The relationship between ratings of perceived exertion (RPE) and relative strength for a fatiguing dynamic upper extremity task: A consideration of multiple cycles and conditions.

The goal of this study was to evaluate the relationship between ratings of perceived exertion (RPE) and relative strength with respect to baseline for a fatiguing free dynamic task targeting the upper extremity, namely simulated order picking, and determine whether the relationship remains the same for different conditions (i.e., pace and weight) and with fatigue. Fourteen participants (seven males, seven females) performed four sessions that included two 45-min work periods separated by 15 min of rest. The work periods involved picking weighted bottles from shoulder height and packaging them at waist height for four combinations of bottle mass and picking rate: 2.5 kg-15 bottles per minute (bpm), 2.5 kg-10 bpm, 2.5 kg-5 bpm, and 1.5 kg-15 bpm. Participants reported their RPEs every 5 min and performed a maximum isometric shoulder flexion exertion every 9 min. Pearson product-moment correlation was used to evaluate the linear relationship between RPE and relative strength for each subject and work period. Then, the effects of condition and work period on the average relationship were assessed using a repeated-measures analysis of variance (ANOVA). For the first 45-min period, there were no significantly different correlations between RPE and relative strength across conditions (average r = -0.62 (standard deviation = 0.38); p = 0.57). There was a significant decrease in average correlation for the second work period (r = -0.39 (0.53)). These results suggest that individual subjective responses consistently increase while relative strength declines when starting from a non-fatigued state. However, correlations are weaker when re-engaging in work following incomplete recovery. Thus, starting fatigue levels should be accounted for when considering the expected relationship between RPE and relative strength.

Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking.

OCCUPATIONAL APPLICATIONSMusculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue. In most cases, the fatigue indicator is a functional variable, meaning that the fatigue score or measurement is a curve captured over time, which could be modeled as a function. In this study, we demonstrate that functional data analysis tools, such as functional analysis of variance (FANOVA), prove more effective than traditional methods in specifying how task factors contribute to the development of fatigue in the upper extremities. Furthermore, since there are inherent differences among workers that could affect their fatigue development process, the data heterogeneity could be tackled by employing clustering methods.

Background: Preventing musculoskeletal disorders is a paramount safety concern for industries, with order pickers in warehouses being particularly vulnerable due to their repetitive and dynamic tasks. Understanding the factors contributing to upper-extremity fatigue in such settings is crucial. Purpose: This paper investigates the impact of task-related factors on two upper-extremity fatigue indicators: ratings of perceived fatigue and relative muscle strength. Several statistical approaches were used and compared in terms of their capability in eliciting these effects. Methods: Simulated over-shoulder, order-picking lab experiments were conducted under different combinations of two bottle loads and three picking paces. Fourteen participants, evenly distributed between genders, completed the experiment. A FANOVA was executed as the principal analytical approach, considering the functional nature of the two fatigue indicators measured over the work period. To underscore the benefits of considering the whole functional curve instead of discrete variables, we also conducted repeated-measures and two-way ANOVA as benchmark analyses. Results: FANOVA outcomes affirmed that both task factors (load and pace) significantly influenced both fatigue indicators. The FANOVA method identified larger effect sizes (0.11< ηp2 < 0.19) for both task factors compared to the conventional methods (0< ηp2 < 0.11), supporting the efficacy of FANOVA in identifying the importance of these factors. Conclusions: The FANOVA approach proved effective in detecting the impact of task factors on fatigue indicators, yielding superior results compared to conventional benchmark methods. To address participant heterogeneity, functional clustering and gender-based clustering were introduced into the FANOVA framework, both effectively mitigating this challenge. Notably, FANOVA with functional clusters had superior performance compared to the one with gender clustering, suggesting functional clustering as a more suitable method in overcoming participant heterogeneity.

Adjustments in Shoulder and Back Kinematics during Repetitive Palletizing Tasks.

