Unlocking human-robot synergy: The power of intent communication in warehouse robotics

As autonomous mobile robots (AMR) are introduced into workspace environments shared with people, effective human-robot communication is critical to the prevention of injury while maintaining a high level of productivity. This research presents an empirical study that evaluates four alternative methods for communicating between an autonomous mobile robot and a human at a warehouse intersection. The results demonstrate that using an intent communication system for human-AMR interaction improves objective measures of productivity (task time) and subjective metrics of trust and comfort.

Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors

Clinical tests like Timed Up and Go (TUG) facilitate the assessment of post-stroke mobility, but they lack detailed measures. In this study, 21 stroke survivors and 20 control participants underwent TUG, sit-to-stand (STS), and the 10 Meter Walk Test (10MWT). Tests incorporated single tasks (STs) and motor-cognitive dual-task (DTs) involving reverse counting from 200 in decrements of 10. Eight wearable motion sensors were placed on feet, shanks, thighs, sacrum, and sternum to record kinematic data. These data were analyzed to investigate the effects of stroke and DT conditions on the extracted features across segmented portions of the tests. The findings showed that stroke survivors (SS) took 23% longer for total TUG (p < 0.001), with 31% longer turn time (p = 0.035). TUG time increased by 20% (p < 0.001) from STs to DTs. In DTs, turning time increased by 31% (p = 0.005). Specifically, SS showed 20% lower trunk angular velocity in sit-to-stand (p = 0.003), 21% longer 10-Meter Walk time (p = 0.010), and 18% slower gait speed (p = 0.012). As expected, turning was especially challenging and worsened with divided attention. The outcomes of our study demonstrate the benefits of instrumented clinical tests and DTs in effectively identifying motor deficits post-stroke across sitting, standing, walking, and turning activities, thereby indicating that quantitative motion analysis can optimize rehabilitation procedures.

A machine learning model to predict heart failure readmission: toward optimal feature set

Background

Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance.

Objective

This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model.

Material and methods

Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC.

Results

A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters.

Conclusion

The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

BACKGROUND

Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy.

OBJECTIVE

Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols.

METHODS

21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models-Support Vector Machine, Logistic Regression, and Random Forest-were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated.

RESULTS

The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk.

CONCLUSION

Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.

Alterations in Physical Demands During Virtual/Augmented Reality-Based Tasks: A Systematic Review

The digital world has recently experienced a swift rise in worldwide popularity due to Virtual (VR) and Augmented Reality (AR) devices. However, concrete evidence about the effects of VR/AR devices on the physical workload imposed on the human body is lacking. We reviewed 27 articles that evaluated the physical impact of VR/AR-based tasks on the users using biomechanical sensing equipment and subjective tools. Findings revealed that movement and muscle demands (neck and shoulder) varied in seven and five studies while using VR, while in four and three studies during AR use, respectively, compared to traditional methods. User discomfort was also found in seven VR and three AR studies. Outcomes indicate that interface and interaction design, precisely target locations (gestures, viewing), design of virtual elements, and device type (location of CG as in Head-Mounted Displays) influence these alterations in neck and shoulder regions. Recommendations based on the review include developing comfortable reach envelopes for gestures, improving wearability, and studying temporal effects of repetitive movements (such as effects on fatigue and stability). Finally, a guideline is provided to assist researchers in conducting effective evaluations. The presented findings from this review could benefit designers/evaluations working towards developing more effective VR/AR products.

A Systematic Review on Lower-Limb Industrial Exoskeletons: Evaluation Methods, Evidence, and Future Directions

Industrial tasks that involve frequent sitting/standing transitions and squatting activities can benefit from lower-limb industrial exoskeletons; however, their use is not as widespread as their upper-body counterparts. In this review, we examined 23 articles that evaluated the effects of using Wearable Chair (WC) and Squat-assist (SA) exoskeletons. Evaluations mainly included assessment of muscular demands in the thigh, shank, and upper/lower back regions. Both types of devices were found to lessen muscular demands in the lower body by 30-90%. WCs also reduced low-back demands (~ 37%) and plantar pressure (54-80%) but caused discomfort/unsafe feeling in participants. To generalize outcomes, we suggest standardizing approaches used for evaluating the devices. Along with addressing low adoption through design upgrades (e.g., ground and body supports/attachments), we recommend that researchers thoroughly evaluate temporal effects on muscle fatigue, metabolic rate, and stability of wearers. Although lower-limb exoskeletons were found to be beneficial, discrepancies in experimental protocols (posture/task/measures) were discovered. We also suggest simulating more realistic conditions, such as walking/sitting interchangeability for WCs and lifting loads for SA devices. The presented outcomes could help improve the design/evaluation approaches, and implementation of lower limb wearable devices across industries.

