A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

2022 ◽  
Author(s):  
Jiamin Zhao ◽  
Yang Yu ◽  
Xu Wang ◽  
Shihan Ma ◽  
Xinjun Sheng ◽  
...  
Keyword(s):  
Surface Emg ◽  
Musculoskeletal Model ◽  
Muscle Synergy ◽  
Superior Performance ◽  
Model Driven ◽  
Regression Methods ◽  
Weighting Matrix ◽  
Online Test ◽  
Specific Muscle ◽  
Human Motions

Abstract Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Fall Detection via Inaudible Acoustic Sensing

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Jie Lian ◽  
Xu Yuan ◽  
Ming Li ◽  
Nian-Feng Tzeng
Keyword(s):  
Continuous Wave ◽  
Detection System ◽  
Fall Detection ◽  
Superior Performance ◽  
Medical Assistance ◽  
Acoustic Sensing ◽  
Value Decomposition ◽  
Human Motions ◽  
Audio Device ◽  
Fall Accidents

The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment.

scholarly journals A three-dimensional musculoskeletal model of the dog

2020 ◽  
Author(s):  
Heiko Stark ◽  
Martin S. Fischer ◽  
Alexander Hunt ◽  
Fletcher Young ◽  
Roger Quinn ◽  
...  
Keyword(s):  
Experimental Data ◽  
Body Size ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Musculoskeletal Model ◽  
Muscle Synergy ◽  
Locomotor System ◽  
Wide Range ◽  
Joint Load

AbstractDogs are an interesting object of investigation because of the wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. In vivo measurements/records of joint forces and loads or deep/small muscles are complex, invasive, and sometimes ethically questionable. The use of detailed musculoskeletal models may help in filling that knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: Three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. Predicted activation patterns exhibited good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence joint neuronal control and loading in dog locomotion.

scholarly journals Subject-Specific Muscle Synergies in Human Balance Control Are Consistent Across Different Biomechanical Contexts

2010 ◽  
Vol 103 (6) ◽  
pp. 3084-3098 ◽  
Author(s):  
Gelsy Torres-Oviedo ◽  
Lena H. Ting
Keyword(s):  
Muscle Activation ◽  
Balance Control ◽  
Nonnegative Matrix ◽  
The Body ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Support Surface ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Specific Muscle

The musculoskeletal redundancy of the body provides multiple solutions for performing motor tasks. We have proposed that the nervous system solves this unconstrained problem through the recruitment of motor modules or functional muscle synergies that map motor intention to action. Consistent with this hypothesis, we showed that trial-by-trial variations in muscle activation for multidirectional balance control in humans were constrained by a small set of muscle synergies. However, apparent muscle synergy structures could arise from characteristic patterns of sensory input resulting from perturbations or from low-dimensional optimal motor solutions. Here we studied electromyographic (EMG) responses for balance control across a range of biomechanical contexts, which alter not only the sensory inflow generated by postural perturbations, but also the muscle activation patterns used to restore balance. Support-surface translations in 12 directions were delivered to subjects standing in six different postural configurations: one-leg, narrow, wide, very wide, crouched, and normal stance. Muscle synergies were extracted from each condition using nonnegative matrix factorization. In addition, muscle synergies from the normal stance condition were used to reconstruct muscle activation patterns across all stance conditions. A consistent set of muscle synergies were recruited by each subject across conditions. When balance demands were extremely different from the normal stance (e.g., one-legged or crouched stance), task-specific muscle synergies were recruited in addition to the preexisting ones, rather generating de novo muscle synergies. Taken together, our results suggest that muscle synergies represent consistent motor modules that map intention to action, regardless of the biomechanical context of the task.

scholarly journals A musculoskeletal model driven by dual Microsoft Kinect Sensor data

2017 ◽  
Vol 41 (4) ◽  
pp. 297-316 ◽  
Author(s):  
Sebastian Skals ◽  
Kasper P. Rasmussen ◽  
Kaare M. Bendtsen ◽  
Jian Yang ◽  
Michael S. Andersen
Keyword(s):  
Musculoskeletal Model ◽  
Microsoft Kinect ◽  
Sensor Data ◽  
Kinect Sensor ◽  
Model Driven ◽  
Microsoft Kinect Sensor

scholarly journals Evidence for Shared Neural Information Between Muscle Synergies and Corticospinal Efficacy

2022 ◽  
Author(s):  
David R. Young ◽  
Caitlin L. Banks ◽  
Theresa E. McGuirk ◽  
Carolynn Patten
Keyword(s):  
Motor Evoked Potentials ◽  
Surface Emg ◽  
Muscle Synergy ◽  
Neural Function ◽  
Stroke Survivors ◽  
Analysis Technique ◽  
Gait Dysfunction ◽  
Neural Information ◽  
The Relationship

Abstract Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH) and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

scholarly journals A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

2020 ◽  
Vol 10 (12) ◽  
pp. 4439-4448
Author(s):  
Zigui Wang ◽  
Deborah Chapman ◽  
Gota Morota ◽  
Hao Cheng
Keyword(s):  
Association Studies ◽  
Bayesian Variable Selection ◽  
Bayesian Regression ◽  
Genome Wide Association ◽  
Superior Performance ◽  
Genome Wide Association Studies ◽  
Multiple Trait ◽  
Regression Methods ◽  
Genome Wide ◽  
Causal Structures

Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-Bayesian alphabet, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into multi-trait Bayesian regression methods. SEM-Bayesian alphabet provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait GWAS by performing GWAS based on indirect, direct and overall marker effects. The superior performance of SEM-Bayesian alphabet was demonstrated by comparing its GWAS results with other similar multi-trait GWAS methods on real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.

Muscle Synergy Constraints Improve Prediction of Knee Contact Force During Gait

2013 ◽  
Author(s):  
Benjamin J. Fregly ◽  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D’Lima ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Optimization Methods ◽  
Internal Force ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Patient Specific ◽  
Joint Contact ◽  
Specific Muscle ◽  
Clinical Conditions

Knowledge of patient-specific muscle and joint contact forces during activities of daily living could improve the treatment of movement-related disorders (e.g., osteoarthritis, stroke, cerebral palsy, Parkinson’s disease). Unfortunately, it is currently impossible to measure these quantities directly under common clinical conditions, and calculation of these quantities using computer models is limited by the redundant nature of human neural control (i.e., more muscles than theoretically necessary to actuate the available degrees of freedom in the skeleton). Walking is a particularly important task to understand, since loss of mobility is associated with increased morbidity and decreased quality of life [1]. Though numerous musculoskeletal computer modeling studies have used optimization methods to resolve the neural control redundancy problem, these estimates remain unvalidated due to the lack of internal force measurements that can be used for validation purposes.

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