A comparison of instrumentation methods to estimate thoracolumbar motion in field-based occupational studies

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.

Trunk range of motion: A wearable sensor-based test protocol and indicator of fall risk in older people

Conventionally, trunk range of motion (TROM) requires manual measurement by an external health professional with a general-purpose goniometer. This study aims to propose a convenient test protocol to assess TROM based on a single wearable sensor and to further investigate the relationship between TROM and fall risk of older people. We first explored the optimal sensor position by comparing TROMs from four representative locations (T1, T12, L5 and sternum) and optical motion capture system (golden reference). A follow-up experiment was conducted to evaluate the relationship between TROM and fall risk. The results showed that T12 achieved the minimum root mean square error (3.8 ± 2.2°) against the golden reference and the non-faller group had significantly higher TROMs than the faller group. These findings suggest that the newly proposed protocol is convenient yet valid and TROM can be a promising indicator of fall risk in older people.

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.

Continuous assessment of trunk posture in healthcare workers assigned to wards with different MAPO index

Healthcare professionals generally experience an above-average incidence of low back disorders (LBDs) compared with workers of other professions, and its level of risk is commonly assessed using observational methods such as the MAPO method (Movement and Assistance of Hospital Patients). In this study, we continuously monitored the trunk posture of 30 healthcare workers using a single inertial sensor to: (1) understand whether the MAPO classification is effective in adequately discriminating the risk associated with the time spent in non-neutral trunk postures and (2) characterise the variability of biomechanical exposure among workers employed in wards with the same MAPO index. The results substantially confirm the validity of the MAPO approach in discriminating among wards characterised by different levels of biomechanical exposure associated with the risk of developing LBDs. However, they also highlight the need to assess workers' exposure on an individual basis due to the high intra-group variability.Practitioner summary: Employing a quantitative measurement setup to monitor trunk posture along with an observational method (ie MAPO) can identify the existence of criticalities or the poor application of ergonomic recommendations given during the training of healthcare workers even in hospital wards characterised by little or no risk of developing low back disorders.

Curative Effect of Foraminal Endoscopic Surgery and Efficacy of the Wearable Lumbar Spine Protection Equipment in the Treatment of Lumbar Disc Herniation

Lumbar disc herniation is a common and frequently-occurring disease in pain clinics. The incidence rate of affliction is increasing with every passing year. Besides the aged, young people also suffer from long-term pain, which not only affects their daily routines but may also lead to serious impairment. The causes of chronic low back and leg pain caused by lumbar disc herniation are mainly related to mechanical compression, the adhesion of epidural space, intervertebral space, and aseptic inflammatory reaction. The treatment of lumbar disc herniation should follow the principle of step-by-step treatment. An appropriate treatment scheme needs to be adopted according to the patient's condition. About 80% of patients received nonsurgical treatment to get relief from the pain symptoms. However, 10% to 15% of patients still need traditional open surgery. Spinal foraminal surgery is a new method for the treatment of lumbar disc herniation, lumbar surgery failure syndrome, and lumbar spinal stenosis. However, there are only scattered clinical reports on the efficacy of spinal foraminal surgery. Based on it, this paper proposes a method to explore the efficacy of spinal foraminal mirror surgery in the treatment of lumbar disc herniation. Besides, postoperative wearable lumbar protective equipment is proposed to ensure a seamless rehabilitation effect on the patients. Statistical analysis performed using a t-test revealed that there was a significant difference between the visual analog scales (VAS) scores of the two groups after 3 and 6 months of treatment (P < 0.05). The paper analyzes and summarizes the cases with definite and poor curative effects, which not only provides the basis for clinical practice but also paves the way to multicenter clinical research.

Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies

Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMU_norm and IMU_flex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMU_norm and <4.1°/s for IMU_flex. During wrist deviation and pronation/supination, all methods showed errors, where the IMU_norm method had the largest overestimations. For simulated work tasks, the IMU_flex method had small bias and better accuracy than the IMU_norm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMU_flex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures.

