Computational Prediction of Muscle Moments During ARED Squat Exercise on the International Space Station

The human microbiome in space: parallels between Earth-based dysbiosis, implications for long-duration spaceflight, and possible mitigation strategies

SUMMARYThe human microbiota encompasses the diverse communities of microorganisms that reside in, on, and around various parts of the human body, such as the skin, nasal passages, and gastrointestinal tract. Although research is ongoing, it is well established that the microbiota exert a substantial influence on the body through the production and modification of metabolites and small molecules. Disruptions in the composition of the microbiota-dysbiosis-have also been linked to various negative health outcomes. As humans embark upon longer-duration space missions, it is important to understand how the conditions of space travel impact the microbiota and, consequently, astronaut health. This article will first characterize the main taxa of the human gut microbiota and their associated metabolites, before discussing potential dysbiosis and negative health consequences. It will also detail the microbial changes observed in astronauts during spaceflight, focusing on gut microbiota composition and pathogenic virulence and survival. Analysis will then turn to how astronaut health may be protected from adverse microbial changes via diet, exercise, and antibiotics before concluding with a discussion of the microbiota of spacecraft and microbial culturing methods in space. The implications of this review are critical, particularly with NASA's ongoing implementation of the Moon to Mars Architecture, which will include weeks or months of living in space and new habitats.

Movement in low gravity environments (MoLo) programme-The MoLo-L.O.O.P. study protocol

BACKGROUND

Exposure to prolonged periods in microgravity is associated with deconditioning of the musculoskeletal system due to chronic changes in mechanical stimulation. Given astronauts will operate on the Lunar surface for extended periods of time, it is critical to quantify both external (e.g., ground reaction forces) and internal (e.g., joint reaction forces) loads of relevant movements performed during Lunar missions. Such knowledge is key to predict musculoskeletal deconditioning and determine appropriate exercise countermeasures associated with extended exposure to hypogravity.

OBJECTIVES

The aim of this paper is to define an experimental protocol and methodology suitable to estimate in high-fidelity hypogravity conditions the lower limb internal joint reaction forces. State-of-the-art movement kinetics, kinematics, muscle activation and muscle-tendon unit behaviour during locomotor and plyometric movements will be collected and used as inputs (Objective 1), with musculoskeletal modelling and an optimisation framework used to estimate lower limb internal joint loading (Objective 2).

METHODS

Twenty-six healthy participants will be recruited for this cross-sectional study. Participants will walk, skip and run, at speeds ranging between 0.56-3.6 m/s, and perform plyometric movement trials at each gravity level (1, 0.7, 0.5, 0.38, 0.27 and 0.16g) in a randomized order. Through the collection of state-of-the-art kinetics, kinematics, muscle activation and muscle-tendon behaviour, a musculoskeletal modelling framework will be used to estimate lower limb joint reaction forces via tracking simulations.

CONCLUSION

The results of this study will provide first estimations of internal musculoskeletal loads associated with human movement performed in a range of hypogravity levels. Thus, our unique data will be a key step towards modelling the musculoskeletal deconditioning associated with long term habitation on the Lunar surface, and thereby aiding the design of Lunar exercise countermeasures and mitigation strategies.

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.

Influence of gravity on biomechanics in flywheel squat and leg press

Resistance exercise on Earth commonly involves both body weight and external load. When developing exercise routines and devices for use in space, the absence of body weight is not always adequately considered. This study compared musculoskeletal load distribution during two flywheel resistance knee-extension exercises, performed in the direction of (vertical squat; S) or perpendicular to (horizontal leg press; LP) the gravity vector. Eleven participants performed these two exercises at a given submaximal load. Motion analysis and musculoskeletal modelling were used to compute joint loads and to simulate a weightless situation. The flywheel load was more than twice as high in LP as in S (p < 0.001). Joint moments and forces were greater during LP than during S in the ankle, hip and lower back (p < 0.01) but were similar in the knee. In the simulated weightless situation, hip and lower-back loadings in S were higher than corresponding values at Earth gravity (p ≤ 0.01), whereas LP joint loads did not increase. The results suggest that LP is a better terrestrial analogue than S for knee-extension exercise in weightlessness and that the magnitude and direction of gravity during resistance exercise should be considered when designing and evaluating countermeasure exercise routines and devices for space.

Functional resistance training during walking: Mode of application differentially affects gait biomechanics and muscle activation patterns

BACKGROUND

Task-specific loading of the limbs-termed as functional resistance training-is commonly used in gait rehabilitation; however, the biomechanical and neuromuscular effects of various forms of functional resistance training have not been studied systematically. This information is crucial for correctly selecting the appropriate mode of functional resistance training when treating individuals with gait disorders.

RESEARCH QUESTION

To comprehensively evaluate the biomechanical (i.e., joint moment and power) and muscle activation changes with different forms of functional resistance training that are commonly used in clinics and research using biomechanical simulation-based analyses.

METHODS

We developed simulations of functional resistance training during walking using OpenSim (Gait2354, 23 degrees of freedom and 54 muscles) and custom MATLAB scripts. We investigated five modes of functional resistance training that have been commonly used in clinics or in research: (1) a weight attached at the ankle, (2) an elastic band attached at the ankle, (3) a viscous device attached to the hip and knee, (4) a weight attached at the pelvis, and (5) a constant backwards pulling force at the pelvis. Lower-extremity joint moments and powers were computed using inverse dynamics and muscle activations were estimated using computed muscle control while walking with each device under multiple resistance levels: normal walking with no resistance, and walking with 30, 60, and 90 Newtons of resistance.

RESULTS

The results indicate that the way in which resistance is applied during gait training differentially affects the internal joint moments, powers, and muscle activations as well as the joints and phase of the gait cycle where the resistance was experienced.

SIGNIFICANCE

The results highlight the importance of understanding the joints and muscles that are targeted by various modes of functional resistance training and carefully choosing the best mode of training that meets the specific therapeutic needs of the patient.

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.