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1. Model Reveals Joint Properties for Which Co-contracting Antagonist Muscles Increases Joint Stiffness.

   Kudyba, Isabella M.; Szczecinski, Nicholas S. (August 2023, Biomimetic and Biohybrid Systems. Living Machines 2023.)

   Meder, F.; Hunt, A.; Margheri, L.; Mura, A.; Mazzolai, B. (Ed.)

   A challenge in robotics is to control interactions with the environment by modulating the stiffness of a manipulator’s joints. Smart servos are controlled with proportional feedback gain that is analogous to torsional stiffness of an animal’s joint. In animals, antagonistic muscle groups can be temporarily coactivated to stiffen the joint to provide greater opposition to external forces. However, the joint properties for which coactivation increases the stiffness of the joint remain unknown. In this study, we explore possible mechanisms by building a mathematical model of the stick insect tibia actuated by two muscles, the extensor and flexor tibiae. Muscle geometry, passive properties, and active properties are extracted from the literature. Joint stiffness is calculated by tonically activating the antagonists, perturbing the joint from its equilibrium angle, and calculating the restoring moment generated by the muscles. No reflexes are modeled. We estimate how joint stiffness depends on parallel elastic element stiffness, the shape of the muscle activation curve, and properties of the force-length curve. We find that co-contracting antagonist muscles only stiffens the joint when the peak of the force-length curve occurs at a muscle length longer than that when the joint is at equilibrium and the muscle force versus activation curve is concave-up. We discuss how this information could be applied to the control of a smart servo actuator in a robot leg. 

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2. The force response of muscles to activation and length perturbations depends on length history

   https://doi.org/10.1242/jeb.243991  

   Jeong, Siwoo; Nishikawa, Kiisa (February 2023, Journal of Experimental Biology)

   ABSTRACT Recent studies have demonstrated that muscle force is not determined solely by activation under dynamic conditions, and that length history has an important role in determining dynamic muscle force. Yet, the mechanisms for how muscle force is produced under dynamic conditions remain unclear. To explore this, we investigated the effects of muscle stiffness, activation and length perturbations on muscle force. First, submaximal isometric contraction was established for whole soleus muscles. Next, the muscles were actively shortened at three velocities. During active shortening, we measured muscle stiffness at optimal muscle length (L0) and the force response to time-varying activation and length perturbations. We found that muscle stiffness increased with activation but decreased as shortening velocity increased. The slope of the relationship between maximum force and activation amplitude differed significantly among shortening velocities. Also, the intercept and slope of the relationship between length perturbation amplitude and maximum force decreased with shortening velocity. As shortening velocities were related to muscle stiffness, the results suggest that length history determines muscle stiffness and the history-dependent muscle stiffness influences the contribution of activation and length perturbations to muscle force. A two-parameter viscoelastic model including a linear spring and a linear damper in parallel with measured stiffness predicted history-dependent muscle force with high accuracy. The results and simulations support the hypothesis that muscle force under dynamic conditions can be accurately predicted as the force response of a history-dependent viscoelastic material to length perturbations. 

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3. Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling

   https://doi.org/10.1115/1.4063899  

   Tahmid, Shadman; Font-Llagunes, Josep M; Yang, James (January 2024, Journal of Biomechanical Engineering)

   Abstract Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29). 

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4. Some Challenges of Playing with Power: Does Complex Energy Flow Constrain Neuromuscular Performance?

   https://doi.org/10.1093/icb/icz108  

   Roberts, Thomas J. (June 2019, Integrative and Comparative Biology)

   Abstract Many studies of the flow of energy between the body, muscles, and elastic elements highlight advantages of the storage and recovery of elastic energy. The spring-like action of structures associated with muscles allows for movements that are less costly, more powerful and safer than would be possible with contractile elements alone. But these actions also present challenges that might not be present if the pattern of energy flow were simpler, for example, if power were always applied directly from muscle to motions of the body. Muscle is under the direct control of the nervous system, and precise modulation of activity can allow for finely controlled displacement and force. Elastic structures deform under load in a predictable way, but are not under direct control, thus both displacement and the flow of energy act at the mercy of the mechanical interaction of muscle and forces associated with movement. Studies on isolated muscle-tendon units highlight the challenges of controlling such systems. A carefully tuned activation pattern is necessary for effective cycling of energy between tendon and the environment; most activation patterns lead to futile cycling of energy between tendon and muscle. In power-amplified systems, “elastic backfire” sometimes occurs, where energy loaded into tendon acts to lengthen active muscles, rather than accelerate the body. Classic models of proprioception that rely on muscle spindle organs for sensing muscle and joint displacement illustrate how elastic structures might influence sensory feedback by decoupling joint movement from muscle fiber displacements. The significance of the complex flow of energy between muscles, elastic elements and the body for neuromotor control is worth exploring. 

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5. Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model

   https://doi.org/10.1115/1.4056255  

   Tahmid, Shadman; Font-Llagunes, Josep M.; Yang, James (June 2023, Journal of Computing and Information Science in Engineering)

   Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results. 

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