Moment Estimation Research Articles

Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion.

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein-Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Seismic moment of local earthquakes in the central part of the Baikal rift by the coda envelope inversion

The article presents an estimation of scalar seismic moment by the coda envelope inversion of local earthquakes recordings in the central part of the Baikal rift zone. In addition to the earthquake source parameters, the method allows us to simultaneously estimate seismic energy loss and site amplification factors for frequency bands from 0.53 to 34 Hz. Because of the compromise between spectral source energy and site responses, we estimated amplification factors for used stations relative to the "UlanUde" (UUDB) reference site located in crystalline rock. In order to estimate intrinsic and scattering attenuation we used events recorded at three broadband seismic stations and epicentral distances between 40 and 120 km. The result suggests that intrinsic absorption is dominant over scattering attenuation in the central part of the Baikal rift zone for most frequency bands, but the seismic albedo B0, expressing the contribution of scattering to the total attenuation, showed variations from 0.23 to 0.6 for frequencies below 1 Hz, with mean B0 value 0.33. Attenuation and siteamplification factors were used to estimate the seismic moment and moment magnitudes Mw of local earthquakes. The resultant moment magnitudes exhibit a good agreement with routinely reported local magnitude (ML) estimates for the study area. The coda inversion estimates of seismic moment provide stable, unbiased moment magnitudes for events that are too small to be seen at teleseismic distances.

Tunnelling-induced nonlinear responses of continuous pipelines resting on tensionless Winkler foundation

Prevailing analytical approaches for tunnelling-induced soil-pipeline interactions predominantly rely on linear analyses, limiting their applicability in nonlinear scenarios. This study introduces a novel tensionless Winkler solution that accounts for gap formation and soil yielding, validated against three well-documented experiments and demonstrating superiority over existing Winkler solutions. Additionally, plate load tests refine traditional soil-bearing theories for buried pipelines in sand, providing subgrade stiffness and ultimate bearing capacity values pertinent to tunnelling-induced interactions. Parametric studies highlight amplified nonlinearity in pipeline behaviours with increased pipeline flexural rigidity and tunnel volume loss, due to soil-pipeline separation and subgrade yielding. Notably, ignoring gap formation and soil yielding leads to overly conservative estimations of pipeline deflections and bending moments. Higher subgrade moduli increase pipeline strains, while enhanced subgrade bearing capacity above the pipeline prevents soil yielding, rendering its effect negligible, whereas the bearing capacity beneath the pipeline is inconsequential in tunnelling scenarios.

How Does Digital Finance Contribute to Sustainable Wealth Growth: Perspective from Residents’ Income

Sustainable growth relies on common prosperity, which is reflected in increasing total income and equitable income distribution. This study first proposes the theoretical mechanisms by which digital financial development affects residents’ total income and income distribution. After that, a two-stage generalized method of moments estimation model with endogeneity treatment is constructed to investigate the impact of digital finance on residents’ total income in 31 Chinese provinces. Moreover, Moran’s I and a spatial autoregression model are used to explore the impact of digital finance on residents’ income distribution. The results demonstrate that digital financial development can significantly contribute to the increase in residents’ total income in both urban and rural areas, thus contributing to regional sustainable wealth growth. In addition, digital finance has a spatial direct effect and a spatial spillover effect on the optimization of residents’ income distribution. This indicates that a region’s digital financial development benefits regional sustainable wealth growth, as it not only can improve residents’ income distribution within the same region but also can promote the income distribution of neighboring regions.

The ergodicity and uniform large deviations for the 1D stochastic Landau-Lifshitz-Bloch equation

In this article, we consider two aspects asymptotic behaviors for the 1D stochastic Landau-Lifshitz-Bloch equation driven by additive noise including the unique ergodicity and the uniform large deviations. Using a deterministic argument relying on the exponential moment and exponential stability estimates, we first establish the unique ergodicity of invariant measure in H 1. Then, the uniform large deviations is established in C ( [ 0 , T ] ; H 1 ) by the uniform contraction principle.

