Muscle activation patterns during gait: A hierarchical clustering analysis

An open-source toolbox for enhancing the assessment of muscle activation patterns during cyclical movements

Objective.The accurate temporal analysis of muscle activations is of great importance in several research areas spanning from the assessment of altered muscle activation patterns in orthopaedic and neurological patients to the monitoring of their motor rehabilitation. Several studies have highlighted the challenge of understanding and interpreting muscle activation patterns due to the high cycle-by-cycle variability of the sEMG data. This makes it difficult to interpret results and to use sEMG signals in clinical practice. To overcome this limitation, this study aims at presenting a toolbox to help scientists easily characterize and assess muscle activation patterns during cyclical movements.Approach.CIMAP(Clustering for the Identification of Muscle Activation Patterns) is an open-source Python toolbox based on agglomerative hierarchical clustering that aims at characterizing muscle activation patterns during cyclical movements by grouping movement cycles showing similar muscle activity.Main results.From muscle activation intervals to the graphical representation of the agglomerative hierarchical clustering dendrograms, the proposed toolbox offers a complete analysis framework for enabling the assessment of muscle activation patterns. The toolbox can be flexibly modified to comply with the necessities of the scientist.CIMAPis addressed to scientists of any programming skill level working in different research areas such as biomedical engineering, robotics, sports, clinics, biomechanics, and neuroscience. CIMAP is freely available on GitHub (https://github.com/Biolab-PoliTO/CIMAP).Significance.CIMAPtoolbox offers scientists a standardized method for analyzing muscle activation patterns during cyclical movements.

The Effects of Proprioceptive Neuromuscular Facilitation Pattern Kinesio Taping on Arm Swing, Balance, and Gait Parameters among Chronic Stroke Patients: A Randomized Controlled Trial

(1) Background: This study aimed to investigate the effects of proprioceptive neuromuscular facilitation pattern kinesio taping on arm swing, balance, and gait parameters among chronic stroke patients. (2) Methods: Twenty-eight participants were randomized into proprioceptive neuromuscular facilitation pattern kinesio taping during gait training (n = 14) and gait training (n = 14) groups. The proprioceptive neuromuscular facilitation pattern kinesio taping during gait training group employed proprioceptive neuromuscular facilitation pattern kinesio taping during 15 min treadmill-based gait training five times a week for four weeks, while the gait training group underwent the same gait training without proprioceptive neuromuscular facilitation pattern kinesio taping. Arm swing angle was measured using the Image J program, static balance was assessed with an AMTI force plate, dynamic balance was evaluated through the Timed Up and Go test, and gait parameters were recorded using the GAITRite system and the Dynamic Gait Index. (3) Results: After 4 weeks of training, the proprioceptive neuromuscular facilitation pattern kinesio taping during gait training group exhibited significant improvements in all variables compared to the baseline (p < 0.05), whereas the gait training group did not show statistically significant differences in any variables (p > 0.05). (4) Conclusions: This study demonstrates the effectiveness of proprioceptive neuromuscular facilitation pattern kinesio taping during gait training in enhancing arm swing angle, balance, and gait parameters.

Athlete Muscular Phenotypes Identified and Compared with High-Dimensional Clustering of Lower Limb Muscle Volume Measurements

INTRODUCTION

Athletes use their skeletal muscles to demonstrate performance. Muscle force generating capacity is correlated with volume, meaning that variations in sizes of different muscles may be indicative of how athletes meet different demands in their sports. Medical imaging enables in vivo quantification of muscle volumes; however, muscle volume distribution has not been compared across athletes of different sports.

PURPOSE

The goal of this work was to define "muscular phenotypes" in athletes of different sports and compare these using hierarchical clustering.

METHODS

Muscle volumes normalized by body mass of athletes (football, baseball, basketball, or track) were compared with control participants to quantify size differences using z -scores. z -Scores of 35 muscles described the pattern of volume deviation within each athlete's lower limb, characterizing their muscular phenotype. Data-driven high-dimensional clustering analysis was used to group athletes presenting similar phenotypes. Efficacy of clustering to identify similar phenotypes was demonstrated by grouping athletes' contralateral limbs before other athletes' limbs.

