1,721,115 research outputs found
Quantification of ankle muscle co-contraction during early stance by wavelet-based analysis of surface electromyographic signals
No full textThe present study involves Continuous Wavelet Transform (CWT) for the analysis of surface electromyographic (sEM G) signals, with the aim of assessing muscle co-contraction during early stance of healthy-subj ect walking. CWT approach allows computing the coscalogram function, a localized statistical assessment of cross-energy density between two signals. In this study, CWT coscalogram function between two sEMG signals from antagonist muscles is used to quantify muscular co-contraction activity. Daubechies of order 4 (factorization in 6 levels) is adopted as mother wavelet. Noise reduction in the sEMG signals is performed applying CWT denoising. Co-contractions between gastrocnemius lateralis and tibialis anterior are assessed on a set of experimental sEM G signals acquired in 15 able-bodied subjects during walking. Results show as the present CWT approach can provide a reliable assessment of co-contraction in early-stance phase of walking, highlighting that this co-contraction is short (< 1 0 ms) and very frequent. A large variability in the occurrence of the co-contraction is also detected, suggesting that each subject adopts her/his own modality of co-contraction. However, the same physiological purpose is maintained for all subj ects, i.e., to control shock absorption and improve weight-bearing stability during the first phase of human walking. Physiological reliability of experimental results suggests the appropriateness of the present method in clinical applications
A New Method for sEMG Envelope Detection from Reduced Measurements
No full textThe amount of data to be transmitted from each smart device to the cloud server increases with the number of sensors, so compressing the acquired biosignals before transmission is relevant to increase the efficiency in Internet of Things networks. This principle applies to surface Electromyography (sEMG) signals for gait analysis as well. The paper proposes a new method based on Compressed Sensing (CS) for sEMG processing from reduced measurements. A deterministic matrix is chosen to model the compression phase. Instead, a matrix built with a Daubechies wavelet kernel is considered for the reconstruction phase. The CS reconstruction is then applied to the detection of a significant feature of sEMG signals, that is the linear envelope. Thus, the CS-based method for envelope detection is analyzed on sEMG signals corresponding to strides measured by 10 healthy subjects. The proposed method proves to be reliable for envelope detection. In fact, by comparing peak amplitudes and time positions of envelopes corresponding to reconstructed and original signals, the proposed CS-based method shows an irrelevant loss of information
A WAV file dataset of bottlenose dolphin whistles, clicks, and pulse sounds during trawling interactions
Full text linkGlobally, interactions between fishing activities and dolphins are cause for concern due to their negative effects on both mammals and fishermen. The recording of acoustic emissions could aid in detecting the presence of dolphins in close proximity to fishing gear, elucidating their behavior, and guiding potential management measures designed to limit this harmful phenomenon. This data descriptor presents a dataset of acoustic recordings (WAV files) collected during interactions between common bottlenose dolphins (Tursiops truncatus) and fishing activities in the Adriatic Sea. This dataset is distinguished by the high complexity of its repertoire, which includes various different typologies of dolphin emission. Specifically, a group of free-ranging dolphins was found to emit frequency-modulated whistles, echolocation clicks, and burst pulse signals, including feeding buzzes. An analysis of signal quality based on the signal-to-noise ratio was conducted to validate the dataset. The signal digital files and corresponding features make this dataset suitable for studying dolphin behavior in order to gain a deeper understanding of their communication and interaction with fishing gear (trawl)
Muscle Co-Contraction Detection in the Time–Frequency Domain
Full text linkBackground: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based crossenergy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signalto-noise ratio (SNR), and then validated against an approach based on the acknowledged doublethreshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-theart approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications
Measurement of stride time by machine learning: sensitivity analysis for the simplification of the experimental protocol
No full textLimited stride-time variability is considered a marker of safe walking. Thus, the measurement of stride time is a meaningful information for gait analysis. The use of machine-learning (ML) techniques has been proven to be useful to this aim, even if the amount of data provided as input influences the computation process. The present study is aiming to analyze the sensitivity of the experimental protocol (number of sensors and signals) on the performance of a stride-time measurement system based on ML interpretation of surface EMG signals (sEMG). To this purpose, sEMG signals from ten leg muscles of 30 volunteers are used to train a single-layer neural network. Five experimental protocols (from five to one sEMG sensors per leg) are comparatively tested. Results show that reducing the sEMG-protocol complexity (less sensors utilized) is decreasing the prediction performances. Based on the test results, this study proposes an experimental protocol composed of two sEMG sensors per leg (over gastrocnemius lateralis and tibialis anterior), as the best compromise between the need of a simplified experimental set-up and the necessity of high performances (F1-score±SD = 99.0±1.2%; mean absolute value, MAE±SD = 17.9±4.3 ms). The use of only two sEMG probes is going to have a great impact on gait analysis, improving patient comfort and reducing clinical costs and time consumption. A possible, further reduction of experimental protocol to a single muscle (gastrocnemius lateralis) is feasible accepting a less efficient prediction of the stride-time
