Lumbar Loads and Muscle Activity During Flywheel and Barbell Leg Exercises.

ABSTRACT: Sjöberg, M, Eiken, O, Norrbrand, L, Berg, HE, and Gutierrez-Farewik, EM. Lumbar loads and muscle activity during flywheel and barbell leg exercises. J Strength Cond Res 37(1): 27-34, 2023-It is anticipated that flywheel-based leg resistance exercise will be implemented in future long-duration space missions, to counter deconditioning of weight-bearing bones and postural muscles. The aim was to examine low back loads and muscle engagements during flywheel leg press (FWLP) and flywheel squat (FWS) and, for comparisons, free-weight barbell back squat (BBS). Eight resistance-trained subjects performed 8 repetition maximums of FWLP, FWS, and BBS. Motion analysis and inverse dynamics-based musculoskeletal modeling were used to compute joint loads and muscle forces. Muscle activities were measured with electromyography (EMG). At the L4-L5 level, peak vertebral compression force was similarly high in all exercise modes, whereas peak vertebral posteroanterior shear force was greater ( p < 0.05) in FWLP and BBS than in FWS. Among the back-extensor muscles, the erector spinae longissimus exerted the greatest peak force, with no difference between exercises. Peak force in the lumbar multifidus was lower ( p < 0.05) during FWLP than during FWS and BBS. Peak EMG activity in the lumbar extensor muscles ranged between 31 and 122% of maximal voluntary isometric contraction across muscles and exercise modes, with the greatest levels in the lumbar multifidus. The vertebral compression forces and muscle activations during the flywheel exercises were sufficiently high to presume that when implementing such exercise in space countermeasure regimens, they may be capable of preventing muscle atrophy and vertebral demineralization in the lumbar region.

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors-specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and -5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side's mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

The relationships between pelvic range of motion, step width and performance during an athletic sprint start.

The aims of this study were (a) to describe the kinematics underlying the phenomenon of the knee of the swing leg passing medially in front of the athlete during the single push (SP) phase of the block sprint start, and (b) to determine the relationships between block phase pelvis range of motion (RoM), 1st step width and block phase performance. Three-dimensional kinematic data (250 Hz) were collected from eleven competitive sprinters (100 m PB: 11.17 ± 0.41) performing maximal effort block starts. The joint angles of the rear hip with respect to the pelvis and the pelvis segment angles with respect to the laboratory coordinate system were calculated during the block start phase to the end of the 1st stance. A combination of pelvis list and rotation (not hip adduction) was coupled with the thigh of the swing leg moving medially during the SP phase. A very high positive correlation was found between pelvic list RoM and 1st step width (r = 0.799, p = 0.003). No other significant correlations were found. Attempting to reduce pelvic RoM or changing frontal and transverse plane hip joint angles to minimise medial thigh motion is unlikely to lead to an improvement to performance.

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network.

Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.

The effect of a reduced first step width on starting block and first stance power and impulses during an athletic sprint start.

This study investigated how manipulating first step width affects 3D external force production, centre of mass (CoM) motion and performance in athletic sprinting. Eight male and 2 female competitive sprinters (100m PB: 11.03 ± 0.36 s male and 11.6 ± 0.45 s female) performed 10 maximal effort block starts. External force and three-dimensional kinematics were recorded in both the block and first stance phases. Five trials were performed with the athletes performing their preferred technique (Skating) and five trials with the athletes running inside a 0.3 m lane (Narrow). By reducing step width from a mean of 0.31 ± 0.06 m (Skating) to 0.19 ± 0.03 m (Narrow), reductions were found between the two styles in medial block and medial 1st stance impulses, 1st stance anterior toe-off velocity and mediolateral motion of the CoM. No differences were found in block time, step length, stance time, average net resultant force vector, net anteroposterior impulse nor normalised external power. Step width correlated positively with medial impulse but not with braking nor net anteroposterior impulse. Despite less medially directed forces and less mediolateral motion of the CoM in the Narrow trials, no immediate improvement to performance was found by restricting step width.

Effects of Botulinum Toxin-A and Goal-Directed Physiotherapy in Children with Cerebral Palsy GMFCS Levels I & II.

