The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems.

This study assesses the agreement of compressive and shear force estimates at the L5-S1 joint using inertial motion capture (IMC) within a musculoskeletal simulation model during manual lifting tasks, compared against a top-down optical motion capture (OMC)-based model. Thirty-six participants completed lifting and lowering tasks while wearing a modified Plug-in Gait marker set for the OMC and a full-body IMC set-up consisting of 17 sensors. The study focused on tasks with variable load weights, lifting heights, and trunk rotation angles. It was found that the IMC system consistently underestimated the compressive forces by an average of 34% (975.16 N) and the shear forces by 30% (291.77 N) compared with the OMC system. A critical observation was the discrepancy in joint angle measurements, particularly in trunk flexion, where the IMC-based model underestimated the angles by 10.92-11.19 degrees on average, with the extremes reaching up to 28 degrees. This underestimation was more pronounced in tasks involving greater flexion, notably impacting the force estimates. Additionally, this study highlights significant differences in the distance from the spine to the box during these tasks. On average, the IMC system showed an 8 cm shorter distance on the X axis and a 12-13 cm shorter distance on the Z axis during lifting and lowering, respectively, indicating a consistent underestimation of the segment length compared with the OMC system. These discrepancies in the joint angles and distances suggest potential limitations of the IMC system's sensor placement and model scaling. The load weight emerged as the most significant factor affecting force estimates, particularly at lower lifting heights, which involved more pronounced flexion movements. This study concludes that while the IMC system offers utility in ergonomic assessments, sensor placement and anthropometric modeling accuracy enhancements are imperative for more reliable force and kinematic estimations in occupational settings.

Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist.

Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively.

Gyroscope vector magnitude: A proposed method for measuring angular velocities.

High movement velocities are among the primary risk factors for work-related musculoskeletal disorders (MSDs). Ergonomists have commonly used two methods to calculate angular movement velocities of the upper arms using inertial measurement units (accelerometers and gyroscopes). Generalized velocity is the speed of movement traveled on the unit sphere per unit time. Inclination velocity is the derivative of the postural inclination angle relative to gravity with respect to time. Neither method captures the full extent of upper arm angular velocity. We propose a new method, the gyroscope vector magnitude (GVM), and demonstrate how GVM captures angular velocities around all motion axes and more accurately represents the true angular velocities of the upper arm. We use optical motion capture data to demonstrate that the previous methods for calculating angular velocities capture 89% and 77% relative to our proposed method.

Reliability Analysis of Observation-Based Exposure Assessment Tools for the Upper Extremities: A Systematic Review.

(1) Background: The objectives of this systematic review were to (i) summarize the results of studies evaluating the reliability of observational ergonomics exposure assessment tools addressing exposure to physical risk factors associated with upper extremity musculoskeletal disorders (MSDs), and (ii) identify best practices for assessing the reliability of new observational exposure assessment tools. (2) Methods: A broad search was conducted in March 2020 of four academic databases: PubMed, Science Direct, Ergonomic Abstracts, and Web of Science. Articles were systematically excluded by removing redundant articles, examining titles and abstracts, assessing relevance to physical ergonomics and the upper extremities, and article type. (3) Results: Eleven articles were included in the review. The results indicated no singular best practice; instead, there were multiple methodological approaches researchers chose to use. Some of the significant variations in methodologies include the selection of reliability coefficients, rater and participant selection, and direct vs. digital observation. (4) Conclusion: The findings serve as a resource summarizing the reliability of existing observational risk assessment tools and identify common methods for assessing the reliability of new observational risk assessment tools. Limitations of this review include the number of databases searched, the removal of truncation symbols, and the selection of keywords used for the initial search.

Evidence-Based Strategies for Improving Occupational Safety and Health Among Teleworkers During and After the Coronavirus Pandemic.