A Max-Plus Algebra-Based Platform for Modelling Coordinated Underwater Tasks of Fish Robots
No full textThis paper presents the design and implementation of a max-plus algebra-based platform to plan and model the coordinated behaviour of fish robots during underwater surveys. The tool, developed using MATLAB App Designer, features an intuitive Graphical User Interface (GUI) that allows users to set travel and exploration timings for a given route and input data. The GUI then simulates the temporal evolution of the coordinated tasks through the background creation of a general max-plus linear system. The GUI provides a clear visualisation of the input and output values, enabling seamless integration of mathematical modelling with practical applications. The case studies demonstrate the platform's capability to make task coordination efficient and highlight potential improvements to advance underwater robotics research
Prediction of stride duration by neural-network interpretation of surface EMG signals
No full textMeasuring stride duration as a marker of regular walking is a relevant issue, also in the modern gait analysis. The present project was designed to test the hypothesis that an artificial-neural-network approach is able to provide a reliable prediction of stride, stance, and swing duration, based on the analysis of only EMG signals acquired during able-bodied walking. To this objective, surface EMG signals from ten leg muscles of 23 adult subjects are used to train a multi-layer perceptron model. Performance of classifiers is tested vs. gold standard, represented by foot-floor-contact signals measured by means of three footswitches positioned under each foot. Outcomes indicate an accurate prediction of stride duration (mean absolute value, MAE ± SD = 18.1 ± 6.2 ms), stance duration (MAE ± SD = 29.2 ± 10.3 ms), and swing duration (MAE ± SD = 28.8 ± 9.6 ms), at least comparable to those reported in IMU-based studies. A significant contribution of this approach is that only sEMG signals (and no further data) during patient walking are needed to get the gait durations, after training the neural network. This contributes to reduce the costs of the test, the clinical time-wasting, and the invasiveness of instrumentation worn by the patient, making this approach very suitable especially for the clinical analysis of neuromuscular disorders where the evaluation of muscular recruitment is recommended
A deep learning approach to EMG-based classification of gait phases during level ground walking
Full text linkCorrectly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Literature reported few machine-learning-based approaches for gait-phases classification from surface electromyographic (sEMG) signal during treadmill walking. To our knowledge, no attempts were made during ground walking in daily-life conditions. A methodology for classification of stance/swing and prediction of foot-floor-contact signal during ground walking in conditions similar to daily life is proposed here, based on the application of Multi-Layer Perceptron models to sEMG signal alone. sEMG were acquired from eight lower-limb muscles in about 13.000 strides from 23 healthy adults, during ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. Classification and prediction accuracy were tested vs. the ground truth, represented by the basographic signal provided by three foot-switches, through samples not used in the learning phase, coming from both the same group of subjects used to generate the learning set (LS-Test) and brand-new subjects (unlearned, US). Results showed an average classification accuracy (± SD) over 23 folds of 94.9 ± 0.3 for LS-test and 93.4 ± 2.3 for US. Prediction of foot-floor-contact signal was quantified in terms of timing of heel strike and toe off: mean (over ten folds) absolute difference between predictions and footswitch data for UL was 15 ± 17 ms and 36 ± 22 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot-floor-contact signal from sEMG signals during ground walking in daily-life conditions
Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis
Full text linkThe assessment of muscle-recruitment timing from electromyography (EMG) signal is relevant in different fields, including clinical gait analysis and robotic systems to interpret user's motion intention. However, available methods typically provide only information in time domain without evaluating muscle-activation frequency content. This study aims to propose a novel adaptative algorithm for detecting muscle activation in time-frequency domain based on continuous wavelet transform (CWT) analysis. Precisely, the novel contribution of the proposed algorithm consists of evaluating the frequency range of every muscle activations detected in time domain. Performances are evaluated on a test bench of 720 simulated and 105 real surface EMG signals, stratified for signal-to-noise ratio (SNR), and then validated against different reference algorithms. Outcomes indicate that the proposed approach can provide an accurate prediction of muscle onset and offset timing in both simulated (mean absolute error, MAE \approx 10 ms) and real datasets (MAE < 30 ms), minimally affected by the SNR variability and compatible with the timing of EMG-driven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggests a large within-muscle between-muscle variability of the frequency range. In conclusion, the current study introduces a novel reliable wavelet-based algorithm to detect both time and frequency content of muscle activation, suitable in different conditions of signal quality
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