AIMS: To evaluate short and long-term effects of botulinum toxin-A combined with goal-directed physiotherapy in children with cerebral palsy (CP). METHOD: A consecutive selection of 40 children, ages 4-12 years, diagnosed with unilateral or bilateral CP, and classified in GMFCS levels I-II. During the 24 months, 9 children received one BoNT-A injection, 10 children two injections, 11 children three injections, and 10 children received four injections. 3D gait analysis, goal-attainment scaling, and body function assessments were performed before and at 3, 12, and 24 months after initial injections. RESULTS: A significant but clinically small long-term improvement in gait was observed. Plantarflexor spasticity was reduced after three months and remained stable, while passive ankle dorsiflexion increased after 3 months but decreased slightly after 12 months. Goal-attainment gradually increased, reached the highest levels at 12 months, and levels were maintained at 24 months. CONCLUSION: The treatments' positive effect on spasticity reduction was identified, but did not relate to improvement in gait or goal-attainment. No long-term positive change in passive ankle dorsiflexion was observed. Goal attainment was achieved in all except four children. The clinical significance of the improved gait is unclear. Further studies are recommended to identify predictors for positive treatment outcome.

The role of visual stimuli on standing posture in children with bilateral cerebral palsy.

BACKGROUND: In children with bilateral cerebral palsy (CP) maintaining a standing position can be difficult. The fundamental motor task of standing independently is achieved by an interaction between the visual, somatosensory, and vestibular systems. In CP, the motor disorders are commonly accompanied by sensory and perceptual disturbances. Our aims were to examine the influence of visual stimuli on standing posture in relation to standing ability. METHODS: Three dimensional motion analysis with surface electromyography was recorded to describe body position, body movement, and muscle activity during three standing tasks: in a self-selected position, while blindfolded, and during an attention-demanding task. Participants were twenty-seven typically-developing (TD) children and 36 children with bilateral CP, of which 17 required support for standing (CP-SwS) and 19 stood without support (CP-SwoS). RESULTS: All children with CP stood with a more flexed body position than the TD children, even more pronounced in the children in CP-SwS. While blindfolded, the CP-SwS group further flexed their hips and knees, and increased muscle activity in knee extensors. In contrast, the children in CP-SwoS maintained the same body position but increased calf muscle activity. During the attention-demanding task, the children in CP-SwoS stood with more still head and knee positions and with less muscle activity. CONCLUSIONS: Visual input was important for children with CP to maintain a standing position. Without visual input the children who required support dropped into a further crouched position. The somatosensory and vestibular systems alone could not provide enough information about the body position in space without visual cues as a reference frame. In the children who stood without support, an intensified visual stimulus enhanced the ability to maintain a quiet standing position. It may be that impairments in the sensory systems are major contributors to the difficulties to stand erect in children with CP.

Influence of External Visual Focus on Gait in Children With Bilateral Cerebral Palsy.

PURPOSE: To explore whether focusing a target influenced gait in children with cerebral palsy (CP) and typical development (TD). METHODS: Thirty children with bilateral CP (Gross Motor Function Classification System [GMFCS] I-III) and 22 with TD looked at a light at walkway end (Gaze Target) while walking and returned (No Target). RESULTS: During Gaze versus No Target, children with TD reduced temporal-spatial parameters and movements in the sagittal (SPM) and transverse planes. In comparison, during Gaze Target, children in CP1 (GMFCS I) had larger trunk SPM, children in CP2 (GMFCS II) larger neck (SPM), and children in CP3 (GMFCS III) greater head and neck frontal plane movements, and reduced cadence and single support. CONCLUSIONS: Focusing a target altered gait in children with CP. Children in CP1 reduced movements similar to children with TD, children in CP2 behaved nearly unchanged, whereas children in CP3 reduced movements and temporal-spatial parameters, potentially as a consequence of lack of sensory information from lower limbs.

Gait dynamics in the wide spectrum of children with arthrogryposis: a descriptive study.

BACKGROUND: Arthrogryposis Multiplex Congenita (AMC) is a heterogeneous condition characterized by multiple joint contractures at birth. Greater movements in the trunk and pelvis during walking have been observed in children with AMC using orthoses compared to those wearing only shoes. This study investigated gait dynamics in children with AMC and identified compensatory mechanisms that accommodate walking. METHODS: Twenty-six children with AMC who walked with orthoses or shoes and a control group consisting of 37 typically-developing children were evaluated in 3D gait analysis. Children with AMC were divided into subgroups based on which joints needed to be stabilized in the sagittal plane; AMC1 used knee-ankle-foot orthoses (KAFOs) with locked knee joints, AMC2 used KAFOs with open knee joints or ankle-foot orthoses, and AMC3 used shoes. RESULTS: The Gait Deviation Index was lower in AMC groups than in the control group, with the lowest in AMC1. Excessive trunk movements in frontal and transverse planes were observed in AMC2 and especially in AMC1. Lower hip flexion moment was found in AMC1, while AMC2 and AMC3 showed similar hip flexion moments as the control group. Knee extension moments were similar between the groups. In the frontal plane there were only small differences between the groups in hip abduction moment. A joint work analysis indicated greater contribution from the hip muscles to overall positive work in AMC groups, particularly in AMC1, than in the control group. CONCLUSION: All AMC groups showed less hip extension than the control group, but hip flexion moment was significantly lower only in AMC1, which can be attributed to their gait strategy with bilateral locked KAFOs. AMC1, who had weak knee extensors, were helped by their locked KAFOs and therefore showed similar knee extension moment as the other groups. This finding, together with their gait patterns, demonstrates the children's high reliance on hip muscles and presumably trunk muscles to provide propulsion. Our study shows that with adequate orthotic support, children with AMC and even with severe weakness and contractures can achieve walking.