Recovering from Laboratory-Induced slips and trips causes high levels of lumbar muscle activity and spine loading

Slips, trips, and falls are some of the most substantial and prevalent causes of occupational injuries and fatalities, and these events may contribute to low-back problems. We quantified lumbar kinematics (i.e., lumbar angles relative to pelvis) and kinetics during unexpected slip and trip perturbations, and during normal walking, among 12 participants (6F, 6 M). Individual anthropometry, lumbar muscle geometry, and lumbar angles, along with electromyography from 14 lumbar muscles were used as input to a 3D, dynamic, EMG-based model of the lumbar spine. Results indicated that, in comparison with values during normal walking, lumbar range of motion, lumbosacral (L5/S1) loads, and lumbar muscle activations were all significantly higher during the slip and trip events. Maximum L5/S1 compression forces exceeded 2700 N during slip and trip events, compared with ∼ 1100 N during normal walking. Mean values of L5/S1 anteroposterior (930 N), and lateral (800 N) shear forces were also substantially larger than the shear force during the normal walking (230 N). These observed levels of L5/S1 reaction forces, along with high levels of bilateral lumbar muscle activities, suggest the potential for overexertion injuries and tissue damage during unexpected slip and trip events, which could contribute to low back injuries. Outcomes of this study may facilitate the identification and control of specific mechanisms involved with low back disorders consequent to slips or trips.

Why multi-tier surgical instrument table matters? An ergonomic analysis from mento-physical demand perspectives

Using traditional back tables (BT) in operating rooms (OR) can lead to high physical/cognitive demand on nurses due to repetitive manual material handling activities. A multi-tier table (MTT) has been developed to relieve such stressors by providing extra working surfaces to avoid stacking the instrument trays and facilitate access to surgical tools. In this study, sixteen participants performed lifting/lowering and instrument findings tasks on each table, where kinematics, kinetics, subjective, and performance-related measures were recorded. Results indicated that MTT required lesser shoulder flexion (p-value<0.001), ∼14% lower shoulder loads (0.012), task completion time (<0.001), and cognitive/physical workloads (<0.004). Although peak low-back demands were ∼15% higher using MTT, the number of lifts to complete the same task was 60% lower, leading to lower cumulative demand on the low-back musculature. Utilizing MTT in OR could reduce demand and increase nurses' efficiency, leading to reduced risk of WMSDs and the total time of surgery.

A Systematic Review of Fall Risk Factors in Stroke Survivors: Towards Improved Assessment Platforms and Protocols

Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research.

Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature.

Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995–2020. The results confirmed that the most common FRFs were age (21/27, i.e., considered in 21 out of 27 studies), gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance (i.e., 84% or 16/19). Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures (e.g., 10-m walk), and most recently by quantitative measures based on detailed motion analysis.

Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs.

Abstract TP61: Can A Single Motion Sensor Identify Lower Limb Movement Alterations Among Stroke Survivors?

Introduction: Hindrance in normal movement is common among stroke survivors (SS), leading to a larger prevalence of falling and serious injuries. Current methods for analyzing the movement restrictions (camera-based motion capture) are limited to laboratory/clinical environments. Meanwhile, the presence of Inertial Measurement Unit (IMU) sensors in our everyday lives (e.g., in smartphones) is ever increasing due to their portability, affordability, and accessibility. The purpose of this study was to determine whether a single IMU sensor can differentiate movements between SS and healthy control (HC).

