Usefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait

Muscle synergy and kinematic synergy analyses during sit-to-stand motions in hallux valgus patients before and after treatment with Kinesio taping

OBJECTIVES

To explore the impact of hallux valgus (HV) on lower limb neuromuscular control strategies during the sit-to-stand (STS) movement, and to evaluate the effects of Kinesio taping (KT) intervention on these control strategies in HV patients.

METHODS

We included 14 young healthy controls (HY), 13 patients in the HV group (HV), and 11 patients in the HV group (HVI) who underwent a Kinesio taping (KT) intervention during sit-to-stand (STS) motions. We extracted muscle and kinematic synergies from EMG and motion capture data using non-negative matrix factorization (NNMF). In addition, we calculated the center of pressure (COP) and ground reaction forces (GRF) to assess balance performance.

RESULTS

There were no significant differences in the numbers of muscle and kinematic synergies between groups. In the HV group, knee flexors and ankle plantar flexors were abnormally activated, and muscle synergy D was differentiated. Muscle synergy D was not differentiated in the HVI group.

CONCLUSION

Abnormal activation of knee flexors and plantar flexors led to the differentiation of module D in HV patients, which can be used as an indicator of the progress of HV rehabilitation. KT intervention improved motor control mechanisms in HV patients.

Utilising dynamic motor control index to identify age-related differences in neuromuscular control

PURPOSE

Considering the relationship between aging and neuromuscular control decline, early detection of age-related changes can ensure that timely interventions are implemented to attenuate or restore neuromuscular deficits. The dynamic motor control index (DMCI), a measure based on variance accounted for (VAF) by one muscle synergy (MS), is a metric used to assess age-related changes in neuromuscular control. The aim of the study was to investigate the use of one-synergy VAF, and consecutively DMCI, in assessing age-related changes in neuromuscular control over a range of exercises with varying difficulty.

METHODS

Thirty-one subjects walked on a flat and inclined treadmill, as well as performed forward and lateral stepping up tasks. Motion and muscular activity were recorded, and muscle synergy analysis was conducted using one-synergy VAF, DMCI, and number of synergies.

RESULTS

Difference between older and younger group was observed for one-synergy VAF, DMCI for forward stepping up task (one-synergy VAF difference of 2.45 (0.22, 4.68) and DMCI of 9.21 (0.81, 17.61), p = 0.033), but not for lateral stepping up or walking.

CONCLUSION

The use of VAF based metrics and specifically DMCI, rather than number of MS, in combination with stepping forward exercise can provide a low-cost and easy to implement approach for assessing neuromuscular control in clinical settings.

Speed-dependent modulations of muscle modules in the gait of people with radiographical and asymptomatic knee osteoarthritis and elderly controls: Case-control pilot study

This study investigates the muscle modules involved in the increase of walking speed in radiographical and asymptomatic knee osteoarthritis (KOA) patients using tensor decomposition. The human body possesses redundancy, which is the property to achieve desired movements with more degrees of freedom than necessary. The muscle module hypothesis is a proposed solution to this redundancy. While previous studies have examined the pathological muscle activity modulations in musculoskeletal diseases such as KOA, they have focused on single muscles rather than muscle modules. Moreover, most studies have only examined the gait of KOA patients at a single speed, leaving the way in which gait speed affects gait parameters in KOA patients unclear. Assessing this influence is crucial for determining appropriate gait speed and understanding why preferred gait speed decreases in KOA patients. In this study, we apply tensor decomposition to muscle activity data to extract muscle modules in KOA patients and elderly controls during walking at different speeds. We found a muscle module comprising hip adductors and back muscles that activate bimodally in a gait cycle, specific to KOA patients when they increase their walking speed. These findings may provide valuable insights for rehabilitation for KOA patients.

Changes in walking function and neural control following pelvic cancer surgery with reconstruction

Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.

