Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics

Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals

Surface electromyography techniques are widely used in field of motion analysis and ergonomics combining precise muscular activation assessment with low-invasiveness and wearability. The aim of this investigation is to identify the myoelectrical manifestations of fatigue and to compare the effectiveness of sEMG-based quantitative indices for fatigue assessment. The investigated indexes are the ARV and RMS signal amplitudes, the mean frequency, the median frequency, the Dimitrov index, the instantaneous mean frequency and Wavelet distribution-based WIRE51 index. Two different protocols were developed, and the activity of the lateral deltoid and middle trapezius muscles was recorded. The WIRE51 index is found to have the highest sensitivity in the detection of the difference between the repetitions of each exercise for both protocols. Due to the lack of a unified standard for the performance comparison of fatigue indices, a correlation analysis was carried out between the result provided by the indices and the subjective fatigue perception employing the RPE scale.

Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review

BACKGROUND

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.

RESEARCH QUESTION

This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.

METHODS

The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.

RESULTS

A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.

SIGNIFICANCE

Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

Optimising computer vision-based ergonomic assessments: sensitivity to camera position and monocular 3D pose model

Numerous computer vision algorithms have been developed to automate posture analysis and enhance the efficiency and accuracy of ergonomic evaluations. However, the most effective algorithm for conducting ergonomic assessments remains uncertain. Therefore, the aim of this study was to identify the optimal camera position and monocular 3D pose model that would facilitate precise and efficient ergonomic evaluations. We evaluated and compared four currently available computer vision algorithms: Mediapipe BlazePose, VideoPose3D, 3D-pose-baseline, and PSTMO to determine the most suitable model for conducting ergonomic assessments. Based on the findings, the side camera position yielded the lowest Mean Absolute Error (MAE) across static, dynamic, and combined tasks. This positioning proved to be the most reliable for ergonomic assessments. Additionally, VP3D_FB demonstrated superior performance among evaluated models.Practitioner Summary: This study aimed to determine the most effective computer vision algorithm and camera position for precise and efficient ergonomic evaluations. Evaluating four algorithms, we found that the side camera position with VideoPose3D yielded the lowest Mean Absolute Error (MAE), ensuring precise and efficient evaluations.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities

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.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Achieving a normal gait trajectory for an amputee's active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Background

Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation.

Objective

This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation.

Methods

We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question.

Results

A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures.

Conclusions

This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.

Usefulness of the Rapid Office Strain Assessment (ROSA) tool in detecting differences before and after an ergonomics intervention

Background

Most ergonomics studies on office workstations evaluate the effects of an intervention only by subjective measures such as musculoskeletal pain and discomfort. Limited evidence has been provided regarding risk factor reduction in office environments through standardized methods assessments. The Rapid Office Strain Assessment (ROSA) tool can provide an estimation of risk factor exposure for office workers as a means by which the outcome of interventions can be quantified.

Purpose

The aim of the study was to evaluate if ROSA scores reflect changes in risk factors after an ergonomics intervention among office workers.

Methods

Office workers (n = 60) were divided into two groups. The experimental group received a workstation intervention and the control group received no intervention. Changes in ROSA scores were compared before and after the intervention in both groups.

Results

Statistically significant reductions in the ROSA final and section scores occurred after the intervention in the experimental group with (mean reduction of 2.9, 0.8 and 1.6 points for sections A, B and C, respectively). In contrast, no differences were detected in the control group (mean increase of 0.1 point for sections A and C and mean reduction of 0.1 point for Section B).

Conclusions

These findings show that ROSA scores reflect changes in risk factors after an ergonomics intervention in an office environment. Consequently, this tool can be used for identifying and controlling risk factors among computer workers, before and after interventions.

Ergonomics Risk Assessment for Manual Material Handling of Warehouse Activities Involving High Shelf and Low Shelf Binning Processes: Application of Marker-Based Motion Capture

Lower back pain is a musculoskeletal disorder that is commonly reported among warehouse workers due to the nature of the work environment and manual handling activities. The objective of this study was to assess the ergonomic risks among warehouse workers carrying out high shelf (HS) and low shelf (LS) binning processes. A questionnaire was used to determine the prevalence of musculoskeletal symptoms, while a marker-based motion capture (MoCap) system worksheet was used to record the participants’ motion and determine the action risk level. A total of 33% of the participants reported lower back pain in the past seven days, based on the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) results. Analysis of the body velocities showed that the HS binning process had four major velocity peaks, defined as the initial, lowering, lifting, and final phases. In comparison, the LS binning process had two major peaks defined, the crouching and rising phases. There were significant differences between the mean velocities of the workers for the HS binning process, indicating that the workers have different movement patterns with varying velocities.

Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method

OBJECTIVE

A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting.

BACKGROUND

Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments.

METHODS

Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames.

RESULTS

The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles (R2 = .80, p < .001). The training error for the kinematics model was 2.3°.

CONCLUSION

Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision.

APPLICATION

This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

Load Asymmetry Angle Estimation Using Multiple view Videos

A robust computer vision-based approach is developed to estimate the load asymmetry angle defined in the revised NIOSH lifting equation (RNLE). The angle of asymmetry enables the computation of a recommended weight limit for repetitive lifting operations in a workplace to prevent lower back injuries. An open-source package OpenPose is applied to estimate the 2D locations of skeletal joints of the worker from two synchronous videos. Combining these joint location estimates, a computer vision correspondence and depth estimation method is developed to estimate the 3D coordinates of skeletal joints during lifting. The angle of asymmetry is then deduced from a subset of these 3D positions. Error analysis reveals unreliable angle estimates due to occlusions of upper limbs. A robust angle estimation method that mitigates this challenge is developed. We propose a method to flag unreliable angle estimates based on the average confidence level of 2D joint estimates provided by OpenPose. An optimal threshold is derived that balances the percentage variance reduction of the estimation error and the percentage of angle estimates flagged. Tested with 360 lifting instances in a NIOSH-provided dataset, the standard deviation of angle estimation error is reduced from 10.13° to 4.99°. To realize this error variance reduction, 34% of estimated angles are flagged and require further validation.

Accuracy of a markerless motion capture system for postural ergonomic risk assessment in occupational practice

Established methods for postural ergonomic risk assessment in occupational practice are mostly time-consuming and need to be conducted by experts. Use of technology could improve postural ergonomic risk assessments with regard to time efficiency and accuracy. A study was conducted to assess the accuracy of a markerless motion capture system (Microsoft Kinect V2) compared to a marker-based motion capture system (Vicon Bonita). Angles of different body segments were analysed. The results show major inaccuracies of the markerless motion capture system for capturing axial trunk rotation (mean angular deviation of 14.04°) indicating that potential health risks could be underestimated. Combined working postures of axial trunk rotation and arm anteversion show issues with self-occlusion. Based on the findings, it is discussed whether the detected inaccuracies for axial trunk rotation are likely to lead to overestimation or underestimation of potential health risks when conducting an ergonomic risk assessment.

Implementation of Kinetic and Kinematic Variables in Ergonomic Risk Assessment Using Motion Capture Simulation: A Review

Work-related musculoskeletal disorders (WMSDs) are among the most common disorders in any work sector and industry. Ergonomic risk assessment can reduce the risk of WMSDs. Motion capture that can provide accurate and real-time quantitative data has been widely used as a tool for ergonomic risk assessment. However, most ergonomic risk assessments that use motion capture still depend on the traditional ergonomic risk assessment method, focusing on qualitative data. Therefore, this article aims to provide a view on the ergonomic risk assessment and apply current motion capture technology to understand classical mechanics of physics that include velocity, acceleration, force, and momentum in ergonomic risk assessment. This review suggests that using motion capture technologies with kinetic and kinematic variables, such as velocity, acceleration, and force, can help avoid inconsistency and develop more reliable results in ergonomic risk assessment. Most studies related to the physical measurement conducted with motion capture prefer to use non-optical motion capture because it is a low-cost system and simple experimental setup. However, the present review reveals that optical motion capture can provide more accurate data.

Comparisons of physical exposure between workers harvesting apples on mobile orchard platforms and ladders, part 2: Repetitive upper arm motions

Farmworkers are exposed to physical risk factors including repetitive motions. Existing ergonomic assessment methods are primarily laboratory-based and, thus, inappropriate for use in the field. This study presents an approach to characterize the repetitive motions of the upper arms based on direct measurement using accelerometers. Repetition rates were derived from upper arm inclination data and with video recordings in the field. This method was used to investigate whether harvesting with mobile platforms (teams harvesting apples from the platform and the ground) increased the farmworkers' exposure to upper arm repetitive motions compared to traditional harvesting using ladders. The ladder workers had higher repetitive motions (13.7 cycles per minute) compared to the platform and ground workers (11.7 and 12.2 cycles per minutes). The higher repetitions in the ladder workers were likely due to their ability to work independently and the additional arm movements associated with ladder climbing and walking.

