Quantification of muscle coordination underlying basic shoulder movements using muscle synergy extraction

Effects of Kitchen Cooking Height on Upper Limb Muscle Activation, Posture, and Perceived Discomfort of Chinese Older and Young Women

Inappropriate kitchen cooking height may lead to uncomfortable and muscle fatigue. This study aims to compare the effects of kitchen cooking height on upper limb muscle activation, posture, and perceived discomfort among different age groups. Fifteen older women and fifteen young Chinese women each completed three consecutive 20 s simulated cooking tasks at five different heights. Surface electromyography, motion capture, and Borg CR10 scale were used to measure muscle loading. Results showed that the main power muscles of the cooking task were the anterior deltoid, brachioradialis, and biceps brachii. The higher muscle contribution rate of biceps brachii and triceps brachii was found in the younger group compared to the older group (p < 0.05). Muscle activation of the anterior deltoid (different in 1.28-2.87%), pectoralis major (different in 1.43-1.69%), and erector spinae (different in 0.6-1.21%), as well as right shoulder abduction (different in 5.91°-7.96°), were significantly higher in older group than in young group (p < 0.05). Muscle activation of the anterior deltoid and right shoulder abduction decreased significantly with decreasing height (p < 0.05). A height of 200-250 mm below the elbow was considered a more comfortable cooking height for both age groups. This provides data to support the design of cabinet sizes.

Performance of a Novel Muscle Synergy Approach for Continuous Motion Estimation on Untrained Motion

When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motions performed by the user are different from the motions in the training stage of the model. It has been demonstrated that the prediction accuracy of the currently effective approaches on untrained motions will be significantly reduced. Therefore, we proposed a novel CME method by introducing muscle synergy as feature to achieve better prediction accuracy on untrained motion tasks. Specifically, deep non-smooth NMF (Deep-nsNMF) was firstly introduced on synergy extraction to improve the efficiency of synergy decomposition. After obtaining the activation primitives from various training motions, we proposed a redundancy classification algorithm (RC) to identify shared and task-specific synergies, optimizing the original redundancy segmentation algorithm (RS). NARX neural network was set as the regression model for training. Finally, the model was tested on prediction tasks of eight untrained motions. The prediction accuracy of the proposed method was found to perform better than using time-domain feature as input of the network. Through Deep-nsNMF with RS, the highest accuracy reached 99.7%. Deep-nsNMF with RC performed similarly well and its stability was relatively higher among different motions and subjects. Limitation of the approach is that the problem of positive correlation between the prediction error and the absolute value of real angle remains to be further addressed. Generally, this research demonstrates the potential for CME models to perform well in complex scenarios, providing the feasibility of dedicating CME to real-world applications.

Cost function criteria using muscle synergies: Exploring the potential of muscle synergy hypothesis

BACKGROUND AND OBJECTIVE

Solving the redundant optimization problem for human muscles depends on the cost function. Choosing the appropriate cost function helps to address a specific problem. Muscle synergies are currently limited to those obtained by electromyography. Furthermore, debate continues regarding whether muscle synergy is derived or real. This study proposes new cost functions based on the muscle synergy hypothesis for solving the optimal muscle force output problem through musculoskeletal modeling.

METHODS

We propose two new computational cost functions involving muscle synergies, which are extracted from muscle activations predicted by musculoskeletal modelling rather than electromyography. In this study, we constructed a musculoskeletal model for simulation using the "Grand Challenge Competition to Predict In Vivo Knee Loads" dataset. Muscle synergies were obtained using non-negative matrix factorization. Two cost functions with muscle synergies were constructed by integrating the polynomial and min/max criterion. Two new functions were verified and validated in normal, smooth, and bouncy gaits.

RESULTS

The muscle synergies based on normal gaits were classified into four modules. The cosine similarities of the first three modules were all >0.9. In the normal and smooth gaits, the forces in most muscles predicted using the two new functions were within three standard deviations of the root mean square error for electromyographic comparisons. Predicted muscle force curves using the four methods as well as characteristic points (i.e., time points in the gait cycle when the significant difference was observed between normal and bouncy gaits) were obtained to validate their predictive capabilities.

CONCLUSIONS

This study constructed two new cost functions involving muscle synergies, verified and validated the ability, and explored the potential of muscle synergy hypothesis.