Repetitive task performance is a leading cause of musculoskeletal injuries among order-picking workers in warehouses. The repetition of lifting tasks increases the risk of back and shoulder injuries among these workers. While lifting in this industry is composed of loaded and unloaded picking and placing, the existing literature does not address the separate analysis of the biomechanics of the back and shoulder for these events. To that end, we investigated the kinematics of the back and shoulder movements of nine healthy male participants who performed three sessions of a simulated de/palletization task. Their back and shoulder kinematics were sensed using an optical motion capture system to determine the back inclination and shoulder flexion. Comparison of the kinematics between the first and last sessions indicated statistically significant changes in the timings, angles, coordination between the back and shoulder, and moments around the shoulder (p<0.05). The majority of the significant changes were observed during the loaded events, which confirms the importance of the separation of these events for biomechanical analysis. This finding suggests that focusing worker evaluation on the loaded periods can provide important information to detect kinematic changes that may affect musculoskeletal injury risk.

A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor.

Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning.

Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.

Interventions to Mitigate Fatigue Induced by Physical Work: A Systematic Review of Research Quality and Levels of Evidence for Intervention Efficacy.

OBJECTIVE: We present a literature review on workplace physical fatigue interventions, focusing on evaluating the methodological quality and strength of evidence. BACKGROUND: Physical fatigue is a recognized workplace problem, with negative effects on performance and health-related complaints. Although many studies have focused on the mechanisms and consequences of fatigue, few have considered the effectiveness of interventions to mitigate fatigue. METHOD: A systematic review of the workplace safety literature for controlled trials of physical fatigue interventions was conducted. Data on intervention type, subject characteristics, targeted tasks and body locations, outcome measures, and study design were extracted. The methodological quality for each study was evaluated using the PEDro scale, and the level of evidence was based on quality, amount, and consistency. RESULTS: Forty-five controlled trials were reviewed, examining 18 interventions. We categorized those interventions into individual-focused (N = 28 studies, nine interventions), workplace-focused (N = 12 studies, five interventions), and multiple interventions (N = 5 studies, four interventions). We identified moderate evidence for interventions related to assistive devices and task variation. There was moderate evidence supporting no fatigue attenuation for the garment change category of interventions. The interventions in the remaining categories had limited to minimal evidence of efficacy. The heterogeneity of the included trials precludes the determination of effect size. CONCLUSION: This review showed a lack of high levels of evidence for the effectiveness of most physical fatigue interventions. APPLICATION: Due to a lack of high levels of evidence for any category of reviewed physical fatigue interventions, further high-quality studies are needed to establish the efficacy of others.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors.

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker's condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning.

OBJECTIVES: To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi-automated fashion for tumour-to-cortex peak early-phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging. METHODS: The CNN was trained and validated to identify the kidney + tumour areas in images from 192 patients. The tumour type was differentiated through automated measurement of PEER after manual segmentation of tumours. The performance of this diagnostic model was compared with that of manual expert identification and tumour pathology with regard to accuracy, sensitivity and specificity, along with the root-mean-square error (RMSE), for the remaining 20 patients with CD117(+) oncocytoma or ChRCC. RESULTS: The mean ± sd Dice similarity score for segmentation was 0.66 ± 0.14 for the CNN model to identify the kidney + tumour areas. PEER evaluation achieved accuracy of 95% in tumour type classification (100% sensitivity and 89% specificity) compared with the final pathology results (RMSE of 0.15 for PEER ratio). CONCLUSIONS: We have shown that deep learning could help to produce reliable discrimination of CD117(+) benign oncocytoma and malignant ChRCC through PEER measurements obtained by computer vision.

Reliability analyses and values of isometric shoulder flexion and trunk extension strengths stratified by body mass index.

The main goal of this study was to investigate the reliability of muscle strength across different levels of obesity. A sample of 142 healthy subjects performed maximum voluntary isometric contractions for shoulder flexion and trunk extension on each of four days. Subjects were recruited into one of three groups, non-obese, overweight, or obese, based on body mass index (BMI). Reliability of the strength measurements within each session and across the four sessions was determined from the intraclass correlation coefficient, coefficient of repeatability, coefficient of variation, and standard error of measurement. For the shoulder flexion measures, the coefficient of variation was < 10% and intraclass correlation coefficient was > 0.75. The absolute reliability of trunk extension strength measurement was rejected due to a high variability across sessions. For both tasks, comparable strengths across the BMI groups were found.

