1,720,986 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
Online Continuous Detection of Time-Varying Muscle Synergies
No full textMuscle synergies have been widely used as a compact description of the neuromuscular motor control strategies. The online detection of synergistic activations might therefore improve the feasibility of sEMG-based control algorithms. In this study, a simple online detector of time-varying muscle synergies activation timings is proposed, and its performance is evaluated in a simulated online scenario on a small sample of experimental signals
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
Smartphone-Based Answering to School Subject Questions Alters Gait in Young Digital Natives
No full textSmartphone texting while walking is a very common activity among people of different ages, with the so-called “digital natives” being the category most used to interacting with an electronic device during daily activities, mostly for texting purposes. Previous studies have shown how the concurrency of a smartphone-related task and walking can result in a worsening of stability and an increased risk of injuries for adults; an investigation of whether this effect can be identified also in people of a younger age can improve our understanding of the risks associated with this common activity. In this study, we recruited 29 young adolescents (12 ± 1 years) to test whether walking with a smartphone increases fall and injuries risk, and to quantify this effect. To do so, participants were asked to walk along a walkway, with and without the concurrent writing task on a smartphone; several different parameters linked to stability and risk of fall measures were then calculated from an inertial measurement unit and compared between conditions. Smartphone use determined a reduction of spatio-temporal parameters, including step length (from 0.64 ± 0.08 to 0.55 ± 0.06 m) and gait speed (1.23 ± 0.16 to 0.90 ± 0.16 m/s), and a general worsening of selected indicators of gait stability. This was found to be mostly independent from experience or frequency of use, suggesting that the presence of smartphone activities while walking may determine an increased risk of injury or falls also for a population that grew up being used to this concurrency
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
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
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
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
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
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