Human Pose Estimation for Clinical Analysis of Gait Pathologies

Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is labor-intensive and heavily reliant on the expertise and judgment of the therapist. This study introduces a binary classification method for the quantitative assessment of gait impairments, specifically focusing on Duchenne muscular dystrophy (DMD), a prevalent and fatal neuromuscular genetic disorder. The research compares spatiotemporal and sagittal kinematic gait features derived from 2D and 3D human pose estimation trajectories against concurrently recorded 3D motion capture (MoCap) data from healthy children. The proposed model leverages a novel benchmark dataset, collected from YouTube and publicly available datasets of their typically developed peers, to extract time-distance variables (e.g. speed, step length, stride time, and cadence) and sagittal joint angles of the lower extremity (e.g. hip, knee, and knee flexion angles). Machine learning and deep learning techniques are employed to discern patterns that can identify children exhibiting DMD gait disturbances. While the current model is capable of distinguishing between healthy subjects and those with DMD, it does not specifically differentiate between DMD patients and patients with other gait impairments. Experimental results validate the efficacy of our cost-effective method, which relies on recorded RGB video, in detecting gait abnormalities, achieving a prediction accuracy of 96.2% for Support Vector Machine (SVM) and 97% for the deep network.

Understanding muscle coordination during gait based on muscle synergy and its association with symptoms in patients with knee osteoarthritis

OBJECTIVE

We aimed to investigate the muscle coordination differences between a control group and patients with mild and severe knee osteoarthritis (KOA) using muscle synergy analysis and determine whether muscle coordination was associated with symptoms of KOA.

METHOD

Fifty-three women with medial KOA and 19 control patients participated in the study. The gait analyses and muscle activity measurements of seven lower limb muscles were assessed using a motion capture system and electromyography. Gait speed and knee adduction moment impulse were calculated. The spatiotemporal components of muscle synergy were extracted using non-negative matrix factorization, and the dynamic motor control index during walking (walk-DMC) was computed. The number of muscle synergy and their spatiotemporal components were compared among the mild KOA, severe KOA, and control groups. Moreover, the association between KOA symptoms with walk-DMC and other gait parameters was evaluated using multi-linear regression analysis.

RESULTS

The number of muscle synergies was lower in mild and severe KOA compared with those in the control group. In synergy 1, the weightings of biceps femoris and gluteus medius in severe KOA were higher than that in the control group. In synergy 3, the weightings of higher tibial anterior and lower gastrocnemius lateralis were confirmed in the mild KOA group. Regression analysis showed that the walk-DMC was independently associated with knee-related symptoms of KOA after adjusting for the covariates.

CONCLUSIONS

Muscle coordination was altered in patients with KOA. The correlation between muscle coordination and KOA may be attributed to the knee-related symptoms. Key points • Patients with knee osteoarthritis (OA) experienced a deterioration in muscle coordination when walking. • Loss of muscle coordination was associated with severe knee-related symptoms in knee OA. • Considering muscle coordination as a knee OA symptom-related factor may provide improved treatment.

Role of kinesiophobia in the selective motor control during gait in patients with low back-related leg pain

Fear of movement has been related to changes in motor function in patients with low back pain, but little is known about how kinesiophobia affects selective motor control during gait (ability of muscles performing distinct mechanical functions) in patients with low back-related leg pain (LBLP). The aim of the study was to determine the association between kinesiophobia and selective motor control in patients with LBLP. An observational cross-sectional study was performed on 18 patients. Outcome included: kinesiophobia using the Tampa Scale of Kinesiophobia; pain mechanism using Leeds Assessment of Neuropathic Signs and Symptoms; disability using Roland-Morris Disability Questionnaire; mechanosensitivity using Straight Leg Raise. Surface electromyography was used to assess selective motor control during gait by examining the correlation and coactivation in muscle pairs involved in the stance phase. Pairs included vastus medialis (VM) and medial gastrocnemius (MG), causing opposite moments around the knee joint, and gluteus medius (GM) and MG, as muscles with distinct mechanical functions (weight acceptance vs. propulsion). A strong association was observed between kinesiophobia and correlation (r = 0.63; p = 0.005) and coactivation (r = 0.69; p = 0.001) between VM versus MG. A moderate association was observed between kinesiophobia and correlation (r = 0.58; p = 0.011) and coactivation (r = 0.55; p = 0.019) between GM versus MG. No significant associations were obtained for other outcomes. A high kinesiophobia is associated with low selective motor control of the muscles involved in the weight acceptance and propulsion phases during gait in patients with LBLP. Fear of movement was better associated with decreased neuromuscular control than other clinical variables such as pain mechanism, disability, and mechanosensitivity.