RESULTS

Analyses revealed that athletes did not tend to cluster with others competing in the same sport. Basketball players with similar phenotypes grouped by clustering also demonstrated similarities in performance. Clustering also identified muscles with similar volume variation patterns across athletes, and principal component analysis revealed specific muscles that accounted for most of the variance (gluteus maximus, sartorius, semitendinosus, vastus medialis, vastus lateralis, and rectus femoris).

CONCLUSIONS

Athletes exhibit heterogeneous lower limb muscle volumes that can be characterized and compared as individual muscular phenotypes. Clustering revealed that athletes with the most similar phenotypes do not always play the same sport such that patterns of muscular heterogeneity across a group of athletes reflect factors beyond their specific sports.

The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach

Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate body composition based on a weak electric current flow through the body. The measured voltage is used to calculate body bioelectrical impedance, divided into resistance and reactance, which can be used to estimate body parameters such as total body water (TBW), fat-free mass (FFM), fat mass (FM), and muscle mass (MM). This study aims to find the tendency of osteoporosis in obese subjects, presenting a method based on hierarchical clustering, which, using BIA parameters, can group patients who show homogeneous characteristics. Grouping similar patients into clusters can be helpful in the field of medicine to identify disorders, pathologies, or more generally, characteristics of significant importance. Another added value of the clustering process is the possibility to define cluster prototypes, i.e., imaginary patients who represent models of "states", which can be used together with clustering results to identify subjects with similar characteristics in a classification context. The results show that hierarchical clustering is a method that can be used to provide the detection of states and, consequently, supply a more personalized medicine approach. In addition, this method allowed us to elect BIA as a potential prognostic and diagnostic instrument in osteoporosis risk development.

Quantification and identification analysis of Ziziphus jujuba Mill. peel pigmentation at different developmental stages

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Lutein, β-carotene, chlorophyll a, chlorophyll b, and 13 anthocyanins were identified for the first time in fruit peel of a color mutant jujube cultivar-‘Sanbianhong’.

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Major distinctions in the metabolic profiles among the five different colored peel groups.

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Key color metabolites that contribute to the pigmentation in S1, S3, and S5 developmental stages were identified.

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The key color metabolites were quantified using standards as calibrators.

The fruit peel of a color mutant jujube cultivar, ‘Sanbianhong’ (SBF), was investigated using an ultra-high performance liquid chromatography quadrupole Orbitrap mass spectrometry (UHPLC-Q-Orbitrap MS) at five ripening stages (S1, Young fruit stage; S2, swelling stage; S3, white-mature stage; S4, pre-mature stage and S5, mature stage). Lutein, β-carotene, chlorophyll a, chlorophyll b, and 13 anthocyanins were identified. Chlorophyll a and cyanidin 3-O-galactoside were considered key color metabolites in S1 with the content of 1.083 mg/g of fresh weight (FW) and 4.585 mg/g of FW, respectively. Delphinidin (0.488 mg/g FW) and cyanidin (6.259 mg/g FW) were identified as the key pigments in S3. Delphinidin 3-O-glucoside (0.256 mg/g FW) was identified as the key anthocyanin in maturity S5. Herein, the identification and quantitation of pigment-related metabolites of SBF were studied for the first time, and the results provide a theoretical basis for understanding the pigment changes of jujube fruit during ripening.

Using K-Means Clustering in Python with Periodic Boundary Conditions

Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview

Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

The Effect of Number of Gait Cycles on Principal Activation Extraction

To cope with the high intra-subject variability of muscle activation intervals, a large amount of gait cycles is necessary to clearly document physiological or pathological muscle activity patterns during human locomotion. The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been proposed to help clinicians obtaining a synthetic and clear description of normal and pathological muscle functions in human walking. Moreover, this algorithm allows the extraction of Principal Activations (PAs), defined as those muscle activations that are strictly necessary to perform a specific task and occur in every gait cycle. This contribution aims at assessing the impact of the number of gait cycles on the extraction of the PAs. Results demonstrated no statistically significant differences between PAs extracted considering different numbers of gait cycles, revealing, on average, similarity values higher than 0.88.Clinical Relevance-This contribution demonstrates the potential applicability of the CIMAP algorithm to the analysis of subjects affected by neurological disorders, for whom the assessment of motor functions may be of the uttermost importance and only a reduced number of gait cycles can be acquired.

Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics.

Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements

Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.

