantitative the evaluation information of multi-objectives in conceptual schemes selection. However, the above process ignores the various customers' preferences for each scheme under the evaluation objective, causing inconsistent preference weights in the various schemes, which cannot guarantee the market value of the optimal scheme. Furthermore, the ambiguous attitude from experts in the early design stage is not well taken into account. To this end, a conceptual scheme decision model with considering diverse customer preference distribution based on interval-valued intuitionistic fuzzy set (IVIFS) is proposed. The model is divided into three parts. Firstly, the initial decision matrix of multi-experts concerning the qualitative and quantitative design attributes is constructed based on intuitionistic fuzzy sets, and then the IFS decision matrix with interval boundaries is formed by using rough set technology. Secondly, the mapping model of design attribute to customer preference is constructed, and then the demand preference strategy implied by design attribute is judged. Thirdly, based on the demand preference strategy, the preferences’ weights for each scheme are calculated. Next, integrating the evaluation data with the same preference in the scheme, the comprehensive satisfaction of the scheme is obtained through IVIFS weighted aggregation operator, and then the optimal scheme is decided. Eventually, a case study of mobile phone form feature schemes is further employed to verify the proposed decision model, and results are sensitivity analyzed and compared.     

Ergonomics demands associated with combinations of manual and powered emergency medical service cots and ambulance loading systems: A work simulation study

Digital human models were used to perform a virtual ergonomics assessment of manual and powered emergency medical services cots combined with both manual and powered ambulance loading systems. Simulations were run with all combinations of emergency medical technicians (EMTs), at 50 kg (female), 72 kg (female) and 125 kg (male) with cots containing no patient and patients of 125 kg and 159 kg. There was a substantial decrease in low back and upper extremity demands with the use of a powered cot, and a further decrease with the additional use of a powered loading system, even though it only required one EMT. The benefits of a fully powered system were magnified with the simulation of both heavier EMTs and patients. Additionally, this study demonstrates the utility of digital human models and work simulation to evaluate product designs that impact occupational demands and injury risk.     

A cooperative game theory based user-centered medical device design decision approach under uncertainty

The medical device conceptual design decision-making is a process of coordinating pertinent stakeholders, which will significantly affect the quality of follow-up market competitiveness. However, as the most challenging parts of user-centered design, traditional methods are mainly focusing on determining the priorities of the evaluation criteria and forming the comprehensive value (utility) of the conceptual scheme, may not fully deal with the interaction and interdependent between the conflicts of interest among stakeholders and weigh the ambiguous influence on the overall design expectations, which results in the unstable decision-making results. To overcome this drawback, this paper proposes a cooperative game theory based decision model for device conceptual scheme under uncertainty. The proposed approach consists of three parts: first part is to collect and classify needs of end users and professional users based on predefined evaluation criteria; second part is using rough set theory technique to create criteria correlation diagram and scheme value matrix from users; and third part is developing the fuzzy coalition utility model to maximize the overall desirability through the criteria correlation diagram with the conflict of interests of end and professional users considered, and then selecting the optimal scheme. A case study of blood pressure meter is used to illustrate the proposed approach and the result shows that this approach is more robust compared with the widely used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach.     

Reliability of a new risk assessment method for visual ergonomics

IntroductionThe Visual Ergonomics Risk Assessment Method (VERAM) is a newly developed and validated method to assess visual ergonomics at workplaces. VERAM consists of a questionnaire and an objective evaluation.ObjectiveTo evaluate reliability of VERAM by assessing test-retest reliability of the questionnaire, and intra- and inter-rater reliability of the objective evaluation.MethodsForty-eight trained evaluators used VERAM to evaluate visual ergonomics at 174 workstations. The time interval for test-retest and intra-rater evaluations was 2–3 weeks, and the time interval for inter-rater evaluations was 0–2 days. Test-retest reliability was assessed by intraclass correlation (ICC), the standard error of measurement (SEM) and the smallest detectable change (SDC). Intra- and inter-rater reliability were assessed with weighted kappa coefficients and absolute agreement. Systematic changes were analysed with repeated measures analyses of variance and Wilcoxon sign rank test.ResultsThe ICC of the questionnaire indices ranged from 0.69 to 0.87, while SEM ranged from 7.21 to 10.19 on a scale from 1 to 100, and SDC from 14.42 to 20.37. Intra-rater reliability of objective evaluations ranged from 0.57 to 0.85 (kappa coefficients) and the agreement from 69 to 91%. Inter-rater reliability of objective evaluations ranged from 0.37 to 0.72 (kappa coefficients) and the agreement from 52 to 87%.ConclusionVERAM is a reliable instrument for assessing risks in visual work environments. However, the reliability might increase further by improving the quality of training for evaluators. Complementary evaluations of VERAM's sensitivity to changes in the visual environment are needed.Relevance to industryIt is advantageous to set up a work environment for maximal visual comfort to avoid negative effects on work postures and movements and thus prevent visual- and musculoskeletal symptoms. This method, VERAM, satisfies the need of a valid and reliable tool for determining risks associated with the visual work environment.     