Methods: Turning (360°) was selected as the activity since it is a common and complex motion. Data was collected from 12 participants (6 SS, 6 HC) using an IMU sensor (Xsens®) placed on unaffected leg (shank) and was analyzed using a 3-step process: filtration (5 Hz cutoff frequency), segmentation (cycles – consecutive foot placement), and feature extraction. Analyzed features included duration of task (sec), number of cycles, mean angular velocity (deg/sec) of shank per cycle, ratio of stance-time to the duration of task (%), and range of flexion angle (deg) for shank.

Results: Obtained values were larger in SS for number of cycles (SS: 5.8, HC: 3.2, p-value<0.01) and duration of task (7.8, 3.4, <0.01). Meanwhile, HC showed higher velocity of shank (unaffected) flexion (1.2, 1.7, <0.01) and the ratio of stance-time to the duration of task (57.7, 32.3, <0.01).

Conclusion: A single IMU sensor successfully detected kinematic disparities between two groups. Meanwhile, further work is required to examine the robustness and detection improvements upon using other sensor locations. The use of a single IMU sensor, also present in most smartphones/smartwatches, could enable this accessible technology to be used as a remote assessment/monitoring tool, extending the reach of diagnostic tools and rehabilitation programs.

Kinematic Analysis of 360° Turning in Stroke Survivors Using Wearable Motion Sensors

Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. Using inertial sensors to record the digital biomarkers during turning could reveal the relevant turning alterations. Objectives: In this study, movement alterations in stroke survivors (SS) were studied and compared to healthy individuals (HI) in the entire turning task due to its requirement of synergistic application of multiple bodily systems. Methods: The motion of 28 participants (14 SS, 14 HI) during turning was captured using a set of four Inertial Measurement Units, placed on their sternum, sacrum, and both shanks. The motion signals were segmented using the temporal and spatial segmentation of the data from the leading and trailing shanks. Several kinematic parameters, including the range of motion and angular velocity of the four body segments, turning time, the number of cycles involved in the turning task, and portion of the stance phase while turning, were extracted for each participant. Results: The results of temporal processing of the data and comparison between the SS and HI showed that SS had more cycles involved in turning, turn duration, stance phase, range of motion in flexion–extension, and lateral bending for sternum and sacrum (p-value < 0.035). However, HI exhibited larger angular velocity in flexion–extension for all four segments. The results of the spatial processing, in agreement with the prior method, showed no difference between the range of motion in flexion–extension of both shanks (p-value > 0.08). However, it revealed that the angular velocity of the shanks of leading and trailing legs in the direction of turn was more extensive in the HI (p-value < 0.01). Conclusions: The changes in upper/lower body segments of SS could be adequately identified and quantified by IMU sensors. The identified kinematic changes in SS, such as the lower flexion–extension angular velocity of the four body segments and larger lateral bending range of motion in sternum and sacrum compared to HI in turning, could be due to the lack of proper core stability and effect of turning on vestibular system of the participants. This research could facilitate the development of a targeted and efficient rehabilitation program focusing on the affected aspects of turning movement for the stroke community.

Kinematic Assessment of Turning and Walking Tasks Among Stroke Survivors by Employing Wearable Sensors and Pressure Platform

Stroke survivors often experience reduced movement capabilities due to alterations in their neuromusculoskeletal systems. Modern sensor technologies and motion analyses can facilitate the determination of these changes. Our work aims to assess the potential of using wearable motion sensors to analyze the movement of stroke survivors and identifying the affected functions. We recruited 10 participants (5 stroke survivors, 5 healthy individuals) and conducted a controlled laboratory evaluation for two of the most common daily activities: turning and walking. Among the extracted kinematic parameters, range of trunk and sacrum lateral bending in turning were significantly larger in stroke survivors (p-value<0.02). However, no statistical difference in mean angular velocity and range of motion for trunk/sacrum/shank flexion-extension were obtained in the turning task. Our results also indicated that during walking, while there was no difference in swing time, double support portion of gait among the stroke group was significantly larger (p-value = 0.001). Outcomes of this investigation may help in designing new rehabilitation programs for stroke and other neurological disorders and/or in improving the efficacy of such programs.Clinical Relevance- This study may provide a better insight on the detailed functional differences between stroke survivors and healthy individuals which in turn could be used to develop a more efficient rehabilitation program for stroke community.