A Statistical Approach for the Assessment of Muscle Activation Patterns during Gait in Parkinson’s Disease

Recently, the statistical analysis of muscle activation patterns highlighted that not only one, but several activation patterns can be identified in the gait of healthy adults, with different occurrence. Although its potential, the application of this approach in pathological populations is still limited and specific implementation issues need to be addressed. This study aims at applying a statistical approach to analyze muscle activation patterns of gait in Parkinson’s Disease, integrating gait symmetry and co-activation. Surface electromyographic signal of tibialis anterior and gastrocnemius medialis were recorded during a 6-min walking test in 20 patients. Symmetry between right and left stride time series was verified, different activation patterns identified, and their occurrence (number and timing) quantified, as well as the co-activation of antagonist muscles. Gastrocnemius medialis presented five activation patterns (mean occurrence ranging from 2% to 43%) showing, with respect to healthy adults, the presence of a first shorted and delayed activation (between flat foot contact and push off, and in the final swing) and highlighting a new second region of anticipated activation (during early/mid swing). Tibialis anterior presented five activation patterns (mean occurrence ranging from 3% to 40%) highlighting absent or delayed activity at the beginning of the gait cycle, and generally shorter and anticipated activations during the swing phase with respect to healthy adults. Three regions of co-contraction were identified: from heel strike to mid-stance, from the pre- to initial swing, and during late swing. This study provided a novel insight in the analysis of muscle activation patterns in Parkinson’s Disease patients with respect to the literature, where unique, at times conflicting, average patterns were reported. The proposed integrated methodology is meant to be generalized for the analysis of muscle activation patterns in pathologic subjects.

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.

An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation-while ignoring the inherent correlation in high-dimensional spaces-which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

Individual muscle contributions to hip joint-contact forces during walking in unilateral transfemoral amputees with osseointegrated prostheses

Direct skeletal attachment of prostheses in transfemoral amputees circumvents skin-interface complications associated with conventional sockets; however, joint pain and musculoskeletal disease is known to occur postoperatively. This study quantified hip contact forces and the roles of individual muscles in producing hip contact forces during walking in transfemoral amputees with osseointegrated prostheses. Musculoskeletal models were developed for four transfemoral amputees. Gluteus maximus and gluteus medius were the major contributors to the hip contact forces, and the intact limb hip muscles demonstrated greater contributions to hip contact forces than those of the residual limb. The findings may be useful for mitigating walking asymmetry.

How to Improve Robustness in Muscle Synergy Extraction

The muscle synergy theory was widely used in literature to assess the modular organization of the central nervous system (CNS) during human locomotion. The extraction of muscle synergies may be strongly influenced by the preprocessing techniques applied to surface electromyographic (sEMG) signals. The aim of this contribution is to assess the robustness improvement in muscle synergy extraction obtained using an innovative pre-processing technique with respect to the standard procedure. The new pre-processing technique that we propose is based on the extraction of principal muscle activation intervals (necessary to accomplish a specific biomechanical task during gait) from the original sEMG signals, discarding the secondary muscle activation intervals (activations that occur only in some strides with auxiliary functions). Results suggest that the extraction of the principal activation intervals from sEMG provide a more consistent and stable description of the modular organization of the CNS with respect to the standard pre-processing procedure.

Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach.

Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm

Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.

[Recognition and classification of posture and gait patterns of rollator users by distance measurements-a comparison between clinical assessment and automatic classification]

BACKGROUND

This article describes the development of an add-on module for wheeled walkers dedicated to sensor-based posture and gait pattern recognition with the goal to develop an everyday aid for fall prevention. The core contribution is a clinical study that compared single gait parameter assessments coming from medical staff to those obtained from an automatic classification algorithm, i. e. the Mahalanobis distance over time series of sensor measurements.

METHODS

The walker-module described here extends an off-the-shelf wheeled walker by two depth cameras that observe the torso, pelvic, region and legs of the user. From the stream of depth images, distance measurements to eight relevant feature points on the body surface (shoulders, iliac crests, upper and lower legs) are combined to time series that describe the individual gait cycles. For automatic classification of gait cycle descriptions 14 safety-relevant gait parameters (gait width, height, length, symmetry, variability; flection of torso, knees (l/r), hips (l/r); position, distance to walker; 2‑value, 5‑value gait patterns [While the two-value gait pattern differentiates a gait cycle into physiological and pathological, the five-value gait pattern distinguishes between antalgic, atactic, paretic, protective, and physiological gait]), single classifier algorithms were trained using machine learning techniques based on the mathematical concept of the Mahalanobis distance (distance of individual gait cycles to class averages and corresponding covariance matrices). For this purpose, training and test datasets were gathered in a clinical setting from 29 subjects. Here, the assessment of gait properties given by medical experts served for the labelling of sensorial gait cycle descriptions of the training and test datasets. In order to evaluate the quality of the automated classification in the add-on module a final comparison between human and automatic gait parameter assessment is given.

