Real-time biofeedback of muscle forces should help clinicians adapt their movement recommendations. Because these forces cannot directly be measured, researchers have developed numerical models and methods informed by electromyography (EMG) and body kinematics to estimate them. Among these methods, static optimization is the most computationally efficient and widely used. However, it suffers from limitation, namely: unrealistic joint torques computation, non-physiological muscle forces estimates and inconsistent for motions inducing co-contraction. Forward approaches, relying on numerical optimal control, address some of these issues, providing dynamically consistent estimates of muscle forces. However, they result in a high computational cost increase, apparently disqualifying them for real-time applications. However, this computational cost can be reduced by combining the implementation of a moving horizon estimation (MHE) and advanced optimization tools. Our objective was to assess the feasibility and accuracy of muscle forces estimation in real-time, using a MHE. To this end, a 4-DoFs arm actuated by 19 Hill-type muscle lines of action was modeled for simulating a set of reference motions, with corresponding EMG signals and markers positions. Excitation- and activation-driven models were tested to assess the effects of model complexity. Four levels of co-contraction, EMG noise and marker noise were simulated, to run the estimator under 64 different conditions, 30 times each. The MHE problem was implemented with three cost functions: EMG-markers tracking (high and low weight on markers) and marker-tracking with least-squared muscle excitations. For the excitation-driven model, a 7-frame MHE was selected as it allowed the estimator to run at 24 Hz (faster than biofeedback standard) while ensuring the lowest RMSE on estimates in noiseless conditions. This corresponds to a 3,500-fold speed improvement in comparison to state-of-the-art equivalent approaches. When adding experimental-like noise to the reference data, estimation error on muscle forces ranged from 1 to 30 N when tracking EMG signals and from 8 to 50 N (highly impacted by the co-contraction level) when muscle excitations were minimized. Statistical analysis was conducted to report significant effects of the problem conditions on the estimates. To conclude, the presented MHE implementation proved to be promising for real-time muscle forces estimation in experimental-like noise conditions, such as in biofeedback applications.
Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).
This paper presents the MappEMG pipeline. The goal of this pipeline is to augment the traditional classical concert experience by giving listeners access, through the sense of touch, to an intimate and non-visible dimension of the musicians' bodily experience while performing. The live-stream pipeline produces vibrations based on muscle activity captured through surface electromyography (EMG). Therefore, MappEMG allows the audience to experience the performer's muscle effort, an essential component of music performance which is typically unavailable to direct visual observation. The paper is divided in four sections. First, we overview related works on EMG, music performance, and vibrotactile feedback. We then present conceptual and methodological issues of capturing musicians' muscle effort related to their expressive intentions. We further explain the different components of the live-stream data pipeline: a python software named Biosiglive for data acquisition and processing, a Max/MSP patch for data post-processing and mapping, and a mobile application named hAPPtiks for real-time control of smartphones' vibration. Finally, we address the application of the pipeline in an actual music performance. Thanks to their modular structure, the tools presented could be used in different creative and biomedical contexts involving gestural control of haptic stimuli.
Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorders, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control based on both direct multiple shooting and direct collocation, handling musculoskeletal models. Relying on algorithmic differentiation, Bioptim is fast and it interfaces several nonlinear solvers. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control.
biosiglive aims to provide a simple and efficient way to access and process biomechanical data in real time. It was conceived as user-friendly software aimed for both non-expert and expert programmers. The library uses interfaces to access data from several sources, such as motion capture software or any Python software development kit (SDK). Some interfaces are already implemented for Vicon Nexus motion capture (Oxford, UK) and Delsys electromyography SDK (EMG) (Boston, USA). That say, any additional interface can be added as custom interface using the abstract class. biosiglive was designed for biosignals, therefore, existing classes represent data collected from standard acquisition systems in biomechanics, such as markers for motion capture or EMG. Methods are available to process in real-time any input signal. Data can be saved in a binary file at each time frame to avoid any data loss in case of system shutdown. Data can also be displayed using the LivePlot class, which is based on PyQtGraph (C++ core) and allows, therefore, fast real-time displaying. Finally, 'biosiglive' was conceived as a flexible real-time data processing and streaming tool adaptable to various set-ups, software, and systems. Therefore, a TCP/IP connection module was implemented to send data to a distant port to be used by any other system.
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