1,720,963 research outputs found
A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue
No full textMean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2–10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction
Estimation of mean and median frequency from synthetic sEMG signals: Effects of different spectral shapes and noise on estimation methods
No full textThe detection of electrical signs of muscle fatigue passes through the choice of appropriate spectral estimation techniques for the extraction of parameters such as Mean and Median Frequency from surface myoelectric signals. Unfortunately, despite the huge number of contributions using various spectral techniques to process signals recorded in fatiguing protocols, there is no agreement on the method that works best, and to our knowledge, the performance of the estimation techniques has been evaluated only on a limited range of spectral shapes. To study the fatigue phenomenon, it is important to consider the range of variation of the spectral parameters and then the different shapes assumed by the signal spectrum. In this work, the latter characteristics have been used to simulate synthetic myoelectric signals whose Power Spectral Density has been estimated in different noise conditions by using the following techniques: a) indirect technique by the estimation of the autocorrelation function; b) direct technique by the Welch estimation method with special attention to the window functions for signal segmentation; c) Burg autoregressive approach with special attention to the model order. The main result, after assessing that Burg's approach outperforms all the other techniques for all the examined conditions, outlines that the model's order, despite ranging between 6 and 12, depends on the spectral shape and affects the estimation of the Median Frequency. This partially overwrites previous results of the literature and provides the useful recommendation to choose, among the spectral estimation techniques, the most adaptive to the dynamics of the fatigue process
The Influence of the sEMG Amplitude Estimation Technique on the EMG–Force Relationship
No full textThe estimation of the sEMG–force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths
The Impact of Human-Robot Collaboration Levels on Postural Stability During Working Tasks Performed While Standing: Experimental Study
No full textBackground: The integration of collaborative robots (cobots) in industrial settings has the potential to enhance worker safety and efficiency by improving postural control and reducing biomechanical risk. Understanding the specific impacts of varying levels of human-robot collaboration on these factors is crucial for optimizing cobot use. Objective: This study aims to investigate the biomechanical effects of different levels of human-robot collaboration on postural stability and control during simulated working tasks. Methods: A total of 14 participants performed simulated cashier working activities under 4 different collaboration modalities, with increasing levels of cobot assistance: full (Fu), half robot touch (HRT), half robot (HRb), and full robot (FRb). Center of pressure trajectories were extracted from 2 force plates’ data to calculate 4 posturography parameters—mean distance (MDIST), mean velocity (MVELO), 95% confidence ellipse area (AREA-CE), and sway area (AREA-SW)—which were analyzed to assess the impact of cobot intervention on postural control. Results: Nonparametric tests showed significance in the effect of the collaboration modalities on the 4 analyzed parameters. Post hoc tests revealed that FRb modality led to the greatest enhancement in postural stability, with a reduction in MDIST (4.2, SD 1.3 cm in Fu vs 1.6, SD 0.5 cm in FRb) and MVELO (16.3, SD 5.2 cm/s in Fu vs 7.9, SD 1.1 cm/s in FRb). AREA-CE and AREA-SW also decreased significantly with higher levels of cobot assistance (AREA-CE: 134, SD 91 cm2 in Fu vs 22, SD 12 cm2 in FRb; AREA-SW: 16.2, SD 8.4 cm2/s in Fu vs 4.0, SD 1.6 cm2/s in FRb). Complete assistance of the cobot significantly reduced interindividual variability of all center of pressure parameters. In FRb modality, as compared with all other conditions, removing the weight of the object during loading or unloading phases caused a significant decrease in all parameter values. Conclusions: Increased cobot assistance significantly enhances postural stability and reduces biomechanical load on workers during simulated tasks. Full assistance from cobots, in particular, minimizes postural displacements, indicating more consistent postural control improvements across individuals. However, high levels of cobot intervention also reduced the natural variation in how people balanced themselves. This could potentially lead to discomfort in the long run. Midlevel cobot assistance modalities can thus be considered as a good compromise in reducing biomechanical risks associated with postural stability at the same time granting a satisfactory level of user control
Modalities of sequential human robot collaboration trigger different modifications of trunk oscillations
No full textIntroduction: Human robot collaboration is quickly gaining importance in the robotics and ergonomics fields due to its ability to reduce biomechanical risk on the human operator while increasing task efficiency. The performance of the collaboration is typically managed by the introduction of complex algorithms in the robot control schemes to ensure optimality of its behavior; however, a set of tools for characterizing the response of the human operator to the movement of the robot has yet to be developed. Methods: Trunk acceleration was measured and used to define descriptive metrics during various human robot collaboration strategies. Recurrence quantification analysis was used to build a compact description of trunk oscillations. Results and discussion: The results show that a thorough description can be easily developed using such methods; moreover, the obtained values highlight that, when designing strategies for human robot collaboration, ensuring that the subject maintains control of the rhythm of the task allows to maximize comfort in task execution, without affecting efficiency
Novel Metrics for High-Density sEMG Analysis in the Time-Space Domain during Sustained Isometric Contractions
No full textGoal: This study introduces a novel approach to examine the temporal-spatial information derived from HighDensity surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. Methods: Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. Results: Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. Conclusions: These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failur
Analysis of muscular activity of a cashier during different modalities of human robot collaboration
No full textThe effects of siding a collaborative robot (cobot) to a human operator on muscle activity during a scenario simulating a working task (i.e., a supermarket cashier moving cyclically packages) were investigated in this study, by considering different modalities of human-robot collaboration (HRC). Wearable sensors were used to collect kinematic and EMG data, and four different HRC interaction modalities were tested to determine their impact on the biomechanical risk associated with the examined working task. The range, amplitude, and smoothness values of accelerometer data coming from a sensor placed on the trunk, as well as coactivation indexes extracted from EMG data of different relevant muscles were calculated. Without cobot assistance, although a higher smoothness was found in the trunk movement, a high level of prolonged muscular activity was encountered usually associated to a potential increase in the biomechanical risk. In contrast, full assistance from the cobot resulted in the operator developing an intermittent motor control strategy, which reduces the muscular effort but losing smoothness in trunk movements. During two simulated tasks with a medium level of assistance, if the cobot imposed the cadence to complete the task, the required muscular effort remained constantly high, while if the operator was allowed to control the cadence, high smoothness combined with reduced muscular activity resulted. In conclusion, the use of cobots in repetitive tasks can influence the muscle strategy adopted by the operator during HRC. According to the analysis, the most effective approach is to reduce the workload of the operators while allowing them to maintain control over the speed at which tasks are completed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Use of a modified SIR-V model to quantify the effect of vaccination strategies on hospital demand during the Covid-19 pandemic
No full textA novel compartmental model that includes vaccination strategy, permanence in hospital wards and tracing of infected individuals has been implemented to forecast hospital overload caused by COVID-19 pandemics in Italy. The model parameters were calibrated according to available data on cases, hospital admissions, and number of deaths in Italy during the second wave, and were validated in the timeframe corresponding to the first successive wave where vaccination campaign was fully operational. This model allowed quantifying the decrease of hospital demand in Italy associated with the vaccination campaign. Clinical relevance This study provides evidence for the ability of deterministic SIR-based models to accurately forecast hospital demand dynamics, and support informed decisions regarding dimensioning of hospital personnel and technologies to respond to large-scale epidemics, even when vaccination campaigns are available
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