RESULTS

The gait assessment conducted by trained medical staff served as a comparator for the machine learning gait assessment and showed a relatively uniform class distribution of gait parameters over the group of probands, e. g. 57% showed an increased and 43% a normal distance to the walker. Of the subjects 51% positioned themselves central to the walker, while 41% took a left deviating, and 8% a right deviating position. A further 12 gait parameters were differentiated and evaluated in 2-5 classes. In the following, single gait cycle descriptions of each subject were assessed by trained classification algorithms. The best automatic classification rates over all subjects were given by the distance to walker (99.4%), and the 2-value gait pattern (99.2%). Gait variability (94.6%) and position to walker (94.2%) showed the poorest classification rates. Over all gait parameters and subjects, 96.9% of all gait cycle descriptions were correctly classified.

DISCUSSION/OUTLOOK

With an average classification rate of 96.9%, the described gait classification approach is well suited for a patient-oriented training correction system that informs the user about false posture during every day walker use. A second application scenario is the use in a clinical setting for objectifying the gait assessment of patients. To reach these ambitious goals requires more future research. It includes the replacement of depth cameras by small size distance sensors (1D Lidar), the design and implementation of a suitable walker-user interface, and the evaluation of the proposed classification algorithm by contrasting it to results of modern deep convolutional neural network output.

Asymmetry Index in Muscle Activations

Gait asymmetry is typically evaluated using spatio-temporal or joint kinematics parameters. Only a few studies addressed the problem of defining an asymmetry index directly based on muscle activity, extracting parameters from surface electromyography (sEMG) signals. Moreover, no studies used the extraction of the muscle principal activations (activations that are necessary for accomplishing a specific motor task) as the base to construct an asymmetry index, less affected by the variability of sEMG patterns. The aim of this paper is to define a robust index to quantitatively assess the asymmetry of muscle activations during locomotion, based on the extraction of the principal activations. SEMG signals were analyzed combining statistical gait analysis (SGA) and a clustering algorithm that allows for obtaining the muscle principal activations. We evaluated the asymmetry levels of four lower limb muscles in: (1) healthy subjects of different ages (children, adults, and elderly); (2) different populations of orthopedic patients (adults with megaprosthesis of the knee after bone tumor resection, elderly subjects after total knee arthroplasty, and elderly subjects after total hip arthroplasty); and (3) neurological patients (children with hemiplegic cerebral palsy and elderly subjects affected by idiopathic normal pressure hydrocephalus). The asymmetry index obtained for each pathological population was then compared to that of age-matched controls. We found asymmetry levels consistent with the expected impact of the different pathologies on muscle activation during gait. This suggests that the proposed index can be successfully used in clinics for an objective assessment of the muscle activation asymmetry during locomotion.

Co-activation patterns of gastrocnemius and quadriceps femoris in controlling the knee joint during walking

Muscular co-activation is a well-known mechanism for lower limb joint stabilization in both healthy and pathological individuals. This muscular feature appears particularly important for the knee joint, not only during challenging motor tasks such as cutting and landing but also during walking, due to knee cyclic loading. Gastrocnemius acts on the knee joint with a flexor activity and co-activations with quadriceps muscles lead to greater knee ligament strain with respect to an isolated burst of either muscle. Thus, this study aimed to assess possible co-activations between gastrocnemius and quadriceps muscles during walking. Five co-activation periods were assessed: during early stance (identified in 5.7 ± 5.1% of total strides), early and late foot-contact (88.9 ± 8.9% and 8.9 ± 8.2%), push-off (23.9 ± 12.2%) and late swing (29.0 ± 16.1%). Outcomes showed that late foot-contact and swing co-activations could deserve particular attention: in both cases the knee joint was close to the full extension (around 3.5° and 6°, respectively) and thus, considering also the anterior tibia translation due to the quadriceps activity, the simultaneous gastrocnemius burst could lead to an enhanced knee ligaments elongation. Findings of this study represent the first attempt to provide a reference knee joint co-activation framework, useful also for further evaluation in cohorts with knee failures.