OBJECTIVE: To review practical, evidence-based strategies that may be implemented to promote teleworker safety, health, and well-being during and after the coronavirus pandemic of 2019 (COVID-19). BACKGROUND: The prevalence of telework has increased due to COVID-19. The upsurge brings with it challenges, including limited face-to-face interaction with colleagues and supervisors, reduced access to ergonomics information and resources, increased social isolation, and blurred role definitions, which may adversely affect teleworker safety, health, and well-being. METHOD: Evidence-based strategies for improving occupational safety, health, and well-being among teleworkers were synthesized in a narrative-based review to address common challenges associated with telework considering circumstances unique to the COVID-19 pandemic. RESULTS: Interventions aimed at increasing worker motivation to engage in safe and healthy behaviors via enhanced safety leadership, managing role boundaries to reduce occupational safety and health risks, and redesigning work to strengthen interpersonal interactions, interdependence, as well as workers' initiation have been supported in the literature. APPLICATION: This review provides practical guidance for group-level supervisors, occupational safety and health managers, and organizational leaders responsible for promoting health and safety among employees despite challenges associated with an increase in telework.

An ergonomic assessment tool for evaluating the effect of back exoskeletons on injury risk.

Low back disorders (LBDs) are a leading injury in the workplace. Back exoskeletons (exos) are wearable assist devices that complement traditional ergonomic controls and reduce LBD risks by alleviating musculoskeletal overexertion. However, there are currently no ergonomic assessment tools to evaluate risk for workers wearing back exos. Exo-LiFFT, an extension of the Lifting Fatigue Failure Tool, is introduced as a means to unify the etiology of LBDs with the biomechanical function of exos. We present multiple examples demonstrating how Exo-LiFFT can assess or predict the effect of exos on LBD risk without costly, time-consuming electromyography studies. For instance, using simulated and real-world material handling data we show an exo providing a 30 Nm lumbar moment is projected to reduce cumulative back damage by ∼70% and LBD risk by ∼20%. Exo-LiFFT provides a practical, efficient ergonomic assessment tool to assist safety professionals exploring back exos as part of a comprehensive occupational health program.

Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts.

Wearable inertial sensors may be used to objectively quantify exposure to some physical risk factors associated with musculoskeletal disorders. However, concerns regarding their potential negative effects on user safety and satisfaction remain. This study characterized the self-reported daily discomfort, distraction, and burden associated with wearing inertial sensors on the upper arms, trunk, and dominant wrist of 31 manufacturing workers collected over 15 full work shifts. Results indicated that the workers considered the devices as generally comfortable to wear, not distracting, and not burdensome to use. Exposure to non-neutral postures (discomfort, right arm, beta = 0.02; trunk, beta = -0.01), non-cyclic tasks (distraction, beta = -0.26), and higher body mass indices (discomfort, beta = 0.05; distraction, beta = 0.02) contributed to statistically significant (p < 0.05), albeit practically small increases in undesirable ratings. For instance, for each additional percentage of time working with the right arm elevated ≥60°, self-reported discomfort ratings increased 0.02 cm on a standard 10 cm visual analog scale. Female workers reported less discomfort and distraction while wearing the sensors at work than males (discomfort, beta = -0.93; distraction, beta = -0.3). In general, the low ratings of discomfort, distraction, and burden associated with wearing the devices during work suggests that inertial sensors may be suitable for extended use among manufacturing workers.

EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee.

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

Comparing upper arm and trunk kinematics between manufacturing workers performing predominantly cyclic and non-cyclic work tasks.

Musculoskeletal disorders (MSDs) are common among manufacturing workers. Exposure to non-neutral postures and high movement speeds associated with MSDs among manufacturing workers may depend on the extent of the variability in the work tasks performed (i.e., predominantly "cyclic" versus "non-cyclic" work). The objectives of this study were to (i) compare mean levels of full-shift exposure summary metrics based on both posture and movement speed between manufacturing workers performing predominantly cyclic (n = 18) and non-cyclic (n = 17) tasks, and (ii) explore patterns of between- and within-worker exposure variance and between-minute (within-shift) exposure level and variation within each group. Inertial sensors were used to measure exposures for up to 15 full shifts per participant. Results indicated (i) substantially higher upper arm and trunk movement speeds among workers performing predominantly cyclic tasks relative to workers performing non-cyclic tasks despite similar postures, and (ii) greater exposure variability both between and within workers in the non-cyclic group.