A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos.

PURPOSE: To develop and validate an automated assessment of surgical performance (AASP) system for objective and computerized assessment of pelvic lymph node dissection (PLND) as an integral part of robot-assisted radical cystectomy (RARC) using console-feed videos recorded during live surgery. METHODS: Video recordings of 20 PLNDs were included. The quality of lymph node clearance was assessed based on the features derived from the computer vision process which include: the number and cleared area of the vessels/nerve (N-Vs); image median color map; and mean entropy (measures the level of disorganization) in the video frame. The automated scores were compared to the validated pelvic lymphadenectomy appropriateness and completion evaluation (PLACE) scoring rated by a panel of expert surgeons. Logistic regression analysis was employed to compare automated scores versus PLACE scores. RESULTS: Fourteen procedures were used to develop the AASP algorithm. A logistic regression model was trained and validated using the aforementioned features with 30% holdout cross-validation. The model was tested on the remaining six procedures, and the accuracy of predicting the expert-based PLACE scores was 83.3%. CONCLUSIONS: To our knowledge, this is the first automated surgical skill assessment tool that provides an objective evaluation of surgical performance with high accuracy compared to expert surgeons' assessment that can be extended to any endoscopic or robotic video-enabled surgical procedure.

Use of various obesity measurement and classification methods in occupational safety and health research: a systematic review of the literature.

BACKGROUND: This study systematically examined obesity research in occupational safety and health regarding the use of various obesity measurement and classification methods. METHODS: A systematic search of the PubMed database on English language publications from 2000 to 2015 using related keywords and search of citations resulted in selection of 126 studies. They were categorized into two groups based on their main research question: 1) general physical or mental work-related functioning; and 2) task or body part specific functioning. RESULTS: Regardless of the study group, body mass index (BMI) was the most frequently used measure. Over 63% of the studies relied solely on BMI to define obesity. In only 22% of the studies, body fat was directly measured by methods such as dual energy x-ray absorptiometry. Abdominal obesity was defined using waist circumference in recent years, and waist-hip ratio in earlier years. Inconsistent cut-offs have also been used across studies investigating similar topics. CONCLUSIONS: Few authors acknowledged the limitations of using indirect obesity measures. This is in part due to the limited understanding of some occupational safety and health researchers regarding the complex issues surrounding obesity classification and also the mixed recommendations over the past 2-3 decades and across populations. Efforts need to be made to promote appropriate obesity measurement and reporting in this field.

Task and sex differences in muscle oxygenation during handgrip fatigue development.

The purpose of this study was to examine task and sex differences in forearm muscle oxygenation, measured using near infrared spectroscopy, during sustained submaximal handgrip exercises. Forty-eight adults (50% males) performed fatiguing handgrip exercises at 20, 40, 60 and 80% of their maximum handgrip strength. While males and females exhibited similar levels of relative fatigability, forearm oxygenation was found to be task (i.e. contraction intensity and phase of fatigue development) and sex dependent. Higher contraction intensities were associated with greater desaturation over time. Compared to females, males exhibited greater desaturation as fatigue progressed and this was augmented at higher contraction intensities. These may be likely affected by sex differences in muscle mass, morphology and strength differences during exercises at relative intensities. Future work that explores sex differences in muscle oxygenation during absolute force intensities are needed, which may have implications for muscle fatigue development and potential fatigue mitigation strategies. Practitioner Summary: Muscle oxygenation impacts fatigue development that can in turn affect worker health and productivity. Males exhibit greater forearm desaturation than females at higher relative work intensities, despite similar fatigue levels. Females may be predisposed to greater muscle delivery and oxygenation challenges that can increase their fatigability during work at absolute load levels.