Closed-form approximations of moments and densities of continuous–time Markov models

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump–diffusions. The proposed expansions extend the ones in Kristensen and Mele (2011) to cover general Markov processes, and nest transition density and option price expansions recently developed in the literature, thereby connecting seemingly different ideas in a unified framework. We show how the general expansion can be implemented for fully general jump–diffusion models. We provide a new theory for the validity of the expansions which shows that series expansions are not guaranteed to converge as more terms are added in general once the time span of interest gets larger than some model–specific threshold. Thus, these methods should be used with caution when applied to problems with a larger time span of interest, such as long-term options or data observed at a low frequency. At the same time, the numerical studies in this paper demonstrate good performance of the proposed implementation in practice when applied to pricing options with time to maturity below three months. Thus, our expansions are particularly well suited for pricing ultra-short-term (such as “zero–day”) options.

Islamic Banks’ Profit and Violations in Murābaḥah Financing

Purpose — This paper investigates the relationship between murābaḥah contracts and the profitability of Islamic banks, explicitly examining how potential Sharīʿah non-compliance issues related to murābaḥah contracts might impact the banks’ profitability. Design/Methodology/Approach — Employing the Generalised Method of Moments (GMM) estimation for fixed effects dynamic panels, this study analyses quarterly data from 2013Q4 to 2023Q1 of the four largest Islamic banks in Saudi Arabia. Findings — The results reveal a positive correlation between the increase in the value of murābaḥah contracts and Islamic earnings. While domestic macroeconomic conditions influence this relationship, international conditions have minimal impact. This investigation meticulously identifies instances of Sharīʿah violations within Islamic banks as they pursue a ‘search for yield’. Originality/Value — Prior studies have fallen short in establishing a definitive association between the profits of Islamic banks and the value of murābaḥah contracts. This research bridges this gap and introduces a novel dimension to the existing literature by illuminating the potential compromises in Sharīʿah compliance when employing murābaḥah contracts. The dual focus on both financial implications and ethical considerations makes this study unique, representing a significant contribution to the ongoing discourse in Islamic finance literature. Research Limitations/Implications — The availability of data on murābaḥah contracts may pose constraints to this study. Practical Implications — The analysis sheds light on various scenarios where Islamic banks may compromise their adherence to Sharīʿah principles in pursuit of higher profits, offering valuable solutions to address and rectify such violations.

Can Servicification Benefit Manufacturing Exports? Empirical Results for India

To enhance manufacturing exports, India, a major player in the services segment, has recently created an enabling environment through the launch of several programmes, for example, the ‘Make-in-India’ and the production-linked incentive (PLI) schemes. As the firm-level evidence on the synergies between services input use (i.e., servicification) and manufacturing exports in the country is relatively scarce, the current analysis studies the relationship between them by using the CMIE-Prowess dataset over a period of 2000–2019, for both new and existing exporting firms. At the firm level, the article uses a two-step system generalized method of moments (GMM) estimator for studying the impact on export intensity, while a dynamic panel probit model is used to study the impacts on the decision to export. Apart from the analysis at the aggregate level, the influence of servicification is also judged for the low-tech and mid-to-hi-tech sectors separately and the disaggregated industry level. The observed positive relationship between servicification and manufacturing exports implies that the integration of competitiveness in the field of services with hi-tech manufacturing sectors can enhance India’s exports on the one hand and deepen firm-level global value chains participation on the other hand. JEL Codes: C23, F12, F14, F23, L25, L80

Factors influencing the coupled and coordinated development of cities in the Yangtze River Economic Belt: A focus on carbon reduction, pollution control, greening, and growth