Muscle co-activation in the elderly contributes to control of hip and knee joint torque and endpoint force

We investigated the coordinated activity patterns of muscles based on cosine tuning in the elderly during an isometric force exertion task. We also clarified whether these coordinated activity patterns contribute to the control of hip and knee joint torque and endpoint force as co-activation. Preferred direction (PD) of activity for each muscle in 10 young and 8 older males was calculated from the lower limb muscle activity during isometric force exertion task in various directions. The covariance of endpoint force (η) was calculated from the exerted force data using a force sensor. Relationship between PD and η was used to examine the effect of muscle co-activation on the control of endpoint force. Co-activation between rectus femoris and semitendinosus/biceps femoris increased with changes in muscle PD. Additionally, the η values were significantly low, suggesting that co-activation of multiple muscles may contribute to endpoint force exertion. The mechanism for cooperative muscle activity is determined by the cosine tuning of the PD of each muscle, which affects the generation of hip and knee joint torque and endpoint force exertion. Co-activation of each muscle's PD changes with age, causing increased muscle co-activation to control torque and force. We demonstrated that co-activation in the elderly is a stabilizer of unsteady joints and a muscle control strategy for cooperative muscle activity.

Inter-muscular coordination during running on grass, concrete and treadmill

Running is an exercise that can be performed in different environments that imposes distinct foot-floor interactions. For instance, running on grass may help reducing instantaneous vertical impact loading, while compromising natural speed. Inter-muscular coordination during running is an important factor to understand motor performance, but little is known regarding the impact of running surface hardness on inter-muscular coordination. Therefore, we investigated whether inter-muscular coordination during running is influenced by running surface. Surface electromyography (EMG) from 12 lower limb muscles were recorded from young male individuals (n = 9) while running on grass, concrete, and on a treadmill. Motor modules consisting of weighting coefficients and activation signals were extracted from the multi-muscle EMG datasets representing 50 consecutive running cycles using non-negative matrix factorization. We found that four motor modules were sufficient to represent the EMG from all running surfaces. The inter-subject similarity across muscle weightings was the lowest for running on grass (r = 0.76 ± 0.11) compared to concrete (r = 0.81 ± 0.07) and treadmill (r = 0.78 ± 0.05), but no differences in weighting coefficients were found when analyzing the number of significantly active muscles and residual muscle weightings (p > 0.05). Statistical parametric mapping showed no temporal differences between activation signals across running surfaces (p > 0.05). However, the activation duration (% time above 15% peak activation) was significantly shorter for treadmill running compared to grass and concrete (p < 0.05). These results suggest predominantly similar neuromuscular strategies to control multiple muscles across different running surfaces. However, individual adjustments in inter-muscular coordination are required when coping with softer surfaces or the treadmill's moving belt.

The investigation of an analysis method for co-activation of knee osteoarthritis utilizing normalization of peak dynamic method

BACKGROUND

Assessing co-activation characteristics in knee osteoarthritis (knee OA) using method of quantification of the activity ratio (such as the co-contraction index (CCI) or the directed co-activation ratios (DCAR)) for surface electromyography (EMG) has been reported. However, no studies have discussed the differences in results between non-negative matrix factorization (NNMF) and the DCAR.

RESEARCH QUESTION

Does DCAR or NNMF reflect the characteristic co-activation pattern of knee OA while using EMG normalized by the peak dynamic method?

METHODS

Ten elderly control participants (EC) and ten knee OA patients (KOA) volunteered to participate in this study. EMG data from 20 participants were obtained from our previous study. Patients with knee OA were recruited from a local orthopedic clinic. The DCAR of agonist and antagonist muscles and the number of modules using NNMF were calculated to evaluate multiple muscle co-activations. An independent t-test statistical parametric mapping approach was used to compare the DCAR between the two groups. The difference in the number of modules between EC and KOA was evaluated using the Wilcoxon rank-sum test.