Use of wearable sensors to assess patterns of trunk flexion in young and old workers in the Metalworking Industry

Workers exposed to repeated trunk flexions are at risk of onset of low-back disorders and in individuals aged over 50 this issue is exacerbated by the physiologic decline of the musculoskeletal system and longer lifetime occupational exposure. In this study, we investigated the existence of possible age-related differences in patterns of trunk flexion of workers in the metalworking industry. Thirty-three subjects were monitored during an actual shift using a wearable Inertial Measurement Unit (IMU) to assess trunk flexion angles (i.e. between 30° and 60°, 60°-90° and > 90°). Results show that older workers spent less time with their trunk flexed, regardless of the class of flexion considered, with respect to their younger colleagues. Although further studies are necessary to clarify the existence of strategies aimed at optimising trunk movements during ageing, the IMU-based approach appears useful in highlighting potentially harmful conditions, especially in workers with marked signs of decline in their physical capacities. Practitioner summary: Wearable sensors, which are well tolerated and minimally intrusive, represent a valid option to continuously monitor trunk posture in workers employed in metalworking industry. The results of this study show that they provide valuable information about the patterns of flexion of young and old individuals engaged in physically demanding tasks.

Trunk Posture during Manual Materials Handling of Beer Kegs

Craft brewing is a rapidly growing industry in the U.S. Most craft breweries are small businesses with few resources for robotic or other mechanical-assisted equipment, requiring work to be performed manually by employees. Craft brewery workers frequently handle stainless steel half-barrel kegs, which weigh between 13.5 kg (29.7 lbs.) empty and 72.8 kg (161.5 lbs.) full. Moving kegs may be associated with low back pain and even injury. In the present study, researchers performed a quantitative assessment of trunk postures using an inertial measurement unit (IMU)-based kinematic measurement system while workers lifted kegs at a craft brewery. Results of this field-based study indicated that during keg handling, craft brewery workers exhibited awkward and non-neutral trunk postures. Based on the results of the posture data, design recommendations were identified to reduce the hazardous exposure for musculoskeletal disorders among craft brewery workers.

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.

A Mobile Application to Measure Trunk Flexion Angles in Lifting Tasks

Occupational Abstract The aim of this project was to develop and test an application capable of quickly and repeatedly measuring trunk flexion angles during sagittal plane lifting tasks. The developed application uses the built-in accelerometer in mobile devices to approximate trunk flexion angle, as the user follows an operator as they perform a lift. A black line is superimposed over the camera feed, allowing the user to approximate the angle of inclination of a line connecting the operator's seventh cervical and first sacral vertebrae-thereby estimating the trunk flexion angle. The magnitude of this angle and its velocity have been linked to the development of occupational low back pain; thus the application provides ergonomists a more refined means of screening tasks beyond currently available survey tools.

Evaluation of spinal posture during gait with inertial measurement units

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt-twist method was adopted to evaluate flexion-extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.

Measuring Spinal Mobility Using an Inertial Measurement Unit System: A Validation Study in Axial Spondyloarthritis

Portable inertial measurement units (IMUs) are beginning to be used in human motion analysis. These devices can be useful for the evaluation of spinal mobility in individuals with axial spondyloarthritis (axSpA). The objectives of this study were to assess (a) concurrent criterion validity in individuals with axSpA by comparing spinal mobility measured by an IMU sensor-based system vs. optical motion capture as the reference standard; (b) discriminant validity comparing mobility with healthy volunteers; (c) construct validity by comparing mobility results with relevant outcome measures. A total of 70 participants with axSpA and 20 healthy controls were included. Individuals with axSpA completed function and activity questionnaires, and their mobility was measured using conventional metrology for axSpA, an optical motion capture system, and an IMU sensor-based system. The UCOASMI, a metrology index based on measures obtained by motion capture, and the IUCOASMI, the same index using IMU measures, were also calculated. Descriptive and inferential analyses were conducted to show the relationships between outcome measures. There was excellent agreement (ICC > 0.90) between both systems and a significant correlation between the IUCOASMI and conventional metrology (r = 0.91), activity (r = 0.40), function (r = 0.62), quality of life (r = 0.55) and structural change (r = 0.76). This study demonstrates the validity of an IMU system to evaluate spinal mobility in axSpA. These systems are more feasible than optical motion capture systems, and they could be useful in clinical practice.

