High Performance Wearable Ultrasound as a Human-Machine Interface for wrist and hand kinematic tracking

Sgambato, Bruno Grandi; Hasbani, Milia; Barsakcioglu, Deren Y.; Ibáñez, Jaime; Jakob, Anette; Fournelle, Marc; Tang, Meng-Xing; Farina, Dario

doi:10.36227/techrxiv.22127435

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…The most basic way is by assigning joint angles to each DoF by using the function 'load mov' (Figure 3a). The joint angles can be obtained from motion capture data (like in [34]) or be measured from sensors (as by data glove 1 and angle sensors). There are 24 DoFs in the ARMs model and each can be changed separately within a predefined range.…”

Section: Movements Are Simulated By Using the Arms Model In Neuromoti...mentioning

confidence: 99%

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Ma,

Guerra,

Caillet

et al. 2023

Preprint

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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, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three 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, such as 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. Here we also provide representative applications of NeuroMotion. We first show how simulated 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 regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate 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 full-spectrum model will complement experimental approaches and facilitate neuromechanical research.Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

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Section: Movements Are Simulated By Using the Arms Model In Neuromoti...mentioning

confidence: 99%

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Ma,

Guerra,

Caillet

et al. 2023

Preprint

Self Cite

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show abstract

“…In contrast to conventional medical systems, wearable US devices should enable wireless data transmission of multiple channels over a period of several hours of continuous and unobtrusive monitoring during human movement without impairing range of motion. [6][7][8] These advancements have expanded the potential of US technology to new application areas, which were previously impossible to envision with larger, bulkier systems. [6,9,10] On the other hand, piezoelectric US transducers have experienced an impetus due to the development of thin-film technology and its integration into skin-adherent hydrogel, [9] or elastomer matrix.…”

Section: Introductionmentioning

confidence: 99%

Fully Printed Flexible Ultrasound Transducer for Medical Applications

Keller

Leitner

Baumgartner

et al. 2023

Adv Materials Technologies

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The fabrication of a fully printed, lead‐free, polymer piezoelectric transducer is presented and the characterization of its structural, dielectric, and ferroelectric properties at different processing stages is demonstrated. The performance of poly(vinylidene fluoride‐trifluoroethylene) transducers with resonance frequency analyses, acoustic power measurements, and pulse‐echo experiments is evaluated. Notably, for the first time for a fully printed transducer, an optimal performance in the medical ultrasound range (<15 MHz) is demonstrated with acoustic power >1 W cm−2, which is promising for applications in epidermal and wearable electronics. Overall, the findings provide a strong foundation for future research in the area of flexible ultrasound transducers.

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High Performance Wearable Ultrasound as a Human-Machine Interface for wrist and hand kinematic tracking

Cited by 2 publications

References 35 publications

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Fully Printed Flexible Ultrasound Transducer for Medical Applications

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