Interactive effects of work psychosocial factors on participation in workplace wellness programs.

This study explored concurrent effects of six work psychosocial factors on current participation and the self-reported likelihood of future participation in workplace wellness programs using a cross-sectional survey, an ad hoc focus group, and structured interviews. Classification and regression tree analysis was used to analyze survey responses from 343 employees (194 nonparticipants, 95 participants, and 54 engaged participants). A thematic analysis of focus group (n = 7) and interview (n = 5) narratives was also undertaken. In combination with high work control, high superior support was associated with an engaged participant profile. Job demand was the third important variable with low and very high levels associated with participation. With regard to high likelihood of future participation, among respondents with age older than 50, high predictability of occupational activities and control were identified as a significant factor, and among others, high superior support and control. The analysis of narratives revealed peer relations and flexible working hours to be positively linked to participation and general job stress was identified as having a bidirectional relationship. Employees stated that stress led them to take advantage of these programs as a source of relief and that their availability/participation has contributed to lowering their stress. These findings inform practitioners about the importance of addressing poor psychosocial factors as a participation barrier and having a holistic approach to employee well-being.

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task.

The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.

Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.

OBJECTIVE: To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals; (b) potential benefits of using such technologies in the workplace; and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry. BACKGROUND: Wearable sensors are increasingly being promoted as a means to improve employee health and well-being, and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies. METHODS: An electronic survey was emailed to 28,428 registered members of the American Society of Safety Engineers (ASSE) and 1,302 professionals certified by the Board of Certification in Professional Ergonomics (BCPE). RESULTS: A total of 952 valid responses were returned. Over half of respondents described being in favor of using wearable sensors to track OSH-related risk factors and relevant exposure metrics at their respective workplaces. However, barriers including concerns regarding employee privacy/confidentiality of collected data, employee compliance, sensor durability, the cost/benefit ratio of using wearables, and good manufacturing practice requirements were described as challenges precluding adoption. CONCLUSION: The broad adoption of wearable technologies appears to depend largely on the scientific community's ability to successfully address the identified barriers. APPLICATION: Investigators may use the information provided to develop research studies that better address OSH practitioner concerns and help technology developers operationalize wearable sensors to improve employee health and well-being.

Understanding User Requirements for the Design of a Home-Based Stroke Rehabilitation System.

Limitations following stroke make it one of the leading causes of disability. The current medical pathway provides intensive care in the acute stages, but rehabilitation services are commonly discontinued after one year. While written home exercise programs are regularly prescribed at the time of discharge, compliancy is an issue. The goal of this study was to inform the design of a home-based portable rehabilitation system based on feedback from individuals with stroke and clinicians. A main component under consideration is the type and format of information feedback provided to the user, as this is hypothesized to support compliance with the rehabilitation program. From a series of focus groups and usability testing, a set of design requirements for the hardware and software were constructed. Essential features mentioned for the feedback interface included: task completion time, quality of movement, a selection of exercises, goal tracking, and a display of historical data.

A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among U.S. manufacturing workers.

Advanced manufacturing has resulted in significant changes on the shop-floor, influencing work demands and the working environment. The corresponding safety-related effects, including fatigue, have not been captured on an industry-wide scale. This paper presents results of a survey of U.S. manufacturing workers for the: prevalence of fatigue, its root causes and significant factors, and adopted individual fatigue coping methods. The responses from 451 manufacturing employees were analyzed using descriptive data analysis, bivariate analysis and Market Basket Analysis. 57.9% of respondents indicated that they were somewhat fatigued during the past week. They reported the ankles/feet, lower back and eyes were frequently affected body parts and a lack of sleep, work stress and shift schedule were top selected root causes for fatigue. In order to respond to fatigue when it is present, respondents reported coping by drinking caffeinated drinks, stretching/doing exercises and talking with coworkers. Frequent combinations of fatigue causes and individual coping methods were identified. These results may inform the design of fatigue monitoring and mitigation strategies and future research related to fatigue development.

Central and peripheral fatigue development in the shoulder muscle with obesity during an isometric endurance task.

