A deep learning approach for lower back-pain risk prediction during manual lifting

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.Comment: 21 pages, 10 figure

Risk Assessment Methods of Low Back Pain among Masonry Apprentice

Work-related musculoskeletal disorders (WMSDs) are primary cause of non-fatal injuries in construction. They involve instant or persistent stress on a worker's body (muscles, tendons, ligaments, bones) that may affect a worker's ability to perform his work or even cause chronic disability. This review helps the construction sectors in better understanding the intensity of WMSDs and the risks associated with them. This paper provides a layout for research community with a comprehensive overview of existing technique, their drawbacks, and the need for more study in order to achieve automated evaluations on construction sites. Despite the fact that assessing vulnerability to WMsSD risk factors has proven to be possible in order to reduce the rate of this injury, the area remains undeveloped due to a lack of awareness among professionals about the facilitating techniques, as well as their efficiency and limitations. This paper examines the current WMSD risk evaluation methods and outlines their convenience and disadvantages. This study helps the construction sector in better understanding the extremity of WMSDs and the risks associated with them. This review imparts the researchers with an integrated view of available methods, their drawbacks, and the need for study in order to achieve automated evaluations on construction sites

Hum Factors

Objective:A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting.Background:Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments.Methods:Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames.Results:The mean absolute difference between predicted and motion capture measured trunk angles was 14.65\ub0, and there was a significant linear relationship between predicted and measured trunk angles (R2 = 0.80, p < 0.001). The training error for the kinematics model was 2.34\ub0.Conclusion:Using simple computer vision extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision.Application:This computer vision method may be implemented on hand-held devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.R01OH011024/ACL/ACL HHSUnited States/R01 OH011024/OH/NIOSH CDC HHSUnited States/T42OH008434/ACL/ACL HHSUnited States/CC999999/ImCDC/Intramural CDC HHSUnited States/T42 OH008434/OH/NIOSH CDC HHSUnited States

Promoting a healthy ageing workforce: use of Inertial Measurement Units to monitor potentially harmful trunk posture under actual working conditions

Musculoskeletal disorders, particularly those involving the low back, represent a major health concern for workers, and originate significant consequences for the socio-economic system. As the average age of the population is gradually (yet steadily) increasing, such phenomenon directly reflects on labor market raising the need to create the optimal conditions for jobs which must be sustainable for the entire working life of an individual, while constantly ensuring good health and quality of life. In this context, prevention and management of low back disorders (LBDs) should be effective starting from the working environment. To this purpose, quantitative, reliable and accurate tools are needed to assess the main parameters associated to the biomechanical risk. In the last decade, the technology of wearable devices has made available several options that have been proven suitable to monitor the physical engagement of individuals while they perform manual or office working tasks. In particular, the use of miniaturized Inertial Measurement Units (IMUs) which has been already tested for ergonomic applications with encouraging results, could strongly facilitate the data collection process, being less time- and resources-consuming with respect to direct or video observations of the working tasks. Based on these considerations, this research intends to propose a simplified measurement setup based on the use of a single IMUs to assess trunk flexion exposure, during actual shifts, in occupations characterized by significant biomechanical risk. Here, it will be demonstrated that such approach is feasible to monitor large groups of workers at the same time and for a representative duration which can be extended, in principle, to the entire work shift without perceivable discomfort for the worker or alterations of the performed task. Obtained data, which is easy to interpret, can be effectively employed to provide feedback to workers thus improving their working techniques from the point of view of safety. They can also be useful to ergonomists or production engineers regarding potential risks associated with specific tasks, thus supporting decisions or allowing a better planning of actions needed to improve the interaction of the individual with the working environment

IMU-based human activity recognition and payload classification for low-back exoskeletons

Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers’ health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of 90.80 % (activity recognition) and 87.14 % (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human–robot interaction

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

A COMPARISON OF SELECT TRUNK MUSCLE THICKNESS CHANGE BETWEEN SUBJECTS WITH LOW BACK PAIN CLASSIFIED IN THE TREATMENT-BASED CLASSIFICATION SYSTEM AND ASYMPTOMATIC CONTROLS