Development and validation of a cumulative exposure shoulder risk assessment tool based on fatigue failure theory.

OBJECTIVE: To present a new risk assessment tool for shoulder intensive occupational tasks based on fatigue failure theory. METHODS: The tool estimates cumulative damage (CD) based on shoulder moments and loading cycles using an S-N curve derived from in vitro tendon fatigue failure tests. If multiple shoulder tasks are performed, the CD for each is summed. In the validation, 293 workers were evaluated for five separate shoulder outcomes. Logistic regression was used to assess the log CD against five shoulder outcomes adjusted for covariates including age, sex, body mass index (BMI), and plant site. RESULTS: Both crude and adjusted logistic regression results demonstrated strong dose-response associations between the log CD measure and all five shoulder outcomes (continuous ORs ranged from 2.12 to 5.20). CONCLUSIONS: The CD measure of The Shoulder Tool demonstrated dose-response relationships with multiple health outcomes. This provides further support that MSDs may be the result of a fatigue failure process. PRACTITIONER SUMMARY: This study presents a new, easy-to-use risk assessment tool for occupational tasks involving stressful shoulder exertions. The tool is based on fatigue failure theory. The tool was tested against an existing epidemiology study and demonstrated strong relationships to multiple shoulder outcomes. ABBREVIATIONS: MSD: musculoskeletal disorder; NORA: national occupational research agenda; RULA: rapid upper limb assessment; REBA: rapid entire body assessment; S-N: stress-number of cycles; EDL: extensor digitorum longus; DPC: damage per cycle; CD: cumulative damage; UTS: ultimate tensile strength; FTOV: first time office visit; 3DSSPP: 3-dimensional static strength prediction program; AS: visual analogue scale; BMI: body mass index; CI: confidence interval; Nm: newton-metre; LiFFT: lifting fatigue failure tool; DUET: distal upper extremity tool; OMNI-RES: OMNI resistance exercise scale.

Measuring upper arm elevation using an inertial measurement unit: An exploration of sensor fusion algorithms and gyroscope models.

Many sensor fusion algorithms for analyzing human motion information collected with inertial measurement units have been reported in the scientific literature. Selecting which algorithm to use can be a challenge for ergonomists that may be unfamiliar with the strengths and limitations of the various options. In this paper, we describe fundamental differences among several algorithms, including differences in sensor fusion approach (e.g., complementary filter vs. Kalman Filter) and gyroscope error modeling (i.e., inclusion or exclusion of gyroscope bias). We then compare different sensor fusion algorithms considering the fundamentals discussed using laboratory-based measurements of upper arm elevation collected under three motion speeds. Results indicate peak displacement errors of <4.5° with a computationally efficient, non-proprietary complementary filter that did not account for gyroscope bias during each of the one-minute trials. Controlling for gyroscope bias reduced peak displacement errors to <3.0°. The complementary filters were comparable (<1° peak displacement difference) to the more complex Kalman filters.

Biomechanical factors during common agricultural activities: Results of on-farm exposure assessments using direct measurement methods.