A Narrative Review on Contemporary and Emerging Uses of Inertial Sensing in Occupational Ergonomics

Accurate, reliable, and cost-effective quantification of real-time biomechanical exposures in occupational settings remains an enduring pursuit in ergonomics. Miniaturized, wireless, body-worn inertial sensors offer opportunities to directly measure vast and personalized kinematics data in both laboratory and applied settings. This review investigated the contemporary and emerging uses of wearable inertial sensing technology in occupational ergonomics research related to biomechanical exposure assessment in physical work. A review and narrative synthesis of 78 peer-reviewed studies was conducted. A conceptual framework was used for scoping and synthesizing the reviewed scientific literature. Review findings help to contextualize contributions of this emerging technology to the broader goals of reducing work-relevant musculoskeletal trauma disorders. The review made evident that despite the growing interest in wearable inertial sensing technologies for ergonomics research, its use in applied settings still lags. The review also identified differences in sensor attachment locations and methods and measures for calibration and validation, and inconsistent criteria for reporting and assessing biomechanical exposures even across studies with similar objectives. Emerging applications include combining inertial sensing with predictive modeling for obtaining cumulative exposure data, and providing real-time feedback about biomechanical work demands. The manuscript concludes with research directions for enabling inertial sensing technologies as a tool for online biomechanical exposure assessment and feedback, which has particular appeal in non-repetitive work settings.

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.

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.

Trunk Posture Exposure Patterns among Prairie Ranch and Grain Farmers

Objectives: Low back disorders (LBD) are the most common musculoskeletal disorder among farmers, and awkward trunk postures such as repetitive bending are often cited as a contributor. However, it is not clear whether trends of increasing mechanization in agriculture may be impacting the requirement for trunk-intensive tasks. This study compared the patterns of working trunk posture among prairie farmers during both machine-intensive and non-intensive work days.Methods: Forty-nine adult farm workers from 22 farms participated in this study. Individual and farm characteristics were documented via questionnaire. Trunk angles and velocities were measured with an I2M inertial sensor placed on the chest. Participants completed electronic posture assessments during up to three regular work days throughout the growing season for a total of 91 electronic posture measurements. Forward and lateral trunk bending patterns were expressed in three domains: magnitude, duration, and frequency.Results: Working tasks were categorized into driving, manual, and mixed. Driving was the most commonly measured task (52% of work days), and mixed tasks the least (12%). Both 90th percentile trunk flexion-extension angles and velocities were significantly higher for manual as compared to driving tasks. Participants spent 38% of their working time in trunk forward flexion ≥ 20°, which, according to previous epidemiological studies, may increase their risk for LBD.Conclusion: The directly-measured trunk posture exposure patterns in this study suggest that machinery-intensive workdays result in less awkward trunk posture and lower velocities. Increasing mechanization invites more research on the exposures associated with machinery operation and increasing automation.

Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics

Load carriage induces systematic alterations in gait patterns and pelvic-thoracic coordination. Leveraging this information, the objective of this study was to develop and assess a statistical prediction algorithm that uses body-worn inertial sensor data for classifying load carrying modes and load levels. Nine men participated in an experiment carrying a hand load in four modes: one-handed right and left carry, and two-handed side and anterior carry, each at 50% and 75% of the participant's maximum acceptable weight of carry, and a no-load reference condition. Twelve gait parameters calculated from inertial sensor data for each gait cycle, including gait phase durations, torso and pelvis postural sway, and thoracic-pelvic coordination were used as predictors in a two-stage hierarchical random forest classification model with Bayesian inference. The model correctly classified 96.9% of the carrying modes and 93.1% of the load levels. Coronal thoracic-pelvic coordination and pelvis postural sway were the most relevant predictors although their relative importance differed between carrying mode and load level prediction models. This study presents an algorithmic framework for combining inertial sensing with statistical prediction with potential use for quantifying physical exposures from load carriage.