Atmospheric pollutants PM2.5 and CO2 share similar sources and impact mechanisms. Green innovations and urban greening significantly reduce these pollutants while promoting economic growth. However, the synergies and trade-offs between carbon reduction, pollution control, green expansion, and economic growth remain understudied. This paper examines 110 cities in the Yangtze River Economic Belt (YREB), China's premier green development site, as a unified system. Using fractional-order synthesis analysis, this paper constructs an assessment indicator system and measures synergy with a coupled coordination degree model. The driving factors are explored using a system-generalized method of moments estimation. The findings indicate that most cities in the YREB are at an intermediate coordination stage. The coupling of greening with carbon reduction, pollution control, and growth has a low degree, highlighting an urgent need to strengthen greening efforts. Key drivers include the digital economy, advanced industrial structure, innovative talent aggregation, infrastructure construction, financial investment, and marketization. The digital economy significantly influences all regions of the Yangtze River. Notable heterogeneity exists in the impact of other drivers across different regions. These results offer valuable policy insights for managing carbon emissions and pollutants, contributing to sustainable urban development.

Addressing endogeneity in the relationship between early entry and performance: The case of foreign market expansion

Addressing endogeneity issues has been identified as a key priority for ensuring continued progress in the field of strategic management. We contribute to this research agenda by developing a generalized method of moments (GMM) estimation approach that accounts for endogeneity in dynamic models. To illustrate how endogeneity bias impedes reliable interpretation, we examine the relationship between early mover strategies and performance using data on multinational enterprises entering transition economies. Our empirical results demonstrate that early mover advantages are significantly greater after controlling for endogeneity. Additionally, we find evidence that the level of institutional development weakens the advantages of pursuing an early mover strategy in transition economies. After accounting for endogeneity, the negative moderating effect of institutional development is far more pronounced. The magnitude of these two biases underscore the necessity for scholars to adequately address endogeneity and how GMM estimation can facilitate in better understanding the performance implications of strategic choices.

Robust estimator of the ruin probability in infinite time for heavy-tailed distributions

The probability of ruin of an insurance company is one of the main risk measures considered in risk theory, and the problems of its calculation and approximation have attracted much attention. Statistical estimations have been developed on the ruin probability in infinite time for insurance loses from heavy-tailed distributions. However, these estimation suffer heavily from under-coverage or have a robustness problem. We therefore need another method for estimating the probability of ruin in infinite time for heavy-tailed losses. In this paper, we introduce a robust estimator of the infinite-time probability of ruin for such distributions. Our methodology is based on extreme value theory, which offers adequate statistical results for such distributions. Our approach is based on a sensitive distribution known as the t-Hill estimator (t-score or score moment estimation) for the index of any tail distribution and introduced in Fabiáan and Stehlík. We establish their asymptotic normality, and through a simulation study, illustrate their behaviour in terms of absolute bias and mean squared error. The simulation results show that our estimators perform well and that they are fairly robust to outliers.

Regional (economic) integration, political stability uncertainty and (intra-African) exports

The economic literature reports ambiguous effects of regional economic integration (REI) and political stability on exports. Therefore, this research analyzes the effects of REI and political stability on intra-African exports, compares them to the effects on intra-African imports and explores the combined effect of REI and political stability on intra-African exports. We use the two-stage least squares and fixed-effects approach which includes the two-step feasible generalized method of moments (GMM) estimation to address endogeneity issues in a panel of 49 African countries over the period of 1996–2022. The results show that REI and political stability lead to an increase in intra-African exports. However, while REI results in an increase in intra-African imports, political stability results in a decrease; similar findings are highlighted for overall intra-African trade as well as for total exports of goods and services. The results remain robust across diverse estimation techniques. Furthermore, they reveal that political stability plays a role in amplifying the relationship between REI and intra-African exports. Supporting the African Continental Free Trade Area agenda, these findings imply that African policymakers need to (i) strengthen REI to increase intra-African trade and (ii) improve political stability to ensure that a greater part of REI is canalized in a more effective way in order to boost intra-African exports.

