1,721,012 research outputs found
Estimating Spatial Gait Parameters from the Planar Covariation of Lower Limb Elevation Angles: a Pilot Study
No full textWhen characterizing human gait control strategies, theories based on the modularity of the neuromuscular system have been proven to be powerful in providing a compact description of the gait patterns. The planar covariation law of lower limb elevation angles has been proposed as a compact, modular description of gait kinematics. In this paper, we exploit this model for characterizing healthy subjects' spatial gait parameters during walking at different speeds, one self-selected and one slightly slower than the subject's comfortable pace. Different geometrical features have been calculated over the gait loop, that is the planar loop defined by the covariation of the thigh, shank and foot elevation angles. A correlation analysis has been carried out between these features and classical gait spatial parameters (step length, step width, stride length and foot clearance) by training a linear regressor on the dataset comprising both speeds. The results from this analysis have highlighted a correlation with some spatial gait parameters across the two speed conditions, indicating that this compact description of kinematics unravels a significant biomechanical meaning. These results can be exploited to guide the control mechanisms of external assistive devices, such as prostheses or exoskeletons, based purely on the measurement of few relevant kinematic quantities of the lower limb segments
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
An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization
No full textMuscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps
Optimal Identification of Muscle Synergies From Typical Sit-to-Stand Clinical Tests
No full textGoal: The goal of this manuscript is to investigate the optimal methods for extracting muscle synergies from a sit-to-stand test; in particular, the performance in identifying the modular structures from signals of different length is characterized. Methods: Surface electromyography signals have been recorded from instrumented sit-to-stand trials. Muscle synergies have then been extracted from signals of different duration (i.e. 5 times sit to stand and 30 seconds sit to stand) from different portions of a complete sit-to-stand-to-sit cycle. Performance have then been characterized using cross-validation procedures. Moreover, an optimal method based on a modified Akaike Information Criterion measure is applied on the signal for selecting the correct number of synergies from each trial. Results: Results show that it is possible to identify correctly muscle synergies from relatively short signals in a sit-to-stand experiment. Moreover, the information about motor control structures is identified with a higher consistency when only the sit-to-stand phase of the complete cycle is considered. Conclusions: Defining a set of optimal methods for the extraction of muscle synergies from a clnical test such as the sit-to-stand is of key relevance to ensure the applicability of any synergy-related analysis in the clinical practice, without requiring knowledge of the technical signal processing methods and the underlying features of the signal
Classifying reaching height through muscle synergies in unconstrained scenarios
No full textMuscle synergy analysis has been widely adopted in the literature for the analysis of upper limb surface electromyographic signals during reaching tasks and for the prediction of movement direction for myoelectric control purposes. However, previous studies have characterized movements in constrained or semi-constrained scenarios, in which the subjects performing the movement were instructed to reach particular targets or were given some kind of feedback. In this work, the same synergy model has been applied to a completely unconstrained upper limb reaching experiment, with the aim of classifying the height of the target starting from the activity of the synergies. Results show that the synergistic model is able to extract compact features that can identify with good performance three different reaching heights. Moreover, this representation is able to isolate the signals that contain predictive information about the movement direction from the ones that are related to movement timing; this, together with the good performance of the synergy-based classifier supports the proposal of applying this model to the pre-processing of electromyographic signals when dealing with control systems that use signals from multiple muscles to predict movements
Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds
No full textThe accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects’ step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm
Effect of SNR normalization on the estimation of muscle synergies from EMG datasets
No full textMuscle Synergies Analysis (MSA) has gained significant attention in the last two decades as a method to derive motor control strategies and biomarkers of motor impairment and recovery. MSA is usually performed using the non-negative matrix factorization algorithm (NMF) and critically depends on the selection of the order of the model used in the analysis. The most common methodologies for the selection of the model order utilize arbitrary thresholds on the quality of reconstruction. However, the quality of the EMG data analyzed critically affects the model order selection. Here we present a simulative study on the effect of the SNR of the EMG in the selection of the model order for MSA using an objective criterion based on the Akaike Information Criterion (AIC). We show that low data quality leads to an overestimation of the muscle synergies. We then present a methodology for normalizing the SNR of the EMG before MSA. This methodology is based on the detection of the muscle activation intervals followed by a modification of the variance of the interburst noise. By applying this technique to a dataset collected during isometric reaching movements, we show that SNR normalization leads to a decrease in overall AIC values and in the optimal model order for synergies extraction
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
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