The purposes of this dissertation were to determine: 1) the relationship betweenmuscle thickness change (MTC) as measured by rehabilitative ultrasound imaging(RUSI) and EMG activity in the lumbar multifidus (LM), 2) if motor control changesproduced by experimentally induced pain are measurable with RUSI, 3) if a differenceexists in MTC between subjects with low back pain (LBP) classified in the treatmentbasedclassification system (TBC) system and controls, 4) if MTC improves followingintervention.Current literature suggests sub-groups of patients with LBP exist and responddifferently to treatment, challenging whether the majority of LBP is nonspecific . TheTBC system categorizes subjects into one of four categories (stabilization, mobilization,direction specific exercise, or traction). Currently, only stabilization subjects receive anintervention emphasizing stability. Because recent research has demonstrated that motorcontrol impairments of lumbar stabilizing muscles are present in most subjects with LBP,it is hypothesized that impairments may be present across the TBC classifications.Study 1: Established the relationship between MTC as measured by RUSI andEMG in the LM. Study 2: Assessed MTC of the LM during control and painfulconditions to determine if induced pain changes in LM and transverse abdominis (TrA)are measurable with RUSI. Study 3: Measured MTC of the LM and TrA in subjects withLBP classified in the TBC system and 20 controls. Subjects completed a stabilizationprogram and were re-tested.The inter-tester reliability of the RUSI measurements was excellent (ICC3,3 =.91,SEM=3.2%). There was a curvilinear relationship (r = .79) between thickness changeand EMG activity. There was a significant difference (p andlt; .01) between control andpainful conditions on 4 of the 5 LM tasks tested and on the TrA task. There was adifference in MTC between subjects and controls on the loaded LM test which varied bylevel and category. All categories were different from control on the TrA. Followingintervention the TrA MTC improved (p andlt; .01). The LM MTC did not (p values from .13-.86).These findings suggest MTC can be clinically measured and that deficits existwithin TBC system. Significant disability and pain reduction were measured

Wearable Sensors Applied in Movement Analysis

Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges

Optimization of Safety Control System for Civil Infrastructure Construction Projects

Labor-intensive repetitive activities are common in civil construction projects. Construction workers are prone to developing musculoskeletal disorders-related injuries while performing such tasks. The government regulatory agency provides minimum safety requirement guidelines to the construction industry that might not be sufficient to prevent accidents and injuries in a construction site. Also, the regulations do not provide insight into what can be done beyond the mandatory requirements to maximize safety and underscore the level of safety that can be attained and sustained on a site. The research addresses the aforestated problem in three stages: (i) identification of theoretical maximum attainable level of safety, safety frontier, (ii) identification of underlying system inefficiencies and operational inefficiencies, and (iii) identification of achievable level of safety, sustainable safety. The research proposes a novel approach to identify the safety frontier by kinetic analysis of the human body while performing labor-intensive repetitive tasks. The task is a combination of different unique actions, which further involve several movements. For identifying a safe working procedure, each movement frame needs to be analyzed to compute the joint stress. Multiple instances of repetitive tasks can then be analyzed to identify unique actions exerting minimum stress on joints. The safety frontier is a combination of such unique actions. For this, the research proposes to track the skeletal positional data of workers performing different repetitive tasks. Unique actions involved in all tasks were identified for each movement frame. For this, several machine learning techniques were implemented. Moreover, the inverse dynamics principle was used to compute the stress induced by essential joints. In addition to the inverse dynamics principle, several machine learning algorithms were implemented to predict lower back moments. Then, the safety frontier was computed, combining the unique actions exerting minimum stress to the joints. Furthermore, the research conducted a questionnaire survey with construction experts to identify the factors affecting system inefficiencies that are not under the control of the project management team and operational inefficiencies that are under control. Then, the sustainable safety was computed by adding system inefficiencies to the safety frontier and removing operational inefficiencies from observed safety. The research validated the applicability of the proposed methodology in a real construction site. The application of random forest classifier, one-vs-rest classifier, and support vector machine approach were validated with high accuracy (\u3e95%). Similarly, random forest regressor, lasso regression, gradient boosting evaluation, stacking regression, and deep neural network were explored to predict the lower back moment. Random forest regressor and deep neural network predicted the lower back moment with an explained variance of 0.582 and 0.700, respectively. The computed safety frontier and sustainable safety can potentially facilitate the construction sector to improve safety strategies by providing a higher safety benchmark for monitoring, including the ability to monitor postural safety in real-time. Moreover, different industrial sectors such as manufacturing and agriculture can implement the similar approach to identify safe working postures for any labor-intensive repetitive task