Agricultural work is associated with increased risk of adverse musculoskeletal health outcomes. The purpose of this study was to quantify exposure to biomechanical factors among a sample (n = 55) of farmers in the Midwest region of the U.S. while they performed a variety of routine agricultural activities, and to compare exposure levels between these activities. Surface electromyography was used to estimate activity levels of the erector spinae, upper trapezius, forearm flexor, and forearm extensor muscle groups. Simultaneously, inertial sensors were used to measure kinematics of the trunk, upper arm, and wrist. In general, lower muscle activity levels, less extreme postures, and slower movement speeds were observed during activities that involved primarily the use of agricultural machinery in comparison to manual activities, suggesting a potential advantage of mechanization relative to musculoskeletal health. Median wrist movement speeds exceeding recently proposed exposure thresholds were also observed during many manual activities, such as milking animals and repairing equipment. Upper arm postures and movement speeds did not appear to confer excessive risk for shoulder-related outcomes (on the whole), but interpretation of the results is limited by a sampling approach that may not have captured the full extent of exposure variation. Not surprisingly, substantial variation in exposure levels were observed within each agricultural activity, which is related to substantial variation in the equipment, tools, and work practices used by participants. Ultimately, the results of this study contribute to an emerging literature in which the physical demands of routine agricultural work have been described on the basis of sensor-based measurements rather than more common self-report or observation-based approaches.

Job rotation and work-related musculoskeletal disorders: a fatigue-failure perspective.

Job rotation is an organisational strategy that can be used, in part, to reduce occupational exposure to physical risk factors associated with work-related musculoskeletal disorders (MSDs). Recent studies, however, suggest that job rotation schedules may increase the overall risk of injury to workers included in the rotation scheme. We describe a novel optimisation framework evaluating the effectiveness of a job rotation scheme using the fatigue failure model of MSD development and a case study with real injury data. Results suggest that the effect of job rotation is highly-dependent on the composition of the job pool, and inclusion of jobs with higher risk results in a drastic decrease in the effectiveness of rotation for reducing overall worker risk. The study highlights that in cases when high-risk jobs are present, job redesign of those high risk tasks should be the primary focus of intervention efforts rather than job rotation. Practitioner summary: Job rotation is often used in industry as a method to 'balance' physical demands experienced by workers to reduce musculoskeletal disorder (MSD) risk. This article examines the efficacy of reducing MSDs through job rotation using numerical simulation of job rotation strategies and utilising the fatigue failure model of MSD development.

Sleep and stress before and after duty across residency years under 2017 ACGME hours.

INTRODUCTION: Residents may differentially experience high stress and poor sleep across multiple post-graduate years (PGYs), negatively affecting safety. This study characterized sleep and stress among medical and surgical residents across multiple PGYs and at specific times surrounding duty. METHOD: Thirty-two medical and surgical residents (Mage = 28.6 years; 56% male) across PGYs 1-5 participated in 3 appointments (immediately before duty, after duty, and on an off day) providing 96 data points. Sleep, stress, and occupational fatigue were measured by both self-report and objectively (actigraphy, salivary coritsol). RESULTS: Residents averaged 7 h of actigraphy-estimated sleep per night but varied ±3 h day-to-day. Residents reported clinically poor sleep quality. Life stress decreased by PGY-2. All residents averaged elevated life stress values. Poor sleep quality did not differ among PGY cohorts. DISCUSSION: Poor sleep quality is similar between early residency cohorts (PGY-1) and later residency cohorts (PGY-3+). Persistent fatigue is highest in later residency cohorts. Even the most experienced residents may struggle with persisting fatigue. Current hour policies may have shortcomings in addressing this risk.

Effects of age and obesity on trunk kinetics and kinematics during dominant side one-handed carrying.

The proportions of older and obese people are increasing in both the general and working populations worldwide. Older and obese individuals are more susceptible to work-related musculoskeletal disorders (MSDs) in comparison with healthy, younger individuals. Manual material handling (MMH) is associated with the development of work-related MSDs. Although previous research has suggested that one-handed carrying is a particularly undesirable method of MMH, the effects of one-handed carrying on trunk kinetics and kinematics among older and/or obese people have not been adequately studied. The objective of this study was to examine the effects of age and obesity on trunk angles and moments during dominant side one-handed carrying of various load magnitudes. Twenty (20) participants divided into four groups with respect to age (young and older) and obesity (obese and non-obese) carried different loads (No-load [0 kg], Light [5.67 kg], and Heavy [10.21 kg]) in their dominant hand for approximately 6 m. Three-dimensional (3D) trunk angles and moments approximately about the L4/L5 vertebral segment were calculated using Visual3D. The findings indicated that while carrying a load in the dominant hand plays an important role in changing trunk kinematics and kinetics, the results were not dependent on age and/or obesity category. Absolute moments were greatest among participants in the obese groups; however, these moments were mitigated when normalized to body weight and height (%BW * Ht). Age did not exacerbate the effects of load magnitude on trunk kinetics and kinematics.

