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

Improving movement decoding performance under joint constraints based on a neural-driven musculoskeletal model

Electromyography-driven musculoskeletal model (E-DMM) connects the user's control commands with the joint positions from a physiological perspective. However, features extracted directly from the surface EMG signals may be affected by signal crosstalk and amplitude cancellation. This limitation can be addressed with the decomposition algorithms for high-density (HD) EMG signals, which demonstrate the capability of extracting neural drives for the human-machine interface. On this basis, we proposed a neural-driven musculoskeletal model (N-DMM) with improved movement decoding performance for estimating wrist and metacarpophalangeal (MCP) joint positions under joint constraints. Eight limb-intact subjects participated in the experiment of mirrored bilateral training. The wrist and MCP joints of the subjects on one side were constrained, and the HD EMG signals from the same side were recorded. Moreover, the unconstrained side mirrored the joint movements of the phantom limb, while the joint angles were measured simultaneously. The obtained EMG signals were processed with the fast independent component analysis algorithm to extract motor unit discharges, enabling the estimation of neural drives. Then the neural drives were taken as inputs for the N-DMM to estimate joint movements. For comparison, an E-DMM was also employed for joint angle prediction. The results indicated that our N-DMM demonstrated superior performance compared to the E-DMM, potentially allowing for more accurate and robust decoding of continuous movements under joint constraints. Further improvement of the proposed model could offer a promising approach for practical applications in amputees.

Hierarchical Optimization for Personalized Hand and Wrist Musculoskeletal Modeling and Motion Estimation

OBJECTIVE

Surface electromyography (sEMG) driven musculoskeletal models are promising to be applied in the field of human-computer interaction. However, due to the individual-specific physiological characteristics, generic models often fail to provide accurate motion estimation. This study optimized the general model to build a personalized model and improve the accuracy of motion estimation.

METHODS

Inspired by the coupling effect of wrist/hand movement, a hierarchical optimization approach for personalizing musculoskeletal models (HOPE-MM) is proposed, which aligns with the physiological characteristics of the human wrist and hand. To verify the effectiveness of personalized musculoskeletal model, single joint motions and simultaneous joint motions are estimated. In addition, Sobol sensitivity analysis is conducted to identify the key parameters of musculoskeletal model, providing guidance for model simplification.

RESULTS

The mean pearson correlation coefficient between the predicted joint angles and the measured joint angles are 0.95 0.03 and 0.93 0.01 for simultaneous wrist and metacarpophalangeal (MCP) joint movements, respectively, which have a significant improvement compared with the state-of-the-art works. By optimizing only the key parameters including tendon slack length, maximal isometric force and optimal fiber length, the performances of simplified model are comparable to the full-parameter model.

CONCLUSION

These results provide insights into the effects of muscle-tendon parameters on musculoskeletal model, and musculoskeletal models personalized using hierarchical optimization methods can improve the accuracy of motion estimates.

SIGNIFICANCE

These findings facilitate the clinical application of musculoskeletal models in rehabilitation and robotic control.

Wrist torque estimation by combining motor unit discharges with musculoskeletal model

OBJECTIVE

The application of electromyography (EMG) decomposition techniques in myoelectric control has gradually increased. However, most decomposition-based control schemes rely on machine learning, lacking interpretation of the biological mechanisms underlying movement generation and requiring large datasets for training. As neuromusculoskeletal modeling provides a promising alternative, this study proposes a decomposition-based musculoskeletal model for simultaneous and proportional myoelectric control.

METHODS

Sixteen able-bodied subjects participated in two experiments involving isometric wrist contractions in two degrees of freedom (DoF). High-density surface EMG signals and torques were recorded simultaneously. The EMG signals were decomposed into motor unit action potential trains (MUAPts). We proposed four clustering methods (two activation-based and two action potential-based) to group MUAPts, from which three neural features were extracted as neural excitations and input to the musculoskeletal model.

MAIN RESULTS

An activation-based clustering method with the twitch feature achieved a relatively high accuracy (R2 = 0.791 ± 0.101 and 0.622 ± 0.148 in the two experiments) with the highest smoothness (Roughness = 1.389 ± 0.211 and 1.140 ± 0.159).

CONCLUSION AND SIGNIFICANCE

The proposed MUAPt-based musculoskeletal model achieved promising accuracy in estimating continuous 2-DoF wrist torques, providing a novel approach for understanding the neuromechanical properties of multi-DoF movements and advancing the development of dexterous rehabilitation and robotic control.

Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8×8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error ( 0.10 ± 0.02 ) and the highest determination coefficient ( 0.87 ± 0.06 ). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Muscle Strength Identification Based on Isokinetic Testing and Spine Musculoskeletal Modeling

Subject-specific spinal musculoskeletal modeling can help understand the spinal loading mechanism during human locomotion. However, existing literature lacks methods to identify the maximum isometric strength of individual spinal muscles. In this study, a muscle strength identification method combining isokinetic testing and musculoskeletal simulations was proposed, and the influence of muscle synergy and intra-abdominal pressure (IAP) on identified spinal muscle strength was further discussed. A multibody dynamic model of the spinal musculoskeletal system was established and controlled by a feedback controller. Muscle strength parameters were adjusted based on the measured isokinetic moments, and muscle synergy vectors and the IAP piston model were further introduced. The results of five healthy subjects showed that the proposed method successfully identified the subject-specific spinal flexor/extensor strength. Considering the synergistic activations of antagonist muscles improved the correlation between the simulated and measured spinal moments, and the introduction of IAP slightly increased the identified spinal extensor strength. The established method is beneficial for understanding spinal loading distributions for athletes and patients with sarcopenia.

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Consistent control information driven musculoskeletal model for multiday myoelectric control

Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.

A Review of Myoelectric Control for Prosthetic Hand Manipulation

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.