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

Innovative Approaches for the Treatment of Spinal Disorders: A Comprehensive Review

This comprehensive review explores the latest advancements in the management of spinal disorders, including minimally invasive surgical techniques, treatment of complex deformities, disc replacement technologies, and non-surgical approaches. The review highlights the potential of innovations such as robotic-assisted surgeries, regenerative medicine, and artificial intelligence to enhance precision, reduce recovery times, and improve patient outcomes. It also discusses the integration of wearable technologies and personalized medicine in tailoring treatments. Challenges such as high costs, accessibility issues, and limited long-term data are critically analyzed, alongside gaps in research, including a lack of diversity in study populations and insufficient economic evaluations. Future directions emphasize the need for multidisciplinary collaboration to develop durable, accessible, and personalized solutions to address the global burden of spinal disorders.

Effects of a Passive Back-Support Exosuit on Objective and Subjective Measures of Human Performance During a Simulated Bush-Crop Harvesting Task

OBJECTIVE

Interest in wearable passive back-supports (exoskeletons/suits) has grown rapidly as a tool to reduce the risk of low back injury by reducing lumbar extensor muscle loading. Previous studies have shown the effectiveness of passive back-support exoskeleton/suit at reducing low back muscle activity/fatigue in a variety of tasks, but it is unclear whether an exoskeleton/exosuit intervention would be effective in agricultural harvesting tasks that require complex three-dimensional dynamic motions, long duration stooped postures, and variable engagement of the lower extremities. The objective of the current study was to evaluate the effects of a passive lumbar support exosuit on muscle fatigue and comfort/mobility during a simulated harvesting task in a controlled laboratory setting.

METHODS

Sixteen participants were asked to perform a continuous work task that simulated a 3-min bout of harvesting from a bush crop. Participants harvested at a rate of 1 unit per 2 s and were permitted to assume any effective harvesting position except a full kneeling posture. Test contractions and subjective assessments were performed before and after each 3-min bout of a simulated harvesting task to allow for an assessment of the 1) changes in objective measures of erector spinae muscle fatigue development (both time domain and frequency domain measures of muscle fatigue) and 2) subjective measures of physical fatigue, and 3) exosuit comfort and mobility constraints.

RESULTS

The exosuit significantly mitigated the increases in EMG amplitude in the time domain (p = .015; Cohen's d = 0.46) indicating a mitigation of muscle fatigue, but there was no significant exosuit effect on median frequency (p = .145) or perceived fatigue in the low back (p = .289). In addition, the exosuit use was associated with significant increases in perceived movement restriction (p < .001; d = 0.82) which were also manifested in terms of significant effects of the exosuit on the magnitude of the 90th percentile of the trunk flexion (p = .027, d = 0.29).

CONCLUSION

The results of this study demonstrated moderate muscle fatigue reduction effects at the cost of a negative impact on objective and subjective measures of trunk motion restrictions and comfort.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task

OBJECTIVE

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots.

BACKGROUND

Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated.

METHODS

Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics.

RESULTS

HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7).

CONCLUSION

HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation.

APPLICATION

These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Reliability and Accuracy of Standard Reference Procedures for Measurements of Trunk and Arm Postures in Ergonomics

Adequate reference procedures for obtaining the reference zero-angle position are important for precise and accurate posture measurements, but few studies have systematically investigated these. A limited number of previous studies suggest differences in accuracy between procedures, with some causing an underestimation of the true arm elevation angle when sensors are taped to the skin. The reliability of commonly used reference procedures for the measurement of the trunk posture is also not well explored, and alternative procedures may improve precision. Based on this identified gap, this study evaluated the test-retest reliability of the N-position (I-pose), i.e., the standard procedure for recording trunk postures, and compared it with two new alternative procedures. Additionally, the accuracy of the N-position for measuring arm elevation angles was compared with one alternative procedure. A total of 40 participants (22 women and 18 men) aged 26-70 years performed the reference procedures in a laboratory setting. Postures were recorded using a smart workwear system equipped with two inertial measurement units (IMUs) embedded in pockets within the workwear. For the trunk posture, the N-position showed a slight lack of test-retest reliability, while one of the alternative procedures demonstrated better test-retest reliability. For the arm posture, the N-position, which does not include lateral trunk inclination, resulted in a substantial underestimation of the arm elevation angle of approximately 15°, which is a novel finding. In contrast, the posture involving trunk inclination closely matched the targeted reference, with a difference of less than 2°. This study underscores the importance of selecting appropriate reference procedures to ensure precise and accurate posture measurements.