Smartphone-based human fatigue level detection using machine learning approaches

Human muscle fatigue is the main result of diminishing muscle capability, leading to reduced performance and increased risk of falls and injury. This study provides a classification model to identify the human fatigue level based on the motion signals collected by a smartphone. 24 participants were recruited and performed the fatiguing exercise (i.e. squatting). Upon completing each set of squatting, they walked for a fixed distance while the smartphone attached to their right shank and the gait data were associated with the Borg's Rating of Perceived Exertion (i.e. data label). Our machine-learning model of two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue reached the accuracy of 91, 78, and 64%, respectively. The outcomes of this study may facilitate the accessibility of a fatigue-monitoring tool in the workplace, which improves the workers' performance and reduce the risk of falls and injury. Practitioner Summary: This study aimed to develop a machine-learning model to identify human fatigue level using motion data captured by a smartphone attached to the shank. Our results can facilitate the development of an accessible fatigue-monitoring system that may improve the workers' performance and reduce the risk of falls and injury. Abbreviations: WMSD: work-related musculoskeletal disorders; IMU: inertial measurement unit; RPE: rating of perceived exertion; SVM: support vector machine; IRB: institutional review board; SOM: self-organizing map; LDA: linear discriminant analysis; PCA: principal component analysis; FT: fourier transformation; RBF: radial basis function; CUSUM: cumulative sum; ROM: range of motion; MVC: maximum voluntary contractions.

Designing a New Forklift Backrest: Role of Adjustability in Improving Operator Comfort

Selection of a single design to delight customers may not be always possible due to the anthropometric differences in humans, wherein a hybrid design can benefit. Using adjustability, we demonstrate our approach for developing a novel forklift backrest to accommodate drivers with a wide range of body sizes. Field and laboratory evaluations were conducted to assess and improve the design. Our results indicated that the new design could provide improved comfort for longer durations. This study reveals the possibilities for human factors professionals to consider adjustability in vehicle operator compartment interiors, especially backrests and seating, of similar industrial vehicles.

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

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

Investigating the effect of fatigue on muscle microvasculature blood flow during intermittent isometric contraction

Localized muscle fatigue (LMF) decreases muscular strength, while affects the performance and potentially increases the risk of musculoskeletal disorders (MSD). An important mechanism in recovering from muscle fatigue is blood flow (BF). The BF response to muscle contraction and fatigue is highly dynamic and difficult to predict, as it depends on both metabolic demand and intramuscular pressure. The aim of this study was to measure both fatigue and BF during intermittent exertion of the first dorsal interosseous (FDI) muscle, in order to better characterize the relationship between BF and LMF during muscle contraction and rest. This study utilized Diffuse Correlation Spectroscopy (DCS) for BF measurement within the microvasculature of the FDI muscle. Exertion levels (EL) for intermittent fatiguing contraction were set to 20%, 30%, and 40% of an individual's maximum voluntary contraction (MVC). Our results showed that as an individual fatigued, relative BF rates increased, on average, by ~66% during exertion periods and ~330% during rest periods. Differences between exerting and resting BF increased over time for every EL (p<0.04), increasing by up to 11 times the baseline BF. At the same levels of muscle capacity (%MVC), resting BF was also found to increase with EL consistently. Our findings highlight BF dependence on both EL and history of muscle contraction. These results imply a variable recovery rate based on both the current state of contraction, (i.e., exertion vs. rest), and the muscle contraction history. The outcome of our study may facilitate the estimation of BF, thus, the muscle recovery rate, which can be implemented in the fatigue models to improve the prediction of muscle capacity to generate force/power.

A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart "sensor-based" practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today's smartphones, as objective tools for various health applications towards personalized precision medicine.