Innovation and Path to Inclusiveness in Developing Countries

This study examines the effect of innovation on inclusive growth in 63 developing countries and employs System Generalized Method of Moments (GMM) estimation techniques. The study used the global innovation index to measure innovation. In addition, we construct an index of inclusive growth. Further, due to the complex nature of inclusive growth, using a single index may not provide the full picture. So, for substantial empirical support, the study uses a set of dimensions of inclusive growth separately. The empirical results show a positive relationship between the measures of innovation with inclusive growth. Further, the study also constructs indifference curves for selected developing economies to measure the inclusiveness in growth. Finally, it is suggested that developing economies should put in policies to promote innovation activities that include poor segments of society to improve the impact of innovation on inclusive growth.

GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series sensor data

The Industrial Internet of Things (IIoT) is undergoing rapid development, and as a result, security threats have emerged as a significant concern. IIoT networks, while enhancing service quality, are particularly susceptible to security risks because of their intrinsic interconnectedness and the use of low-power devices. The data produced by millions of sensors in the IIoT is highly dynamic, diverse, and of massive magnitude. The risk of dangers to IoT gadgets in a nuclear plant or a petroleum refinery is significantly greater when compared to that of home appliances. Often connected to the internet, IIoT devices and systems lack robust security measures, rendering them susceptible to cyberattacks. A breach in IIoT security could result in data theft, equipment damage, or even physical harm. To mitigate these risks, IIoT systems require secure authentication and encryption protocols, regular software updates, and proactive monitoring and response capabilities. These methods' primary disadvantages are their difficulty in implementation and inability to ensure effective security. Hence, a second line of protection, such as intrusion threat detection in IIoT, is required. In this research, we propose a new threat intrusion detection model in the IIoT through a genetic algorithm with attention mechanism and modified Adam optimized LSTM (GA-mADAM-IIoT). The GA-mADAM-IIoT consists of six modules: the activity receiver, communication module (CM), attention module (AM), intrusion detection module, mitigation module, and alert module. The GA was designed for feature dimensionality and selection trained on network flow data via a Long Short-Term Memory (LSTM) network. The adaptive moment estimation (Adam) optimizer was modified in order to optimize the LSTM (mADAM-LSTM) networks. To enhance the performance of our model, the categorical cross-entropy (CCE) cost function was used to calculate the difference between the predicted output and the actual output. Additionally, the CCE cost function optimized the model's parameters to minimize the difference between predicted and actual values in terms of probability distributions. The Modified Adam (mADAM) optimization algorithm updates the weights and biases of the LSTM to minimize the cost function. Due to the limited availability of real-world datasets containing accurately labelled anomalies, particularly for industrial facilities and manufacturing facilities, we have utilized two sensor datasets derived from physical test-bed systems for water treatment: Secure Water Treatment (SWaT) and Water Distribution (WADI). In these datasets, operators have simulated attack scenarios that occur in real-world water treatment plants and have recorded these instances as the ground truth anomalies. A regularization parameter was added to the cost function to prevent LSTM from overfitting. In order to improve the model's performance, the AM integrates a succinct yet effective attention mechanism that enhances significant information in the output of the CM. This reduces the burden on GA-mADAM-IIoT to detect threat patterns at the IDS module. The experimental findings showed that the threat detection GA-mADAM-IIoT via GA feature dimensionality and modified Adam LSTM outperforms the ablation studies. Furthermore, we improve the suggested transparency of threat detection by integrating the Shapley Additive Explanations (SHAP) technique from Explainable AI. This enhances the trustworthiness and comprehensibility of the threat detection process. The proposed model was also compared with the state-of-the-art models, and our results showed outstanding accuracy of 99.98 %, AUC of 100 %, recall of 99.98 %, precision of 99.98 %, F1 of 99.98 %, and MCC of 99.66 % on SWaT data. On the WADI, we obtained an accuracy of 99.87 %, AUC of 100 %, recall of 99.87 %, precision of 99.87 %, F1-score of 99.87 %, and MCC of 98.20 %. The proposed GA-mADAM-IIoT is a generalized model that can be integrated with other IIoT security solutions, such as firewalls and access controls, to provide comprehensive security coverage in real time.

Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.