RESULTS

There was no significant difference in the DCAR between the two groups. However, NNMF had significantly fewer modules with KOA than with EC.

SIGNIFICANCE

The NNMF with the ratio of the amplitude of each muscle and duration of activity as variables reflected the co-activation of KOA, characterized by the high synchronous and prolonged activity of each muscle. Therefore, the NNMF is suitable for extracting characteristic muscle activity patterns of knee OA independent of the normalization method.

Applying muscle synergy analysis to forearm high-density electromyography of healthy people

Introduction

Muscle synergy is regarded as a motor control strategy deployed by the central nervous system (CNS). Clarifying the modulation of muscle synergies under different strength training modes is important for the rehabilitation of motor-impaired patients.

Methods

To represent the subtle variation of neuromuscular activities from the smaller forearm muscles during wrist motion, we proposed to apply muscle synergy analysis to preprocessed high-density electromyographic data (HDEMG). Here, modulation of muscle synergies within and across the isometric and isotonic training modes for strengthening muscles across the wrist were investigated. Surface HDEMGs were recorded from healthy subjects (N = 10). Three different HDEMG electrode configurations were used for comparison and validation of the extracted muscle synergies. The cosine of principal angles (CPA) and the Euclidian distance (ED) between synergy vectors were used to evaluate the intra- and inter-mode similarity of muscle synergies. Then, how the activation coefficients modulate the excitation of specific synergy under each mode was examined by pattern recognition. Next, for a closer look at the mode-specific synergies and the synergies shared by the two training modes, k-means clustering was applied.

Results

We observed high similarity of muscle synergies across different tasks within each training mode, but decreased similarity of muscle synergies across different training modes. Both intra- and intermode similarity of muscle synergies were consistently robust to electrode configurations regardless of the similarity metric used.

Discussion

Overall, our findings suggest that applying muscle synergy analysis to HDEMG is feasible, and that the traditional muscle synergies defined by whole-muscle components may be broadened to include sub-muscle components represented by the HDEMG channels. This work may lead to an appropriate neuromuscular analysis method for motor function evaluation in clinical settings and provide valuable insights for the prescription of rehabilitation training therapies.

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

Is modular control related to functional outcomes in individuals with knee osteoarthritis and following total knee arthroplasty?

BACKGROUND

Individuals who undergo total knee arthroplasty (TKA) for treatment of knee osteoarthritis often experience suboptimal outcomes. Investigation of neuromuscular control strategies in these individuals may reveal factors that contribute to these functional deficits. The purpose of this pilot study was to determine the relationship between patient function and modular control during gait before and after TKA.

METHODS

Electromyography data from 36 participants (38 knees) were collected from 8 lower extremity muscles on the TKA-involved limb during ≥5 over-ground walking trials before (n = 30), 6-months after (n = 26), and 24-months after (n = 13) surgery. Muscle modules were estimated using non-negative matrix factorization. The number of modules was determined from 500 resampled trials.

RESULTS

A higher number of modules was related to better performance-based and patient-reported function before and 6-months after surgery. Participants with organization similar to healthy, age-matched controls trended toward better function 24-months after surgery, though these results were not statistically significant. We also observed plasticity in the participants' modular control strategies, with 100% of participants who were present before and 24-months after surgery (10/10) demonstrating changes in the number of modules and/or organization of at least 1 module.

CONCLUSIONS

This pilot work suggests that functional improvements following TKA may initially present as increases in the number of modules recruited during gait. Subsequent improvements in function may present as improved module organization.

NOTEWORTHY

This work is the first to characterize motor modules in TKA both before and after surgery and to demonstrate changes in the number and organization of modules over the time course of recovery, which may be related to changes in patient function. The plasticity of modular control following TKA is a key finding which has not been previously documented and may be useful in predicting or improving surgical outcomes through novel rehabilitation protocols.

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

Muscle 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.