The Use of Wearable Systems for Assessing Work-Related Risks Related to the Musculoskeletal System-A Systematic Review

Work-related musculoskeletal disorders (WRMSDs) are a leading cause of chronic conditions among working-age adults. Preventing these disorders is crucial to reducing their impact, and quantitative analysis through sensors can help identify their causes and guide ergonomic solutions. This systematic review aims to compile research from 2000 to 2023 published in English and sourced from Web of Science, Scopus, or PubMed that examines workers' movements during tasks using wearable sensor systems that are applicable in workplace settings. The goal is to identify the job sectors that have been studied and highlight tasks lacking ergonomic risk research. A total of 111 papers were selected through a screening process across three databases, assessed using the McMaster risk of bias tool. The studies span various job sectors and report on the use of different technologies for data collection and study population sizes. The review identifies existing research on WRMSD risks utilizing wearable systems in different job sectors, drawing attention to under-researched areas that warrant further study. It serves as a foundation for future research aimed at understanding the causes of WRMSDs and developing solutions supported by wearable technologies to mitigate these risks.

Evaluation of tomato farmworker upper limb ergonomic risk factors

Research on musculoskeletal disorders among tomato farmworkers is limited. This pilot study aims to generate insights and preliminary data on tasks performed by tomato farmworkers at a demonstration test plot, evaluating associated ergonomic risk factors. A demonstration test plot was constructed to simulate tasks performed by tomato farmworkers. Muscle activity in the anterior deltoid and upper trapezius was measured using surface electromyography (sEMG) and compared among tasks. The intensity of physical activity during stake pounding, bucket tossing, and tying tasks was monitored using a GT9X activity monitor. Non-parametric ANOVA revealed that the upper trapezius exhibited the highest muscle activity in all tasks (p < 0.05). Stake pounding showed the highest muscle activity, peak loads, and the most intense physical activity. Future studies will focus on assessing tomato farmworker activities over longer shifts and evaluating the impact of posture, fatigue, and energy expenditure on the risk for musculoskeletal disorders.

Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75-0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

Defining humeral axial rotation with optical motion capture and inertial measurement units during functional task assessment

Humeral motion can be challenging to measure and analyze. Typically, Euler/Cardan sequences are used for humeral angle decomposition, but choice of rotation sequence has substantial effects on outcomes. A new method called True axial rotation calculation may be more precise. The objective of this study is to compare humeral axial rotation measured from two systems (optical motion capture and inertial measurement units (IMUs)) and calculated with two methods (Euler angles and True axial). Motion of torso and dominant humerus of thirty participants free from any upper limb impairments was tracked using both systems. Each participant performed a functional tasks protocol. Humeral axial rotation was calculated with Euler decomposition and the True axial method. Waveforms were compared with two-way ANOVA statistical parametric mapping. A consistent pattern emerged: axial rotation was not different between motion capture systems when using the True axial method (p > .05), but motion capture systems showed relatively large magnitude differences (~ 20-30°) when using Euler angle calculation. Between-calculation method differences were large for both motion capture systems. Findings suggest that the True axial rotation method may result in more consistent findings that will allow for precise measurements and comparison between motion capture systems. Two methods for calculating humeral axial rotation measured from optical motion capture and inertial measurement units were compared.

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.

Commonly Used Subjective Effort Scales May Not Predict Directly Measured Physical Workloads and Fatigue in Hispanic Farmworkers

In North America, Hispanic migrant farmworkers are being exposed to occupational ergonomic risks. Due to cultural differences in the perception and reporting of effort and pain, it was unknown whether standardized subjective ergonomic assessment tools could accurately estimate the directly measured their physical effort. This study investigated whether the subjective scales widely used in exercise physiology were associated with the direct measures of metabolic load and muscle fatigue in this population. Twenty-four migrant apple harvesters participated in this study. The Borg RPE in Spanish and the Omni RPE with pictures of tree-fruit harvesters were used for assessing overall effort at four time points during a full-day 8-h work shift. The Borg CR10 was used for assessing local discomfort at the shoulders. To determine whether there were associations between the subjective and direct measures of overall exertion measures, we conducted linear regressions of the percentage of heart rate reserve (% HRR) on the Borg RPE and Omni RPE. In terms of local discomfort, the median power frequency (MPF) of trapezius electromyography (EMG) was used for representing muscle fatigue. Then full-day measurements of muscle fatigue were regressed on the Borg CR10 changes from the beginning to the end of the work shift. The Omni RPE were found to be correlated with the % HRR. In addition, the Borg RPE were correlated to the % HRR after the break but not after the work. These scales might be useful for certain situations. In terms of local discomfort, the Borg CR10 were not correlated with the MPF of EMG and, therefore, could not replace direct measurement.