A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs

The mechanical product design process involves much experiential reasoning which relies extensively on accumulated experience knowledge and ambiguous synthetic decision of experts (ASDE). This makes it hard to achieve the automated, intelligent and rapid design of mechanical products. Furthermore, due to the lack of consideration of experts' cognition of product functions and structures in the application of the current case-based reasoning (CBR) method in the field of automated experiential reasoning (AER), the parameter solving process is separated from ASDE. Aiming at improving the accuracy and intelligence level of AER in mechanical product design, this paper proposed a parameter-extended CBR (PECBR) method based on a functional basis by integrating ASDE into AER. The PECBR method mainly contains two parts: firstly, in order to acquire and quantitatively describe expert experiential knowledge to provide an effective basis for AER, a knowledge representation method integrating a function-flow-parameter matrix set (FFP-MS) using functional bases and a parameter experiential correlation matrix (PEC-M) extracted from FFP-MS were presented for mechanical products, where the FFP-MS characterized the operation of function and energy flow during the working process of products. An acquisition rule for FFP-MS was designed to extract the degree of correlation between each two parameters, in which the implicit knowledge hiding among functions, flows and parameters was mined to form PEC-M; secondly, to cope with the difficulty in integrating ASDE into AER, a feature-weighted case adaptation (FCA) method was proposed by adopting a presented weighted kernel support vector machine (WK-SVM) and dynamic particle swarm optimization (DPSO). The FCA method can achieve the intelligent and automated solving of product parameters through identifying PEC-M during the case adaptation process. Two case studies on two-stage reducers and corn huskers were carried out to demonstrate the validity of the PECBR method. Compared with other conventional CBR methods, PECBR method can derive a more accurate value of parameters in mechanical product designs especially in the case of limited similar cases.     

A review of digital twin in product design and development

In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies.     

Human–machine hybrid intelligence for the generation of car frontal forms

With the acceleration of the upgrading of the automobile consumption market, artificial intelligence has become an increasingly effective means of enhancing the creative design of automobile appearance modeling. However, when artificial intelligence processes specific design tasks, creativity is primarily based on data drive, resulting in machine-generated design schemes that do not match human-specific psychological intentions. Due to the absence of design knowledge in the process of machine design, there is a data gap between human cognitive thought and machine information processing. This paper aims to structure the human's complex cognitive knowledge of car frontal form, establish the consistency between human and machine cognitive structures, and reduce communication barriers in the process of human–machine hybrid creative design. To achieve this objective, a human–machine hybrid intelligence methodology – a combination of human cognitive mental model, human–machine shared knowledge base, and Generative Adversarial Networks (GAN) – was developed to generate a large number of car frontal forms that are consistent with the design intent. First, we constructed a mental model of human cognition based on three dimensions: design intent, drawing behavior, and functional structure. Second, we created a shared human–machine knowledge base with design Knowledge. This knowledge base contains 12,560 images of car frontal form designs with corresponding morphological semantic labels and 3,140 sketches of car frontal forms drawn by hand. Human–machine shared knowledge base data was utilized in a machine learning training network. In addition, a conditional cross-domain generative adversarial network was developed to investigate the implicit relationship between sketch characteristics, morphological semantics, and image visual effects. Using the suggested method, a large number of images with the specified morphological semantic category and resembling the hand-drawn sketch of a car frontal form can be generated rapidly. In terms of the quality of car frontal form generation, our research is superior to the baseline model according to qualitative and quantitative assessments. In comparison to the designer's output, the human–machine hybrid intelligent generation also demonstrates excellent creative performance.     

EarlyData knowledge base for material decisions in building design

To make use of the great opportunities for emission reduction in early building design, future emissions need to be calculated when only geometric, but no detailed material information about a building is available. Currently, early design phase life cycle assessments (LCAs) are heavily reliant on assumptions of specific material choices, leading to single point emission values which suggest a precision not representative for an early design stage. By adding knowledge about possible locations and functions of materials within a building to life cycle inventory (LCI) data, the EarlyData knowledge base makes LCA data sets accessible and more transparent. Additionally, “generic building parts” are defined, which describe building parts independently of precise material choices as a combination of layers with specific functions. During evaluation, enriched LCI data and generic building parts enable assessment of a vast number of possible material combinations at once. Thus, instead of single value results for a particular material combination, ranges of results are displayed revealing the building parts with the greatest emission reduction potential. The application of the EarlyData tool is illustrated on a use case comparing a wood building and a concrete building. The database is developed with extensibility in mind, to include other criteria, such as (life cycle) costs.     

Are knee savers and knee pads a viable intervention to reduce lower extremity musculoskeletal disorder risk in residential roofers?

One factor commonly associated with musculoskeletal disorder risk is extreme postures. To lessen this risk, extreme postures should be reduced using proactive and prevention-focused methods. The effect of combinations of two interventions, knee pads and knee savers, on lower extremity kinematics during deep or near full flexion kneeling on differently sloped surfaces was analyzed. Nine male subjects were requested to keep a typical resting posture while kneeling on a sloped roofing simulator with and without knee pads and knee savers. Three-dimensional peak knee kinematics were recording using a motion capture system. The kinematic data were analyzed with a two-way—4(intervention) X 3(slope)—repeated measure analysis of variance (ANOVA). It was observed that knee pads did not alter lower extremity kinematics in a way that may reduce musculoskeletal injury risk, but they do provide comfort. Knee savers did statistically significantly reduce peak lower extremity kinematics, however these changes were small and it is uncertain if the changes will reduce musculoskeletal injury risk. This study has provided initial data that supports the use of knee savers as a potential intervention to reduce musculoskeletal disorder risk due to lower extremity joint angles on a sloped surface, nonetheless, further testing involving other musculoskeletal disorder risk factors is needed prior to a conclusive recommendation.