Mechanical lifting energy consumption in work activities designed by means of the "revised NIOSH lifting equation"\u80\u9d

The aims of the present work were: to calculate lifting energy consumption (LEC) in work activities designed to have a growing lifting index (LI) by means of revised NIOSH lifting equation; to evaluate the relationship between LEC and forces at the L5-S1 joint. The kinematic and kinetic data of 20 workers were recorded during the execution of lifting tasks in three conditions. We computed kinetic, potential and mechanical energy and the corresponding LEC by considering three different centers of mass of: 1) the load (CoML); 2) the multi-segment upper body model and load together (CoMUpp+L); 3) the whole body and load together (CoMTot). We also estimated compression and shear forces. Results shows that LEC calculated for CoMUpp+L and CoMTot grew significantly with the LI and that all the lifting condition pairs are discriminated. The correlation analysis highlighted a relationship between LEC and forces that determine injuries at the L5-S1 joint

Characterizing the Gait of People With Different Types of Amputation and Prosthetic Components Through Multimodal Measurements: A Methodological Perspective

Prosthetic gait implies the use of compensatory motor strategies, including alterations in gait biomechanics and adaptations in the neural control mechanisms adopted by the central nervous system. Despite the constant technological advancements in prostheses design that led to a reduction in compensatory movements and an increased acceptance by the users, a deep comprehension of the numerous factors that influence prosthetic gait is still needed. The quantitative prosthetic gait analysis is an essential step in the development of new and ergonomic devices and to optimize the rehabilitation therapies. Nevertheless, the assessment of prosthetic gait is still carried out by a heterogeneous variety of methodologies, and this limits the comparison of results from different studies, complicating the definition of shared and well-accepted guidelines among clinicians, therapists, physicians, and engineers. This perspective article starts from the results of a project funded by the Italian Worker's Compensation Authority (INAIL) that led to the generation of an extended dataset of measurements involving kinematic, kinetic, and electrophysiological recordings in subjects with different types of amputation and prosthetic components. By encompassing different studies published along the project activities, we discuss the specific information that can be extracted by different kinds of measurements, and we here provide a methodological perspective related to multimodal prosthetic gait assessment, highlighting how, for designing improved prostheses and more effective therapies for patients, it is of critical importance to analyze movement neural control and its mechanical actuation as a whole, without limiting the focus to one specific aspect

Using the frequency signature to detect muscular activity in weak and noisy myoelectric signals

The detection of muscular activity for signals characterized by low amplitude and low signal-to-noise ratio – weak and noisy – is a challenge in biomedical data processing. The aim of this paper is to introduce a method based only on the frequency characteristics of the weak and noisy EMG to detect muscular activity. The algorithm is window-based and consists of two processing steps: i) estimation of zero-crossings and mean instantaneous frequency of the signal; ii) clustering by a k-means approach to separate the muscular activity from the silent phases. We assessed the method on 320 simulated EMG signals that have been generated from a small number of synthetic motor units working at a low firing rate and then manipulated by adding Gaussian noise to simulate four different levels of low signal-to-noise ratio (SNR). Tests were carried on by changing the window dimension – fifteen different window lengths – and the amount of overlap of the window along the signal – four different values of overlapping. The performance of the algorithm was evaluated by calculating the temporal bias of the onset detection, the percentage error made when estimating the activity duration, and the F1 score as a measure of accuracy. The results showed that the algorithm performance does not depend from SNR but depends on both window length and overlap. The detection accuracy ranges from 96% to 98% depending on combinations of window length and overlap, while for specific combinations of window length and overlap, the amount of temporal bias fell below 20 ms. These results open promising scenarios for the application of this algorithm to real weak and noisy EMG data

Lower Limb Muscle Co-Activation Maps in Single and Team Lifting at Different Risk Levels

The central nervous system uses muscle co-activation for body coordination, effector movement control, and joint stabilization. However, co-activation increases compression and shear stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team lifting), to understand how the central nervous system governs lower limb rigidity during these tasks. The surface electromyographic signal of thirteen healthy volunteers (seven males and six females, age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the sagittal plane. Then co-activation was computed according to different approaches: global, full leg, flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost all the approaches. Overall, our findings imply that the central nervous system streamlines the motor regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor, and rostro-caudal co-activation scheme, depending on the risk level/lifting type

Erratum: Alberto, R. et al., Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review. <em>Int. J. Environ. Res. Public Health</em> 2018, 15, 2001

Due to an error during production, the first author’s name of the published paper [...

Adaptive Lifting Index (<i>aLI</i>) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use

Muscle activity detection in pathological, weak and noisy myoelectric signals

The estimation of the on-off timing of the human skeletal muscles during movement is a critical issue in weak and noisy myoelectric signal processing, in motor control studies and in clinical applications. In this study, we used an approach based on the continuous wavelet transform to detect the muscular activity, in muscle with low amplitude and low SNR. The method is based on the calculation of a so-called 'manifestation variable' computed as the maximum output of a bank of matched filters at different scales. EMG signals were generated by simulation using software tool based on an EMG mathematical model and different thresholds were applied for estimating the muscle on- off timing in simulated pathological, weak and noisy (several low SNR values were analyzed) myoelectric signals. The true timing of the EMG activity and the estimated timing of the EMG activity were compared by using a relative percentage error criterion. We performed a two-way ANOVA test, with SNR and threshold as factors, to determine possible significant effects on the relative percentage error. Our results showed that this approach shows satisfactory performances especially when proper threshold values are chosen. In particular, despite the estimated timing of the EMG activity approaches the true timing when SNR is higher, the method works well also for very low SNR. Therefore, this approach to estimate the on-off timing of muscles could be used to study pathological, weak and noisy myoelectric signals

Effect of different smartphone uses on posture while seating and standing

In this study, we investigated possible alterations of neck and trunk posture when using smartphones. Fifteen healthy young subjects were asked to perform four activities with the smartphone (gaming, messaging, web surfing, video watching) in two different postures (stand and seat). Kinematics of both neck and trunk was recorded by an optoelectronic system, and the neck, trunk and cranio-cervical angles were extracted to evaluate the variations with respect to the baseline condition. The results showed statistical differences for neck and trunk angles between the two postures for all the activities with an increase of the values in the seated position. No significant differences were found among the activities except for neck angle in both standing and sitting postures. The results highlight that the significant alteration of the neck and trunk posture shows up when the activities are performed in the seated position. Furthermore, the variation of the neck angle depends on the performed activity; in fact, during gaming the flexion-extension of the neck is greater than during video watching. These results suggest that during smartphone use the posture undergoes some changes that may be a potential risk factor to develop neck pain, musculoskeletal fatigue and disorders

Lifting Activity Assessment Using Kinematic Features and Neural Networks

Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features