Intra-Subject Consistency during Locomotion: Similarity in Shared and Subject-Specific Muscle Synergies

Human locomotion is a complex motor task. Previous research hypothesized that muscle synergies reflect the modular control of muscle groups operated by the Central Nervous System (CNS). Despite the high stride-to-stride variability characterizing human gait, most studies analyze only a few strides. This may be limiting, because the intra-subject variability of motor output is neglected. This gap could be filled by recording and analyzing many gait cycles during a single walking task. In this way, it can be investigated if CNS recruits the same muscle synergies consistently or if different strategies are adopted during the locomotion task. The aim of this work is to investigate the intra-subject consistency of muscle synergies during overground walking. Twelve young healthy volunteers were instructed to walk for 5 min at their natural pace. On the average, 181 ± 10 gait cycles were analyzed for each subject. Surface electromyography was recorded from 12 muscles of the dominant lower limb and the trunk. Gait cycles were grouped into subgroups containing 10 gait cycles each. The consistency of the muscle synergies extracted during the gait trial was assessed by measuring cosine similarity (CS) of muscle weights vectors, and zero-lag cross-correlation (CC) of activation signals. The average intra-subject CS and CC were 0.94 ± 0.10 and 0.96 ± 0.06, respectively. We found five synergies shared by all the subjects: high consistency values were found for these synergies (CS = 0.96 ± 0.05, CC = 0.97 ± 0.03). In addition, we found 10 subject-specific synergies. These synergies were less consistent (CS = 0.80 ± 0.20, CC = 0.89 ± 0.14). In conclusion, our results demonstrated that shared muscle synergies were highly consistent during walking. Subject-specific muscle synergies were also consistent, although to a lesser extent.

Influence of pre-processing in the extraction of muscle synergies during human locomotion

The extraction of muscle synergies in human locomotion may be biased by the kind of pre-processing applied to electromyographic (EMG) data. The aim of this contribution is to analyze the differences in the muscle synergies extracted using a standard pre-processing procedure and a new procedure. The new procedure is based on the selection of the muscle's principal activations (necessary actuations of the muscle to accomplish its specific biomechanical task during gait), discarding secondary activations (with an auxiliary function in motor control). EMG signals were recorded from 12 muscles of a healthy volunteer who was asked to walk, at self-selected pace, for 5 minutes. A dataset of 193 gait cycles was collected and divided into 19 epochs of 10 concatenated gait cycles. The application of the new pre-processing procedure provided 5 instead of 6 muscle synergies accurately reconstructing the original EMG data matrix, and clearer and more stable neural activation commands. The new preprocessing procedure may be easily extended to the extraction of muscle synergies in other cyclic movements, such as running, stair climbing, cyclo-ergometer exercising, and swimming.

Muscle contractions in cyclic movements: Optimization of CIMAP algorithm

During cyclic movements, the number of muscle activations and their timing are different from cycle to cycle. In a previous study, the CIMAP algorithm was proposed for grouping cycles showing similar EMG activation intervals, using dendrogram clustering. Even if the algorithm demonstrated good performances on a healthy population, the intra-cluster variability decreased when applied to datasets from pathological subjects. In this work we propose an optimized version of the CIMAP, comparing the performances of 8 different combinations of parameters used for the dendrogram construction. The cut-off point is also modified. The new and the original version of the algorithm are compared, in terms of intra-cluster variability, considering a population of 60 subjects, both healthy and pathological. The results show that the new CIMAP allows for obtaining clusters with lower variability with respect to the original version of the algorithm (p <; 0.001).

Estimation of joint position error

Joint position error (JPE) is frequently used to assess proprioception in rehabilitation and sport science. During position-reposition tests the subject is asked to replicate a specific target angle (e.g. 30° of knee flexion) for a specific number of times. The aim of this study is to find an effective method to estimate JPE from the joint kinematic signal. Forty healthy subjects were tested to assess knee joint position sense. Three different methods of JPE estimation are described and compared using a hierarchical clustering approach. Overall, the 3 methods showed a high degree of similarity, ranging from 88% to 100%. We concluded that it is preferable to use the more user-independent method, in which the operator does not have to manually place "critical" markers.