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.

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.

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 âˆ¼ 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.

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.

A Systematic Review on Evaluation Strategies for Field Assessment of Upper-Body Industrial Exoskeletons: Current Practices and Future Trends.

With rising manual work demands, physical assistance at the workplace is crucial, wherein the use of industrial exoskeletons (i-EXOs) could be advantageous. However, outcomes of numerous laboratory studies may not be directly translated to field environments. To explore this discrepancy, we conducted a systematic review including 31 studies to identify and compare the approaches, techniques, and outcomes within field assessments of shoulder and back support i-EXOs. Findings revealed that the subjective approaches [i.e., discomfort (23), usability (22), acceptance/perspectives (21), risk of injury (8), posture (3), perceived workload (2)] were reported more common (27) compared to objective (15) approaches [muscular demand (14), kinematics (8), metabolic costs (5)]. High variability was also observed in the experimental methodologies, including control over activity, task physics/duration, sample size, and reported metrics/measures. In the current study, the detailed approaches, their subject-related factors, and observed trends have been discussed. In sum, a new guideline, including tools/technologies has been proposed that could be utilized for field evaluation of i-EXOs. Lastly, we discussed some of the common technical challenges experimenters face in evaluating i-EXOs in field environments. Efforts presented in this study seek to improve the generalizability in testing and implementing i-EXOs.

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.

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.

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.

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today's clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.

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.

Sharif-Human movement instrumentation system (SHARIF-HMIS): Development and validation.

The interest in wearable systems among the biomedical engineering and clinical community continues to escalate as technical refinements enhance their potential use for both indoor and outdoor applications. For example, an important wearable technology known as a microelectromechanical system (MEMS) is demonstrating promising applications in the area of biomedical engineering. Accordingly, this study was designed to investigate the Sharif-Human Movement Instrumentation System (SHARIF-HMIS), consisting of inertial measurement units (IMUs), stretchable clothing, and a data logger-all of which can be used outside the controlled environment of a laboratory, thus enhancing its overall utility. This system is lightweight, portable, able to be deliver data for almost 10 h, and features a new data-fusion algorithm using the Kalman filter with an adaptive approach. In specific terms, the data from the system's gyroscope, accelerometer, and magnetometer sensors can be combined to estimate total-body orientation; additionally, the noise level of these sensors can be changed to accommodate faster motions as well as magnetic disturbances. These variations can be incorporated within the extended Kalman filter by changing the parameters of the filter adaptively. In specific terms, the system's interface was developed to acquire data from eighteen IMUs located on the body to collect kinematic data associated with human motion. Meanwhile, a validation test involving one subject performing different shoulder motions was designed to compare data captured by SHARIF-HMIS and the VICON motion-capture system. This validation test demonstrated correlation values of >0.9. Results also confirmed that the output accuracy of the new system's sensor was <0.55, 1.5 and 3.5° for roll, pitch, and yaw directions, respectively. In summary, SHARIF-HMIS successfully collected kinematic data for specific human movements, which has promising implications for a range of sporting, biomedical, and healthcare-related applications.

Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part II - "Unexpected" effects on shoulder motion, balance, and spine loading.

Adopting a new technology (exoskeletal vest designed to support overhead work) in the workplace can be challenging since the technology may pose unexpected safety and health consequences. A prototype exoskeletal vest was evaluated for potential unexpected consequences with a set of evaluation tests for: usability (especially, donning & doffing), shoulder range of motion (ROM), postural control, slip & trip risks, and spine loading during overhead work simulations. Donning/doffing the vest was easily done by a wearer alone. The vest reduced the max. shoulder abduction ROM by ∼10%, and increased the mean center of pressure velocity in the anteroposterior direction by ∼12%. However, vest use had minimal influences on trip-/slip-related fall risks during level walking, and significantly reduced spine loadings (up to ∼30%) especially during the drilling task. Use of an exoskeletal vest can be beneficial, yet the current evaluation tests should be expanded for more comprehensiveness, to enable the safe adoption of the technology.

Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part I - "Expected" effects on discomfort, shoulder muscle activity, and work task performance.

Use of exoskeletal vests (designed to support overhead work) can be an effective intervention approach for tasks involving arm elevation, yet little is known on the potential beneficial impacts of their use on physical demands and task performance. This laboratory study (n = 12) evaluated the effects of a prototype exoskeletal vest during simulated repetitive overhead drilling and light assembly tasks. Anticipated or expected benefits were assessed, in terms of perceived discomfort, shoulder muscle activity, and task performance. Using the exoskeletal vest did not substantially influence perceived discomfort, but did decrease normalized shoulder muscle activity levels (e.g., ≤ 45% reduction in peak activity). Drilling task completion time decreased by nearly 20% with the vest, but the number of errors increased. Overall, exoskeletal vest use has the potential to be a new intervention for work requiring arm elevation; however, additional investigations are needed regarding potential unexpected or adverse influences (see Part II).