Radio frequency identification to measure the duration of machine-paced assembly tasks: Agreement with self-reported task duration and application in variance components analyses of upper arm postures and movements recorded over multiple days

Technical advances in inertial measurement units (IMUs) with data logging functionality have enabled multi-day collection of fullshift upper arm postures and movements. Such data are useful for characterizing job-level exposures and, when coupled with task-level information, can inform interventions to mitigate high-exposure tasks. Previously reported methods for capturing task-level information, however, were limited primarily to self-report diaries or direct observation. In this study of machine-paced manufacturing workers (n=6), a low-cost radio frequency identification (RFID) system was used to collect information about when, and for how long, specific assembly tasks were performed during up to 14 consecutive work shifts (76 total work shifts across the six participants). The RFID data were compared to information collected with a self-report diary using Bland-Altman analyses. In addition, the RFID data were paired with IMU data to identify task-level exposures from within full-shift recordings of upper arm postures and movements. These data were then used to estimate the relative contributions of between- and within-worker sources of variance to overall variance in posture and movement summary measures using hierarchical random-effects analysis of variance (ANOVA) techniques. Average estimates of daily task duration based on RFID data were comparable to estimates obtained by self-report (mean bias < ±1 minute) but with substantial variability (limits of agreement > ±100 minutes). In addition, the ANOVA models containing task-level information suggested a substantial amount of the overall exposure variance was attributed to repeated observations of the same task within a work day. These findings (i) suggest that while the RFID system used in this study performed adequately, further refinement, validation, and/or alternative strategies may be needed and (ii) underscore the importance of repeated full-shift and task-based measurement approaches in characterizing physical exposures, even in machine-paced environments.

Accuracy of angular displacements and velocities from inertial-based inclinometers

The objective of this study was to evaluate the accuracy of various sensor fusion algorithms for measuring upper arm elevation relative to gravity (i.e., angular displacement and velocity summary measures) across different motion speeds. Thirteen participants completed a cyclic, short duration, arm-intensive work task that involved transfering wooden dowels at three work rates (slow, medium, fast). Angular displacement and velocity measurements of upper arm elevation were simultaneously measured using an inertial measurement unit (IMU) and an optical motion capture (OMC) system. Results indicated that IMU-based inclinometer solutions can reduce root-mean-square errors in comparison to accelerometer-based inclination estimates by as much as 87%, depending on the work rate and sensor fusion approach applied. The findings suggest that IMU-based inclinometers can substantially improve inclinometer accuracy in comparison to traditional accelerometer-based inclinometers. Ergonomists may use the non-proprietary sensor fusion algorithms provided here to more accurately estimate upper arm elevation.

An evaluation of wearable sensors and their placements for analyzing construction worker's trunk posture in laboratory conditions

This study investigates the effect of sensor placement on the analysis of trunk posture for construction activities using two off-the-shelf systems. Experiments were performed using a single-parameter monitoring wearable sensor (SPMWS), the ActiGraph GT9X Link, which was worn at six locations on the body, and a multi-parameter monitoring wearable sensor (MPMWS), the Zephyr BioHarness™3, which was worn at two body positions. One healthy male was recruited and conducted 10 experiment sessions to repeat measurements of trunk posture within our study. Measurements of upper-body thoracic bending posture during the lifting and lowering of raised deck materials in a laboratory setting were compared against video-captured observations of posture. The measurements from the two sensors were found to be in agreement during slow-motion symmetric bending activities with a target bending of ≤45°. However, for asymmetric bending tasks, when the SPMWS was placed on the chest, its readings were substantially different from those of the MPMWS worn on the chest or under the armpit.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications.

Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study

This paper, presents an inertial and magnetic sensor based technological platform, intended for articular amplitude monitoring and telerehabilitation processes considering an efficient cost/technical considerations compromise. The particularities of our platform offer possibilities of a high social impact by making telerehabilitation accessible to large population sectors in marginal socio-economic sectors, especially in underdeveloped countries where, in contrast to developed countries, specialists are scarce and high technology is not available or inexistent. This platform integrates high resolution low cost inertial and magnetic sensors with adequate user interfaces and communication protocols to perform a diagnostic service through the web, or other available communication networks. Elbow amplitude information is generated by sensors and then transferred to a computing device with adequate interfaces to make it accessible to inexperienced personnel, providing a high social value at a low cost. Experimental methodology includes two different sets of tests: the first one uses flexion - extension movements on a robotic arm to validate our platform (IMOCAP) articular amplitude measurements, against the robotic positioning system. The second set of tests was carried out on human patients to test IMOCAP in real operational conditions; results were validated with an optical positioning system. This paper presents experimental results showing the platform applicability to telerehabilitation processes.