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval

Introduction: Pain is a common health problem among older adults worldwide. Older adults tend to suffer from arm, lumbar, and back pain when using hanging cabinets. Methods: This study used surface electromyography to record muscle activity and a motion capture system to record joint motion to research effects of different loads and retrieval postures on muscle activity and joint range of motion when older adults retrieve objects from a high place, to provide optimised feedback for the design of hanging cabinet furniture. Results: We found that: 1) The activity of BB (Biceps brachii) on the side of the body interacting with the cabinet door was greater than that of UT (Upper trapezius) and BR (Brachial radius) when retrieving objects from a high place, the activity of UT on the side of the body interacting with a heavy object was greater than that of BB and BR. 2) The activity of UT decreases when the shoulder joint angle is greater than 90°, but the activity of BB increases as the angle increases. In contrast, increasing the object's mass causes the maximum load on the shoulder joint. 3) Among the different postures for overhead retrieval, alternating between the right and left hand is preferable for the overhead retrieval task. 4) Age had the most significant effect on overhead retrieval, followed by height (of person), and load changes were significantly different only at the experiment's left elbow joint and the L.BR. 5) Older adults took longer and exerted more effort to complete the task than younger adults, and static exercise in older adults may be more demanding on muscle activity in old age than powered exercise. Conclusion: These results help to optimise the design of hanging cabinet furniture. Regarding the height of hanging cabinets, 180 cm or less is required for regular retrieval movements if the human height is less than 150 cm. Concerning the depth of the hanging cabinets, different heights chose different comfort distances, which translated into the depth of the hanging cabinets; the greater the height, the greater the depth of the hanging cabinets to use.

Application of statistical parametric mapping for comparison of scapular kinematics and EMG

Scapular kinematics and EMG are frequently measured as a functional assessment of the shoulder. Previous studies have compared interval averaging for these time series data, but it is not clear whether this method exactly captures the dynamics of scapular kinematics and muscle activity. Statistical parametric mapping (SPM) can be used to compare time series data. The purpose of this study was to investigate whether there is a difference between the results of SPM and interval averaging (every 10° or 30°) in comparing scapular kinematics, EMG, and EMG ratio. Scapular kinematics and EMG of the upper trapezius (UT), middle trapezius (MT), and lower trapezius (LT) and serratus anterior (SA) were measured in 21 healthy males. Tasks included arm raising and lowering with or without load, and we compared scapular kinematics, EMG, and EMG ratio in the loaded and unloaded conditions. Results suggest disagreement between SPM and interval averaging. Characteristic results are that for scapular kinematics during lowering SPM showed a decrease in upward rotation in only the regions 113-65° and 42-30°, while interval averaging showed a decrease in all range. For EMG during lowering, SPM results were not significantly different in SA over 50-48 and 45-30°, while interval averaging suggested increased activity in all ranges. For EMG ratio during raising, SPM showed no significant difference, while interval averaging showed a decrease in UT/LT during the latter period. These results indicate that SPM provides better resolution regarding effect regions than interval averaging, and suggest that SPM may improve shoulder function assessment accuracy.

Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand

The hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.

Estimation of Time-Frequency Muscle Synergy in Wrist Movements

Muscle synergy analysis is a kind of modularized decomposition of muscles during exercise controlled by the central nervous system (CNS). It can not only extract the synergistic muscles in exercise, but also obtain the activation states of muscles to reflect the coordination and control relationship between muscles. However, previous studies have mainly focused on the time-domain synergy without considering the frequency-specific characteristics within synergy structures. Therefore, this study proposes a novel method, named time-frequency non-negative matrix factorization (TF-NMF), to explore the time-varying regularity of muscle synergy characteristics of multi-channel surface electromyogram (sEMG) signals at different frequency bands. In this method, the wavelet packet transform (WPT) is used to transform the time-scale signals into time-frequency dimension. Then, the NMF method is calculated in each time-frequency window to extract the synergy modules. Finally, this method is used to analyze the sEMG signals recorded from 8 muscles during the conversion between wrist flexion (WF stage) and wrist extension (WE stage) movements in 12 healthy people. The experimental results show that the number of synergy modules in wrist flexion transmission to wrist extension (Motion Conversion, MC stage) is more than that in the WF stage and WE stage. Furthermore, the number of flexor and extensor muscle synergies in the frequency band of 0–125 Hz during the MC stage is more than that in the frequency band of 125–250 Hz. Further analysis shows that the flexion muscle synergies mostly exist in the frequency band of 140.625–156.25 Hz during the WF stage, and the extension muscle synergies appear in the frequency band of 125–156.25 Hz during the WE stage. These results can help to better understand the time-frequency features of muscle synergy, and expand study perspective related to motor control in nervous system.