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Objective: Accurate, non-invasive methods for estimating joint and muscle physiological states have the potential to greatly enhance control of wearable devices during real-world ambulation. Traditional modeling approaches and current estimation methods used to predict muscle dynamics often rely on complex equipment or computationally intensive simulations and have difficulty estimating across a broad spectrum of tasks or subjects. Methods: Our approach used deep learning (DL) models trained on kinematic inputs to estimate internal physiological states at the knee, including moment, power, velocity, and force. We assessed each model's performance against ground truth labels from both a commonly used, standard OpenSim musculoskeletal model without EMG (static optimization) and an EMG-informed method (CEINMS), across 28 different cyclic and noncyclic tasks. Results: EMG provided no benefit for joint moment/power estimation (e.g., biological moment), but was critical for estimating muscle states. Models trained with EMG-informed labels but without EMG as an input to the DL system significantly outperformed models trained without EMG (e.g., 33.7% improvement for muscle moment estimation) (p < 0.05). Models that included EMG-informed labels and EMG as a model input demonstrated even higher performance (49.7% improvement for muscle moment estimation) (p < 0.05), but require the availability of EMG during model deployment, which may be impractical. Conclusion/Significance: While EMG information is not necessary for estimating joint level states, there is a clear benefit during muscle level state estimation. Our results demonstrate excellent tracking of these states with EMG included only during training, highlighting the practicality of real-time deployment of this approach.
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Simplified Cost Functions Meet Advanced Muscle Models to Streamline Muscle Force Estimation
Background/Objectives: This study explores an optimization-based strategy for muscle force estimation by employing simplified cost functions integrated with physiologically relevant muscle models. Methods: Considering elbow flexion as a case study, we employ an inverse-dynamics approach to estimate muscle forces for the biceps brachii, brachialis, and brachioradialis, utilizing different combinations of cost functions and muscle constitutive models. Muscle force generation is modeled by accounting for active and passive contractile behavior to varying degrees using Hill-type models. In total, three separate cost functions (minimization of total muscle force, mechanical work, and muscle stress) are evaluated with each muscle force model to represent potential neuromuscular control strategies without relying on electromyography (EMG) data, thereby characterizing the interplay between muscle models and cost functions. Results: Among the evaluated models, the Hill-type muscle model that incorporates both active and passive properties, combined with the stress minimization cost function, provided the most accurate predictions of muscle activation and force production for all three arm flexor muscles. Our results, validated against existing biomechanical data, demonstrate that even simplified cost functions, when paired with detailed muscle models, can achieve high accuracy in predicting muscle forces. Conclusions: This approach offers a versatile, EMG-free alternative for estimating muscle recruitment and force production, providing a more accessible and adaptable tool for muscle force analysis. It has profound implications for enhancing rehabilitation protocols and athletic training, not only broadening the applicability of muscle force estimation in clinical and sports settings but also paving the way for future innovations in biomechanical research.
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- Award ID(s):
- 2145615
- PAR ID:
- 10575166
- Publisher / Repository:
- Biomed
- Date Published:
- Journal Name:
- BioMed
- Volume:
- 4
- Issue:
- 3
- ISSN:
- 2673-8430
- Page Range / eLocation ID:
- 350 to 365
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Hill-type muscle models are highly preferred as phenomenological models for musculoskeletal simulation studies despite their introduction almost a century ago. The use of simple Hill-type models in simulations, instead of more recent cross-bridge models, is well justified since computationally ‘light-weight’—although less accurate—Hill-type models have great value for large-scale simulations. However, this article aims to invite discussion on numerical instability issues of Hill-type muscle models in simulation studies, which can lead to computational failures and, therefore, cannot be simply dismissed as an inevitable but acceptable consequence of simplification. We will first revisit the basic premises and assumptions on the force–length and force–velocity relationships that Hill-type models are based upon, and their often overlooked but major theoretical limitations. We will then use several simple conceptual simulation studies to discuss how these numerical instability issues can manifest as practical computational problems. Lastly, we will review how such numerical instability issues are dealt with, mostly in an ad hoc fashion, in two main areas of application: musculoskeletal biomechanics and computer animation.more » « less
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Abstract Objective.Transcutaneous electrical stimulation of peripheral nerves is a common technique to assist or rehabilitate impaired muscle activation. However, conventional stimulation paradigms activate nerve fibers synchronously with action potentials time-locked with stimulation pulses. Such synchronous activation limits fine control of muscle force due to synchronized force twitches. Accordingly, we developed a subthreshold high-frequency stimulation waveform with the goal of activating axons asynchronously.Approach.We evaluated our waveform experimentally and through model simulations. During the experiment, we delivered continuous subthreshold pulses at frequencies of 16.67, 12.5, or 10 kHz transcutaneously to the median and ulnar nerves. We obtained high-density electromyographic (EMG) signals and fingertip forces to quantify the axonal activation patterns. We used a conventional 30 Hz stimulation waveform and the associated voluntary muscle activation for comparison. We modeled stimulation of biophysically realistic myelinated mammalian axons using a simplified volume conductor model to solve for extracellular electric potentials. We compared the firing properties under kHz and conventional 30 Hz stimulation.Main results.EMG activity evoked by kHz stimulation showed high entropy values similar to voluntary EMG activity, indicating asynchronous axon firing activity. In contrast, we observed low entropy values in EMG evoked by conventional 30 Hz stimulation. The muscle forces evoked by kHz stimulation also showed more stable force profiles across repeated trials compared with 30 Hz stimulation. Our simulation results provide direct evidence of asynchronous firing patterns across a population of axons in response to kHz frequency stimulation, while 30 Hz stimulation elicited synchronized time-locked responses across the population.Significance.We demonstrate that the continuous subthreshold high-frequency stimulation waveform can elicit asynchronous axon firing patterns, which can lead to finer control of muscle forces.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/biomed4030028