Energy efficiency characteristics analysis for process diagnosis under anomaly using self-adaptive-based SHAP guided optimization

Understanding energy efficiency patterns is crucial for developing more effective energy management strategies. However, disruptions from physical characteristics, such as particle accumulation, inhibit the construction of energy efficiency models, pose diagnostic challenges, and require additional fault detection models to isolate this uncertainty. Therefore, this study introduces a self-adaptive, long short-term memory-based energy efficiency model with adaptive moment estimation fine-tuning enhanced by Shapley additive explanation guided optimization. The model adapts its learnable parameters in real-time according to changes in process behavior, which helps in revealing energy inefficiency and particle accumulation through Shapley benchmarking under current operations and energy efficiency characteristics. Validated using a benchmark dataset and applied in a large-scale detergent industry, the model outperforms conventional methods, achieving testing r-squared values of 0.9895 and 0.9859, respectively. Moreover, the proposed model avoided formulating the relationship with faulty variables and demonstrated robust fault detection through energy efficiency patterns without needing fault labels, offering a novel approach to monitoring and optimizing energy efficiency. The adaptive weight analysis emphasized how energy efficiency is influenced by various input variables, leading to an hourly energy saving of 0.0271 GJ/t, equivalent to cost savings of USD 34,408 and a reduction of 115.44 t of carbon emissions.

Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound

ObjectiveEndobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location. MethodsPatient and lymph node data were collected from a single-center database. The diagnosis of metastasis was confirmed with endobronchial ultrasound-guided transbronchial needle aspiration and/or surgically resected specimens; the diagnosis of normal lymph node was confirmed with surgically resected specimens only. An annotation system facilitated automated image extraction from endobronchial ultrasound videos. Image frames were randomly selected and split into training and validation datasets on a per-patient basis. A deep learning model with convolutional neural networks, SqueezeNet, was used for image classification via transfer learning based on pretraining from ImageNet. Adaptive moment estimation and stochastic gradient descent were applied as optimizers. ResultsSqueezeNet, with adaptive moment estimation, achieved a sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 96.7% each after 300 epochs, whereas SqueezeNet with stochastic gradient descent achieved 91.1% each. However, SqueezeNet with stochastic gradient descent demonstrated more stable performance than with adaptive moment estimation. ConclusionsDeep learning-based image classification using convolutional neural networks showed promising diagnostic accuracy for lung cancer nodal metastasis. Future clinical trials are warranted to validate the algorithm's efficacy in a prospective, large-cohort study.

Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.

Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p < 0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p < 0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p < 0.05) which was significantly higher than either modality individually (p < 0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p < 0.05). These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

Assessing the accuracy of a wireless sensor system for estimating lumbar moments during manual lifting tasks considering the effects of load weight, asymmetry, and height

This study assessed the accuracy of L5/S1 moment estimates calculated with an Inertial Motion Capture (IMC) system during an asymmetrical and variable height lifting task. The effects of load weight, asymmetry, and lifting height on estimates of lumbar moment have not been comprehensively considered in studies using IMC systems. Thirty-six participants engaged in tasks involving three loads, lifting heights, and trunk rotation angles. Lumbar moments were calculated using bottom-up and top-down biomechanical models. Gold-standard Optical Motion Capture (OMC) and Force Plates (FP) were used as the reference. A randomized block partially confounded design was used to compare the root mean square errors (RMSE) between the IMC and OMC-based reference estimates. The IMC system's estimated peak moments were 12%–13% lower than those estimated using the gold standard OMC-BU inverse dynamics, while the RMSE varied between 19 and 21 Nm. A Load*Height interaction was found; a trend was identified where the RMSE values increased as both the load and height levels increased. The angle did not show a significant effect on any of the tested scenarios. A close correspondence between the IMC and OMC-based moment estimates was established, with the load being the main factor affecting the differences between systems. The IMC system shows potential for use in occupational settings to capture data on the lumbar moments of workers, which could be utilized to assess ergonomic risk.