Working postures and physical activity among registered nurses

Nurses report a high prevalence of musculoskeletal discomfort, particularly of the low back and neck/shoulder. This study characterized the full-shift upper arm and trunk postures and movement velocities of registered nurses using inertial measurement units (IMUs). Intensity of occupational physical activity (PA) was also ascertained using a waist-worn PA monitor and using the raw acceleration data from each IMU. Results indicated that nurses spent a relatively small proportion of their work time with the arms or trunk in extreme postures, but had few opportunities for rest and recovery in comparison to several other occupational groups. Comparisons between nurses in different PA groups suggested that using a combination of accelerometers secured to several body locations may provide more representative estimates of physical demands than a single, waist-worn PA monitor. The findings indicate a need for continued field-based research with larger sample sizes to facilitate the development of maximally effective intervention strategies.

Toward Unobtrusive Patient Handling Activity Recognition for Injury Reduction Among At-Risk Caregivers

Nurses regularly perform patient handling activities. These activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling activities is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is based on human observational approach, which is neither accurate nor projectable to a large population. In this paper, we aim at addressing these challenges. Our solution comprises a smart wearable device and a novel spatio-temporal warping (STW) pattern recognition framework. The wearable device, named Smart Insole 2.0, is equipped with a rich set of sensors and can provide an unobtrusive way to automatically capture the information of patient handling activities. The STW pattern recognition framework fully exploits the spatial and temporal characteristics of plantar pressure by calculating a novel warped spatio-temporal distance, to quantify the similarity for the purpose of activity recognition. To validate the effectiveness of our framework, we perform a pilot study with eight subjects, including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 91.7%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.3% and 92.5%, respectively.

Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies

The accuracy and repeatability of an inertial measurement unit (IMU) system for directly measuring trunk angular displacement and upper arm elevation were evaluated over eight hours (i) in comparison to a gold standard, optical motion capture (OMC) system in a laboratory setting, and (ii) during a field-based assessment of dairy parlour work. Sample-to-sample root mean square differences between the IMU and OMC system ranged from 4.1° to 6.6° for the trunk and 7.2°-12.1° for the upper arm depending on the processing method. Estimates of mean angular displacement and angular displacement variation (difference between the 90th and 10th percentiles of angular displacement) were observed to change <4.5° on average in the laboratory and <1.5° on average in the field per eight hours of data collection. Results suggest the IMU system may serve as an acceptable instrument for directly measuring trunk and upper arm postures in field-based occupational exposure assessment studies with long sampling durations. Practitioner Summary: Few studies have evaluated inertial measurement unit (IMU) systems in the field or over long sampling durations. Results of this study indicate that the IMU system evaluated has reasonably good accuracy and repeatability for use in a field setting over a long sampling duration.

Evaluation of four sensor locations for physical activity assessment

Direct measurements of physical activity (PA) obtained with inertial measurement units (IMUs) secured to the upper arms and trunk of 36 registered nurses working a full shift were compared to measurements obtained with a commercially-available PA monitor (ActiGraph wGT3X-BT) worn at the waist. Raw accelerations from each device were summarized into PA counts/min and metabolic equivalent (METs) categories using standard definitions. Differences between measurements were examined using repeated measures one-way analyses of variance (ANOVA) and agreement was assessed using Bland-Altman plots. Statistically significant differences were observed between all sensor locations for all PA summary metrics except for between the left and right arm for percentages of work time in the light and moderate counts/min categories. Bland-Altman plots suggested limited agreement between measurements obtained with the IMUs and measurements obtained with the wGT3X-BT waist-worn PA monitor. Results indicate that PA measurements vary substantially based on sensor location.