A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.

AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

Effect of a training program of overground walking on BTS gait parameters in elderly women during single and dual cognitive tasks

We tested the hypothesis that a regular training program might reduce gait disturbances during dual cognitive-motor tasks in elderly women. This open-label experimental study comprised 53 postmenopausal women aged over 65, who were assigned to a 10-week training program (360 min/week). A BTS SMART system examination during free walking and during dual tasks [i.e., walking while performing either a simple (SCT) or a complex (CCT) cognitive task] was performed prior to the training program and again after it had finished. After the 10-week walking training program, a significant decrease was found in the duration of single support phase, double support phase, total support phase, and gait cycle, whereas values for such BTS parameters as swing speed, step length, and gait speed increased significantly. The greatest percentage deltas between the final and initial values of the respective BTS parameters concerned swing speed and gait speed irrespective of the kind of task undertaken while measurements were taken. A cognitive task, irrespective of the level of difficulty, performed during walking had the opposite effect on step width than expected. A 10-week training program significantly improved the cadency and manner of gait in elderly women, but did not change step width. Therefore, further study is needed to estimate the usefulness of cognitive-motor training programs for significant improvement in gait coordination during dual tasks in elderly women.

Prediction model of the effect of postural interactions on muscular activity and perceived exertion

Musculoskeletal disorders are a prevalent disease in many Western countries. While a large number of ergonomic analyses and assessment methods are nowadays available, most current methods that assess exposure calculate overall risk scores of individual body segments without considering interaction effects of exposure variables. Therefore, a study was conducted that aimed at investigating and quantifying interaction effects of trunk inclination and arm lifting on ratings of perceived exertion (RPE) and muscle activity. A multiple regression model to predict musculoskeletal load under consideration of interaction effects was derived. The study revealed that there is a significant interaction effect of trunk inclination and arm lifting. Furthermore, final regression models explained variance in exposure variables in a range of R² = 0.68 to R² = 0.147 with a subset of two to three inputs. The predicative equations support the computer-based post-processing of sensor data. Practitioner summary: This article elaborates on the importance of interaction effects of working postures on assessment results of load. In practise, easy to-use-methods for an assessment of working postures are needed. Therefore, a regression model is derived, which facilitates the quantification of work load under consideration of interaction effects. The use of this regression model for the assessment of posture data gathered by range sensors is recommended. Abbreviations: RPE: rating of perceived exertion; MSD: musculoskeletal disorder; OWAS: ovako working posture analysing system; RULA: rapid upper limb assessment; LUBA: postural loading on the upper body assessment; REBA: rapid entire body assessment; OCRA: occupational repetitive action;S D: standard deviation; EMG: surface electromyography; LUT: left upper trapezius pars descendens; RUT: right upper trapezius pars descendens; LLT: left trapezius pars ascendens; RLT: right trapezius pars ascendens; LAD: left anterior deltoideus; RAD: right anterior deltoideus; LES: left erector spinae longissimus; RES: right erector spinae longissimus; SENIAM: surface electroMyoGraphy for the non-invasive assessment of muscles; MVC: maximum voluntary contraction; MANOVA: multivariate analysis of variance; ANOVA: analysis of variance; OLS: ordinary least squares; MANCOVA: multivariate analysis of covariance.