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological variations, and discomfort of the tractor operators arriving from the repeated clutch and brake operations using wearable non-invasive ergonomic transducers and data-run techniques. Nineteen licensed tractor operators operated three different tractor types of varying power ranges at three operating speeds (4-5 km/h), and on two common operating surfaces (tarmacadam and farm roads). During these operations, ergonomic transducers were utilized to capture the load on foot muscles (gastrocnemius right [GR] and soleus right [SR] for brake operation and gastrocnemius left [GL], and soleus left [SL] for clutch operation) using electromyography (EMG). Forces exerted by the feet during brake and clutch operations were measured using a custom-developed foot transducer. During the process, heart rate (HR) and oxygen consumption rates (OCR) were also measured using HR monitor and K4b2 systems, and energy expenditure rate (EER) was determined using empirical equation. Post-tractor operation cycle, an overall discomfort rating (ODR) for that operation was manually recorded on a 10-point psychophysical scale. EMG-based maximum volumetric contraction (%MVC) measurements revealed higher strain on GR (%MVC = 43%), GL (%MVC = 38%), and SR (%MVC = 41%) muscles which in normal conditions should be below 30%. The clutch and brake actuation forces were recorded in the ranges of 90-312 N and 105-332 N, respectively and were significantly affected by the operating speed, tractor type, and operating surface (p < 0.05). EERs of the operators were measured in the moderate-heavy to heavy ranges (9-24 kJ/min) during the course of trials, suggesting the need to refine existing clutch and brake system designs. Average operator ODR responses indicated 7.8% operations in light, 48.5% in light-moderate, 25.2% in moderate, 10.7% in moderate-high, and 4.9% operations in high discomfort categories. When evaluated for the possibility of minimizing the number of transducers for physical workload assessment, EER showed moderate-high correlations with the EMG signals (r_GR = 0.78, r_GL = 0.75, r_SR = 0.68, r_SL = 0.66). Similarly, actuation forces had higher correlations with EMG signals for all the selected muscles (r = 0.70-0.87), suggesting the use of simpler transducers for effective operator workload assessment. As a means to minimize subjectivity in ODR responses, machine learning algorithms, including K-nearest neighbor (KNN), random forest classifier (RFC), and support vector machine (SVM), predicted the ODR using body mass index (BMI), HR, EER, and EMG at high accuracies of 87-97%, with RFC being the most accurate. Such high-throughput and data-run ergonomic evaluations can be instrumental in reconsidering workplace designs and better fits for end-users in terms of agricultural tractors and machinery systems.

Novel methods to detect impacts within whole-body vibration time series data

We present three candidate mathematical models for detecting impacts within time series accelerometer data in the context of whole-body vibration (WBV). In addition to WBV, data included recordings of erector spinae muscle activity and trunk posture collected during use of agricultural machines in a previous study. For each model, we evaluated associations between several mechanical and biomechanical variables at the time of predicted impact onset and the odds of subsequently observing a bilateral response of the erector spinae muscles. For all models, trunk posture at the time of impact onset was strongly associated with an observed bilateral muscle response; these associations were not observed when impacts were randomly assigned. Results provide a framework for describing the number and magnitudes of impacts that may help overcome ambiguities in current exposure metrics, such as the vibration dose value, and highlight the importance of considering posture in the evaluation of occupational WBV exposures. Practitioner summary: Common metrics of exposure to whole-body vibration do not quantify the number or magnitudes of impacts within time series accelerometer data. Three candidate impact detection methods are presented and evaluated using real-world data collected during use of agricultural machines. Results highlight the importance of considering posture when evaluating vibration exposure.

Mind the gap - development of conversion models between accelerometer- and IMU-based measurements of arm and trunk postures and movements in warehouse work

Sensor type (accelerometers only versus inertial measurement units, IMUs) and angular velocity computational method (inclination versus generalized velocity) have been shown to affect the measurements of arm and trunk movements. This study developed models for conversions between accelerometer and IMU measurements of arm and trunk inclination and between accelerometer and IMU measurements of inclination and generalized (arm) velocities. Full-workday recordings from accelerometers and IMUs of arm and trunk postures and movements from 38 warehouse workers were used to develop 4 angular (posture) and 24 angular velocity (movement) conversion models for the distributions of the data. A power function with one coefficient and one exponent was used, and it correlated well (r2 > 0.999) in all cases to the average curves comparing one measurement with another. These conversion models facilitate the comparison and merging of measurements of arm and trunk movements collected using the two sensor types and the two computational methods.