The influence of mechanical vibration on local and central balance control

Fall prevention has an indispensable role in enhancing life expectancy and quality of life among older adults. The first step to prevent falls is to devise reliable methods to identify individuals at high fall risk. The purpose of the current study was to assess alterations in local postural muscle and central sensory balance control mechanisms due to low-frequency externally applied vibration among elders at high fall risk, in comparison with healthy controls, as a potential tool for assessing fall risk. Three groups of participants were recruited: healthy young (n = 10; age = 23 ± 2 years), healthy elders (n = 10; age = 73 ± 3 years), and elders at high fall risk (n = 10; age = 84 ± 9 years). Eyes-open and eyes-closed upright standing balance performance was measured with no vibration, 30 Hz, and 40 Hz vibration of Gastrocnemius muscles. When vibratory stimulation was applied, changes in local-control performance manifested significant differences among the groups (p < 0.01). On average between conditions, we observed 97% and 92% less change among high fall risk participants when compared to healthy young and older adults, respectively. On the other hand, vibration-induced changes in the central-control performance were not significant between groups (p ≥ 0.19). Results suggest that local-control deficits are responsible for balance behavior alterations among elders at high fall risk and healthy individuals. This observation may be attributable to deterioration of short-latency reflexive loop in elders at high fall risk. On the other hand, we could not ascribe the balance alterations to problems related to central nervous system performance or long-latency responses.

Impact of task design on task performance and injury risk: case study of a simulated drilling task

Existing evidence is limited regarding the influence of task design on performance and ergonomic risk, or the association between these two outcomes. In a controlled experiment, we constructed a mock fuselage to simulate a drilling task common in aircraft manufacturing, and examined the effect of three levels of workstation adjustability on performance as measured by productivity (e.g. fuselage completion time) and quality (e.g. fuselage defective holes), and ergonomic risk as quantified using two common methods (rapid upper limb assessment and the strain index). The primary finding was that both productivity and quality significantly improved with increased adjustability, yet this occurred only when that adjustability succeeded in reducing ergonomic risk. Supporting the inverse association between ergonomic risk and performance, the condition with highest adjustability created the lowest ergonomic risk and the best performance while there was not a substantial difference in ergonomic risk between the other two conditions, in which performance was also comparable. Practitioner Summary: Findings of this study supported a causal relationship between task design and both ergonomic risk and performance, and that ergonomic risk and performance are inversely associated. While future work is needed under more realistic conditions and a broader population, these results may be useful for task (re)design and to help cost-justify some ergonomic interventions.

The effect of parameters of equilibrium-based 3-D biomechanical models on extracted muscle synergies during isometric lumbar exertion

A hallmark of more advanced models is their higher details of trunk muscles represented by a larger number of muscles. The question is if in reality we control these muscles individually as independent agents or we control groups of them called "synergy". To address this, we employed a 3-D biomechanical model of the spine with 18 trunk muscles that satisfied equilibrium conditions at L4/5, with different cost functions. The solutions of several 2-D and 3-D tasks were arranged in a data matrix and the synergies were computed by using non-negative matrix factorization (NMF) algorithms. Variance accounted for (VAF) was used to evaluate the number of synergies that emerged by the analysis, which were used to reconstruct the original muscle activations. It was showed that four and six muscle synergies were adequate to reconstruct the input data of 2-D and 3-D torque space analysis. The synergies were different by choosing alternative cost functions as expected. The constraints affected the extracted muscle synergies, particularly muscles that participated in more than one functional tasks were influenced substantially. The compositions of extracted muscle synergies were in agreement with experimental studies on healthy participants. The following computational methods show that the synergies can reduce the complexity of load distributions and allow reduced dimensional space to be used in clinical settings.

Mathematical Models of Localized Muscle Fatigue: Sensitivity Analysis and Assessment of Two Occupationally-Relevant Models

Muscle fatigue models (MFM) have broad potential application if they can accurately predict muscle capacity and/or endurance time during the execution of diverse tasks. As an initial step toward facilitating improved MFMs, we assessed the sensitivity of selected existing models to their inherent parameters, specifically that model the fatigue and recovery processes, and the accuracy of model predictions. These evaluations were completed for both prolonged and intermittent isometric contractions, and were based on model predictions of endurance times. Based on a recent review of the literature, four MFMs were initially chosen, from which a preliminary assessment led to two of these being considered for more comprehensive evaluation. Both models had a higher sensitivity to their fatigue parameter. Predictions of both models were also more sensitive to the alteration of their parameters in conditions involving lower to moderate levels of effort, though such conditions may be of most practical, contemporary interest or relevance. Although both models yielded accurate predictions of endurance times during prolonged contractions, their predictive ability was inferior for more complex (intermittent) conditions. When optimizing model parameters for different loading conditions, the recovery parameter showed considerably larger variability, which might be related to the inability of these MFMs in simulating the recovery process under different loading conditions. It is argued that such models may benefit in future work from improving their representation of recovery process, particularly how this process differs across loading conditions.