The accuracy of a 2D video-based lifting monitor

A widely used risk prediction tool, the revised NIOSH lifting equation (RNLE), provides the recommended weight limit (RWL), but is limited by analyst subjectivity, experience, and resources. This paper describes a robust, non-intrusive, straightforward approach to automatically extract spatial and temporal factors necessary for the RNLE using a single video camera in the sagittal plane. The participant's silhouette is segmented by motion information and the novel use of a ghosting effect provides accurate detection of lifting instances, and hand and feet location prediction. Laboratory tests using 6 participants, each performing 36 lifts, showed that a nominal 640 pixel × 480 pixel 2D video, in comparison to 3D motion capture, provided RWL estimations within 0.2 kg (SD = 1.0 kg). The linear regression between the video and 3D tracking RWL was R² = 0.96 (slope = 1.0, intercept = 0.2 kg). Since low definition video was used in order to synchronise with motion capture, better performance is anticipated using high definition video. Practitioner's summary: An algorithm for automatically calculating the revised NIOSH lifting equation using a single video camera was evaluated in comparison to laboratory 3D motion capture. The results indicate that this method has suitable accuracy for practical use and may be, particularly, useful when multiple lifts are evaluated. Abbreviations: 2D: Two-dimensional; 3D: Three-dimensional; ACGIH: American Conference of Governmental Industrial Hygienists; AM: asymmetric multiplier; BOL: beginning of lift; CM: coupling multiplier; DM: distance multiplier; EOL: end of lift; FIRWL: frequency independent recommended weight limit; FM: frequency multiplier; H: horizontal distance; HM: horizontal multiplier; IMU: inertial measurement unit; ISO: International Organization for Standardization; LC: load constant; NIOSH: National Institute for Occupational Safety and Health; RGB: red, green, blue; RGB-D: red, green, blue - depth; RNLE: revised NIOSH lifting equation; RWL: recommended weight limit; SD: standard deviation; TLV: threshold limit value; VM: vertical multiplier; V: vertical distance.

Investigation of observational methods assessing workload of static working postures based on surface electromyography

BACKGROUND:

A large number of different methods are available to identify and assess working postures. Although observation-based methods are most commonly used in practise, investigations showed different results regarding validity of such methods.

OBJECTIVE:

To investigate validity of one of the most commonly used observation-based assessment method in ergonomics, the Ovako Working Posture Analysing System (OWAS) and the European standard EN 1005-4 for evaluation of working postures, an experimental laboratory study was conducted.

METHODS:

Muscle activity was measured under combinations of static working postures of trunk inclination and shoulder flexion to compare these measurements and observation-based assessments according to OWAS and EN 1005-4. In order to investigate the magnitude of correspondence between muscle activity and observation-based assessments, Spearman rank correlation coefficients (r_s) were calculated.

RESULTS:

Significant correlations were found between OWAS and muscle activity (range from r_s² = 0.17 r_s² = 0.55). Significant correlations were found between EN 1005-4 and muscle activity (range from r_s² = 0.34 to r_s² = 0.74).

CONCLUSIONS:

Results emphasise a need for further developments of observation-based methods, since the two investigated methods showed a variance of validity ranging from small to large. Such improvements may also form a better basis for the ergonomic improvement of working conditions in practise, which is highly necessary due to a constantly high prevalence of MSDs in the last decades.

Dual Kinect v2 system can capture lower limb kinematics reasonably well in a clinical setting: concurrent validity of a dual camera markerless motion capture system in professional football players

Objectives

To determine whether a dual-camera markerless motion capture system can be used for lower limb kinematic evaluation in athletes in a preseason screening setting.

Design

Descriptive laboratory study.

Setting

Laboratory setting.

Participants

Thirty-four (n=34) healthy athletes.

Main outcome measures

Three dimensional lower limb kinematics during three functional tests: Single Leg Squat (SLS), Single Leg Jump, Modified Counter-movement Jump. The tests were simultaneously recorded using both a marker-based motion capture system and two Kinect v2 cameras using iPi Mocap Studio software.

Results

Excellent agreement between systems for the flexion/extension range of motion of the shin during all tests and for the thigh abduction/adduction during SLS were seen. For peak angles, results showed excellent agreement for knee flexion. Poor correlation was seen for the rotation movements.

Conclusions

This study supports the use of dual Kinect v2 configuration with the iPi software as a valid tool for assessment of sagittal and frontal plane hip and knee kinematic parameters but not axial rotation in athletes.

Validation of an ergonomic assessment method using Kinect data in real workplace conditions

Evaluating potential musculoskeletal disorders risks in real workstations is challenging as the environment is cluttered, which makes it difficult to accurately assess workers' postures. Being marker-free and calibration-free, Microsoft Kinect is a promising device although it may be sensitive to occlusions. We propose and evaluate a RULA ergonomic assessment in real work conditions using recently published occlusion-resistant Kinect skeleton data correction. First, we compared postures estimated with this method to ground-truth data, in standardized laboratory conditions. Second, we compared RULA scores to those provided by two professional experts, in a non-laboratory cluttered workplace condition. The results show that the corrected Kinect data can provide more accurate RULA grand scores, even under sub-optimal conditions induced by the workplace environment. This study opens new perspectives in musculoskeletal risk assessment as it provides the ergonomists with 30 Hz continuous information that could be analyzed offline and in a real-time framework.