Ergonomic Improvements to Agricultural Harvest Baskets to Reduce the Risk of Musculoskeletal Disorders among Farmers

Typical harvesting baskets (TB) are used in various agricultural workplaces; however, no study to date has reported their effect on the musculoskeletal system. Therefore, this study aimed to evaluate the effects of a novel basket with attached rotational handles (RHB) to help alleviate the work-related physical burden of farmers. We analyzed the surface electromyograms (EMGs) of seven muscles, evaluated the subjective discomfort levels and locally perceived discomfort (LPD) scores to investigate the discomfort in the whole body and seven hand muscles, respectively. The EMGs showed that muscle activity decreased in five muscles (flexor carpi ulnaris, extensor carpi radialis, lateral triceps, middle deltoid, and upper trapezius) and increased in two (biceps brachii and erector spinae) when the RHB was used (p < 0.05). The subjective discomfort score for the RHB decreased compared to that for TB (p < 0.001). The LPD scores also decreased, and the RHB and TB scores ranged from 1.25-1.40 and 3.1-3.25, respectively. The use of the RHB may prevent wrist bending, and reduce the activity of certain muscles while increasing the activity of other muscles. Therefore, it is necessary to conduct training and to evaluate the working posture while considering the affected muscles.

Machine learning methods for electromyography error detection in field research: An application in full-shift field assessment of shoulder muscle activity in apple harvesting workers

This study presented an alternative technique for processing electromyography (EMG) data with sporadic errors due to challenges associated with the field collection of EMG data. The application of this technique was used to detect errors, clean and optimize EMG data in order characterize and compare shoulder muscular load in farmworkers during apple harvesting in a trellised orchard. Surface EMG was used to take measurements from twenty-four participants in an actual field work environment. Anomalies in the EMG data were detected and removed with a customized algorithm using principal component analysis, interquartile range cut-off and unsupervised cluster analysis. This study found significantly greater upper trapezius muscle activity in farmworkers who used a ladder as compared to the alternative platform-based method where a team of mobile platform workers harvested apples from the tree tops and a second separate team of ground workers harvested apples from the tree bottoms. By comparing the unprocessed and the processed, anomaly-free EMG data, the robustness of our proposed method was demonstrated.

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.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements

Accelerometer-based inclinometers have dominated kinematic measurements in previous field studies, while the use of inertial measurement units that additionally include gyroscopes is rapidly increasing. Recent laboratory studies suggest that these two sensor types and the two commonly used angular velocity computational methods may produce substantially different results. The aim of this study was, therefore, to evaluate the effects of sensor types and angular velocity computational methods on the measures of work postures and movements in a real occupational setting. Half-workday recordings of arm and trunk postures, and movements from 38 warehouse workers were compared using two sensor types: accelerometers versus accelerometers with gyroscopes-and using two angular velocity computational methods, i.e., inclination velocity versus generalized velocity. The results showed an overall small difference (<2° and value independent) for posture percentiles between the two sensor types, but substantial differences in movement percentiles both between the sensor types and between the angular computational methods. For example, the group mean of the 50th percentiles were for accelerometers: 71°/s (generalized velocity) and 33°/s (inclination velocity)-and for accelerometers with gyroscopes: 31°/s (generalized velocity) and 16°/s (inclination velocity). The significant effects of sensor types and angular computational methods on angular velocity measures in field work are important in inter-study comparisons and in comparisons to recommended threshold limit values.

Evaluation of tractor driving vibration fatigue based on multiple physiological parameters

The vibration generated by tractor field operations will seriously affect the comfort and health of the driver. The low frequency vibration generated by the engine and ground excitation is similar to the natural frequency of human organs. Long term operation in this environment will resonate with the organs and affect drivers' health. To investigate this possibility, in this paper we carried out a collection experiment of human physiological indicators relevant to vibration fatigue. Four physiological signals of surface electromyography, skin electricity, skin temperature, and photoplethysmography signal were collected while the subjects experienced vibration. Several features of physiological signals as well as the law of signal features changing with fatigue are studied. The test results show that with the increase of human fatigue, the overall physiological parameters show the following trends: The median frequency of the human body surface electromyography and the slope of skin surface temperature decreases, the value of skin conductivity and the mean value of the photoplethysmography signal increases. Furthermore, this paper proposes a vibration comfort evaluation method based on multiple physiological parameters of the human body. An artificial neural network model is trained with test samples, and the prediction accuracy rate reaches 88.9%. Finally, the vibration conditions are changed by the shock-absorbing suspension of a tractor, verifying the effectiveness of the physiological signal changing with the vibration of the human body. The established prediction model can also be used to objectively reflect the discomfort of the human body under different working conditions and provide a basis for structural design optimization.