Trunk muscle activity among older and obese individuals during one-handed carrying.

Manual material handling (MMH) is associated with the development of work-related musculoskeletal disorders (MSDs). One-handed carrying is a particularly challenging form of MMH. Age and obesity have been increasing among the general and working populations in the United States and worldwide. While older and obese workers are more susceptible to MSDs in comparison to younger, healthy workers, the effects of one-handed carrying on trunk muscle activity among these populations have not been comprehensively studied. In this paper, we evaluate the effects of age and obesity on trunk muscle activity of six trunk muscle pairs during one-handed carrying of different loads. The results suggest that older and obese individuals do not exhibit considerably larger muscle activity than young and non-obese individuals while carrying a load of approximately 10 kg in one hand for relatively short distances. Accordingly, 10 kg appears to be an acceptable load to be occasionally carried in one hand by older and/or obese individuals from a muscle activity perspective.

Improving the risk assessment capability of the revised NIOSH lifting equation by incorporating personal characteristics.

The impact of manual material handling such as lifting, lowering, pushing, pulling and awkward postures have been studied, and models using these external demands to assess risk of injury have been developed and employed by safety and health professionals. However, ergonomic models incorporating personal characteristics into a comprehensive model are lacking. This study explores the utility of adding personal characteristics such as the estimated L5/S1 Intervertebral Disc (IVD) cross-sectional area, age, gender and Body Mass Index to the Revised NIOSH Lifting Equation (RNLE) with the goal to improve risk assessment. A dataset with known RNLE Cumulative Lifting Indices (CLIs) and related health outcomes was used to evaluate the impact of personal characteristics on RNLE performance. The dataset included 29 cases and 101 controls selected from a cohort of 1022 subjects performing 667 jobs. RNLE risk assessment was improved by incorporation of personal characteristics. Adding gender and intervertebral disc size multipliers to the RNLE raised the odds ratio for a CLI of 3.0 from 6.71 (CI: 2.2-20.9) to 24.75 (CI: 2.8-215.4). Similarly, performance was either unchanged or improved when some existing multipliers were removed. The most promising RNLE change involved incorporation of a multiplier based on the estimated IVD cross-sectional area (CSA). Results are promising, but confidence intervals are broad and additional, prospective research is warranted to validate findings.

Whole-Body Vibration and Trunk Posture During Operation of Agricultural Machinery.

Exposure to whole-body vibration (WBV) is common among agricultural workers and is associated with musculoskeletal health outcomes such as low back pain. Little is known, however, about the characteristics of exposure experienced during actual production practices. We measured WBV levels during agricultural machinery use among a sample of farmers (n = 55) performing routine agricultural activities and explored machinery attributes that may explain WBV summary measures. We also measured trunk posture to provide additional information about physical exposures during machinery operation. Measurements were made on-farm and during actual work conditions of a sample of agricultural machines (n = 112), including tractors, combines, heavy utility vehicles, and all-terrain vehicles (ATVs). Results indicated the presence of high levels of vibration (median frequency-weighted root-mean-square acceleration of approximately 0.8 m s-2) with time signatures that include high-amplitude mechanical shocks (median crest factor of nearly 23). Compared to other machinery types, combines exhibited the lowest WBV levels and among the most favorable trunk postures. Substantial variability was observed in both the WBV and trunk posture summary measures, suggesting for future studies that alternative sampling strategies are needed to fully capture temporal patterns of machinery use.