Ergonomic evaluation of a wearable assistive device for overhead work

Overhead work is an important risk factor for upper extremity (UE) musculoskeletal disorders. We examined the potential of a mechanical arm and an exoskeletal vest as a wearable assistive device (WADE) for overhead work. Twelve participants completed 10 minutes of simulated, intermittent overhead work, using each of three payloads (1.1, 3.4 and 8.1 kg) and with/without the WADE. Ratings of perceived discomfort (RPDs) and electromyography (EMG) were obtained for the upper arms, shoulders and low back. Using the WADE, UE RPDs decreased by ∼50% with the heavier payloads, whereas smaller (∼25%) and non-significant increases in low-back RPDs were found and were relatively independent of payload. Changes in RPDs with WADE use were consistent with physical demands indicated by EMG, though EMG-based differences in fatigue were less apparent. Participants generally preferred using the WADE, particularly with heavier payloads. These results supported the potential utility of a WADE as an intervention for overhead work.

Investigating the effects of slipping on lumbar muscle activity, kinematics, and kinetics

Slips, trips, and falls remain leading causes of occupational injuries and fatalities. The current exploratory study quantified lumbar kinematics and kinetics during both induced slips and normal walking. Individual anthropometry, lumbar muscle geometry, and lumbar kinematics, along with electromyography of 14 lumbar muscles were used as input to a 3D, dynamic, EMG-based model of the lumbar spine. Results indicated that, in comparison with values during normal walking, lumbar kinematics, lumbosacral kinetics, lumbar muscle activations, and lumbosacral reaction forces were all substantially increased during a slip event. Observed levels of muscle activity and lumbosacral reaction forces suggest the potential for low back injury during a slip event. Outcomes of this work may facilitate the identification and control of specific mechanisms involved with low back disorders consequent to a slip.

Design and development of a hand robotic rehabilitation device for post stroke patients

Robot-mediated rehabilitation is a rapidly advancing discipline that seeks to develop improved treatment procedures using new technologies, e.g., robotics, coupled with modern theories in neuroscience and rehabilitation. A robotic device was designed and developed for rehabilitation of upper limbs of post stroke patients. A novel force feedback bimanual working mode provided real-time dynamic sensation of the paretic hand. Results of the preliminary clinical tests revealed a quantitative evaluation of the patient's level of paresis and disability.

How does the central nervous system address the kinetic redundancy in the lumbar spine? Three-dimensional isometric exertions with 18 Hill-model-based muscle fascicles at the L4—L5 level

The human motor system is organized for execution of various motor tasks in a different and flexible manner. The kinetic redundancy in the human musculoskeletal system is a significant property by which the central nervous system achieves many complementary goals. An equilibrium-based biomechanical model of isometric three-dimensional exertions of trunk muscles has been developed. Following the definition and role of the uncontrolled manifold, the kinetic redundancy concept is explored in mathematical terms. The null space of the kinetically redundant system when a certain joint moment and/or stiffness are needed is derived and discussed. The aforementioned concepts have been illustrated, using a three-dimensional three-degrees-of-freedom biomechanical model of the spine with 18 anatomically oriented Hill-type-model muscle fascicles. The considerations of stability and its consequence on the internal loading of the spine and coactivation consequences are discussed in both general and specific cases. The results can shed light on the interaction mechanisms in muscle activation patterns seen in various tasks and exertions and can provide a significant understanding for future research studies and clinical practices related to low-back disorders. Alteration of recruitment patterns in low-back-pain patients has been explained on the basis of this biomechanical analysis. The higher coactivation results in higher internal loading while providing higher joint stiffness that enhances spinal stability, which guards against spinal deformation in the presence of any perturbations.