BACKGROUND: Fatigue increases the likelihood of developing work-related musculoskeletal disorders and injury. Due to the physiological and neuromuscular changes that accompany obesity, it may alter the fatigue development mechanism and exacerbate injury risk. The upper extremities have the highest incidence rates for work-related musculoskeletal disorders. Therefore, the goals of this study were to investigate the effect of obesity on central vs. peripheral fatigue as well as on the physical signs of fatigue on the middle deltoid muscle. METHODS: A measure of central activation ratio was used to quantify central fatigue by considering the increment in the torque output by superimposed twitch relative to its corresponding maximum voluntary contraction. For this purpose, electrical stimulation was delivered at the middle deltoid muscles of 22 non-obese (18 < body mass index (BMI) < 25 kg/m2) and 17 obese (30 < BMI < 40 kg/m2) individuals aged 18-32 years old. Participants completed superimposed maximum voluntary isometric contractions of shoulder abduction before and after a sustained isometric fatiguing task at either 30 or 60% of the muscle capacity. Differences in endurance time, torque fluctuation, torque loss, and muscle activity measured by an electromyography sensor were also investigated. RESULTS: A greater reduction of voluntary activation of motor units (p = 0.001) with fatigue was observed for individuals who are obese. Contrary to the effect of obesity on central fatigue, a trend toward reduced peripheral fatigue (p = 0.06) was observed for the obese group compared to the non-obese group. On average, a 14% higher rate of torque loss per second was observed among individuals with obesity in comparison to non-obese participants. CONCLUSIONS: The observed greater contribution of central fatigue during the sustained endurance tasks suggests that among young healthy obese individuals, the faster fatigue development with obesity, commonly reported in the literature, is most likely due to the central elements rather than the peripheral factors. This finding has implications for fatigue prevention programs during sustained exertions and can help to develop training, work, and rest schedules considering obesity.

Improving Teamwork: Evaluating Workload of Surgical Team During Robot-assisted Surgery.

OBJECTIVE: To investigate the cognitive and physical workload experienced by each operating room team member for different types of urologic procedures. METHODS: Surgeons, anesthesiologists, surgical fellows, bedside assistants, circulating nurses, and scrub nurses completed the National Aeronautics and Space Administration Task Load Index questionnaire for various urologic robot-assisted surgery procedures. A total of 338 questionnaires from 55 unique individuals were collected. Workload differences by role, type of procedure, and surgery duration were analyzed using analysis of variance for each of the 6 domains of the National Aeronautics and Space Administration Task Load Index. The effects of trainees' participation on their perceived workload and the workloads of the lead surgeon and bedside assistant were analyzed with correlation. RESULTS: The role of the surgical team was significant for all the scales of workload, and there was a main effect type of surgery on temporal demand and frustration. Frustration was higher for prostatectomy in comparison to cystectomy for the trainee surgeon. On the other hand, it was lower for the anesthesiologist, bedside assistant, and the circulating nurse. There was no effect of procedural complexity on workload. Regardless of surgical complexity, the trainees performed approximately 40% of the procedure without significantly impacting their perceived workload. CONCLUSION: This study provides an analysis of variations and contributors to workload parameters and serves as a platform to optimize team members' workload during robot-assisted surgery.

Testing the efficacy of existing force-endurance models to account for the prevalence of obesity in the workforce.

This study evaluates whether the existing force-endurance relationship models are predictive of endurance time for overweight and obese individuals, and if not, provide revised models that can be applied for ergonomics practice. Data was collected from 141 participants (49 normal weight, 50 overweight, 42 obese) who each performed isometric endurance tasks of hand grip, shoulder flexion, and trunk extension at four levels of relative workload. Subject-specific fatigue rates and a general model of the force-endurance relationship were determined and compared to two fatigue models from the literature. There was a lack of fit between previous models and the current data for the grip (ICC = 0.8), with a shift toward lower endurance times for the new data. Application of the revised models can facilitate improved workplace design and job evaluation to accommodate the capacities of the current workforce.