Postural orientation during standing in children with bilateral cerebral palsy.

PURPOSE: To investigate postural orientation and maintenance of joint position during standing in children with bilateral spastic cerebral palsy (BSCP). METHODS: Standing was examined with 3-D motion analysis in 26 children with BSCP, and 19 children typically developing (TD). Two groups of children with cerebral palsy (CP) were analyzed: 15 who were able to maintain standing without support and 11 who needed support. RESULTS: Children with CP stood with more flexion than children TD. In the CP groups, children standing without support stood more asymmetrically with less hip and knee flexion and less movement than those who required support. CONCLUSION: Children with CP had varying abilities to stand and maintain standing posture with or without support. Both CP groups stood with more flexion than their potential passive joint angle, more obvious in children requiring support. Investigations on how muscle strength and spatial perception influence posture remains to be explored.

Experiment-guided tuning of muscle-tendon parameters to estimate muscle fiber lengths and passive forces.

The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle-tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle-tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force-length relationships to match reported in vivo joint moment-angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus's operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles' excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle-tendon parameters easily and be adapted to incorporate more experimental data.

A Protocol for Comprehensive Analysis of Gait in Individuals with Incomplete Spinal Cord Injury.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.

Introduction: Recent advancements in reinforcement learning algorithms have accelerated the development of control models with high-dimensional inputs and outputs that can reproduce human movement. However, the produced motion tends to be less human-like if algorithms do not involve a biomechanical human model that accounts for skeletal and muscle-tendon properties and geometry. In this study, we have integrated a reinforcement learning algorithm and a musculoskeletal model including trunk, pelvis, and leg segments to develop control modes that drive the model to walk. Methods: We simulated human walking first without imposing target walking speed, in which the model was allowed to settle on a stable walking speed itself, which was 1.45 m/s. A range of other speeds were imposed for the simulation based on the previous self-developed walking speed. All simulations were generated by solving the Markov decision process problem with covariance matrix adaptation evolution strategy, without any reference motion data. Results: Simulated hip and knee kinematics agreed well with those in experimental observations, but ankle kinematics were less well-predicted. Discussion: We finally demonstrated that our reinforcement learning framework also has the potential to model and predict pathological gait that can result from muscle weakness.

Joint Kinematics, Kinetics and Muscle Synergy Patterns During Transitions Between Locomotion Modes.

There is an increasing demand for accurately predicting human movement intentions. To be effective, predictions must be performed as early as possible in the preceding step, though precisely how early has been studied relatively little; how and when a person's movement patterns in a transition step deviate from those in the preceding step must be clearly defined. In this study, we collected motion kinematics, kinetics and electromyography data from 9 able-bodied participants during 7 locomotion modes. Twelve types of steps between the 7 locomotion modes were studied, including 5 continuous steps (taking another step in the same locomotion mode) and 7 transitions steps (taking a step from one locomotion mode into another). For each joint degree of freedom, joint angles, angular velocities, moments, and moment rates were compared between continuous steps and transition steps, and the relative timing during the transition step at which these parameters diverged from those of a continuous step, which we refer to as transition starting times, were identified using multiple analyses of variance. Muscle synergies were also extracted for each step, and we studied in which locomotion modes these synergies were common (task-shared) and in which modes they were specific (task-specific). The transition starting times varied among different transitions and joint degrees of freedom. Most transitions started in the swing phase of the transition step. These findings can be applied to determine the critical timing at which a powered assistive device must adapt its control to enable safe and comfortable support to a user.

Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks.

Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A cluster approach that grouped movements based on characteristic similarity was implemented, and its ability to improve the estimation accuracy of both NMS and LSTM models was evaluated. We compared torque estimation accuracy of NMS and LSTM models in three cases: Pooled, Individual, and Clustered models. Pooled models used data from all 10 movements to calibrate or train one model, Individual models used data from each individual movement, and Clustered models used data from each cluster. Individual, Clustered and Pooled LSTM models all had relatively high joint torque estimation accuracy. Individual and Clustered NMS models had similarly good estimation performance whereas the Pooled model may be too generic to satisfy all movement patterns. While the cluster approach improved the estimation accuracy in NMS models in some movements, it made relatively little difference in the LSTM neural networks, which already had high estimation accuracy. Our study provides practical implications for designing assist-as-needed exoskeleton controllers by offering guidelines for selecting the appropriate model for different scenarios, and has potential to enhance the functionality of wearable exoskeletons and improve rehabilitation and assistance for individuals with motor disorders.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification.

Introduction: Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. Methods: To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Results: Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. Discussion: The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Gait pattern after electromechanically-assisted gait training with the Hybrid Assistive Limb and conventional gait training in sub-acute stroke rehabilitation-A subsample from a randomized controlled trial.

Introduction: Electromechanically-assisted gait training has been introduced in stroke rehabilitation as a means to enable gait training with a large number of reproducible and symmetrical task repetitions, i.e. steps. However, few studies have evaluated its impact on gait pattern functions. This study includes persons with no independent ambulation function at the start of a 4-week neurorehabilitation period in the sub-acute phase after stroke. The primary aim of the study was to evaluate whether the addition of electromechanically-assisted gait training to conventional training resulted in better gait pattern function than conventional training alone. The secondary aim was to identify correlations between overall gait quality and standardized clinical assessments. Participants and methods: Seventeen patients with no independent ambulation function who participated in a Prospective Randomized Open Blinded End-point study in the sub-acute phase after stroke were randomized into two groups; one group (n = 7) to undergo conventional training only (CONV group) and the other group (n = 10) to undergo conventional training with additional electromechanically-assisted gait training (HAL group). All patients were assessed with 3D gait analysis and clinical assessments after the 4-week intervention period. Overall gait quality as per the Gait Profile Score (GPS), as well as kinematic, and kinetic and other spatiotemporal metrics were collected and compared between intervention groups. Correlations between biomechanical and clinical outcomes were evaluated. Results: Both the CONV and HAL groups exhibited similar gait patterns with no significant differences between groups in any kinematic, kinetic parameters or other spatiotemporal metrics. The GPS for the paretic limb had a median (IQR) of 12.9° (7.8°) and 13.4° (4.3°) for the CONV and HAL groups, respectively (p = 0.887). Overall gait quality was correlated with independence in walking, walking speed, movement function and balance. We found no added benefit in gait pattern function from the electromechanically-assisted gait training compared to the conventional training alone. Discussion: This finding raises new questions about how to best design effective and optimal post-stroke rehabilitation programs in patients with moderate to severe gait impairments to achieve both independent walking and optimal gait pattern function, and about which patients should be in focus in further studies on the efficacy of electromechanically-assisted gait training. Clinical trial registration: The study was retrospectively registered at ClinicalTrials.gov, identifier (NCT02410915) on April 2015.

A Method of Detecting Human Movement Intentions in Real Environments.

Accurate and timely movement intention detection can facilitate exoskeleton control during transitions between different locomotion modes. Detecting movement intentions in real environments remains a challenge due to unavoidable environmental uncertainties. False movement intention detection may also induce risks of falling and general danger for exoskeleton users. To this end, in this study, we developed a method for detecting human movement intentions in real environments. The proposed method is capable of online self-correcting by implementing a decision fusion layer. Gaze data from an eye tracker and inertial measurement unit (IMU) signals were fused at the feature extraction level and used to predict movement intentions using 2 different methods. Images from the scene camera embedded on the eye tracker were used to identify terrains using a convolutional neural network. The decision fusion was made based on the predicted movement intentions and identified terrains. Four able-bodied participants wearing the eye tracker and 7 IMU sensors took part in the experiments to complete the tasks of level ground walking, ramp ascending, ramp descending, stairs ascending, and stair descending. The recorded experimental data were used to test the feasibility of the proposed method. An overall accuracy of 93.4% was achieved when both feature fusion and decision fusion were used. Fusing gaze data with IMU signals improved the prediction accuracy.

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.

Micromechanical Loading Studies in Ex Vivo Cultured Embryonic Rat Bones Enabled by a Newly Developed Portable Loading Device.

Mechanical loading has been described as having the potential to affect bone growth. In order to experimentally study the potential clinical applications of mechanical loading as a novel treatment to locally modulate bone growth, there is a need to develop a portable mechanical loading device enabling studies in small bones. Existing devices are bulky and challenging to transfer within and between laboratories and animal facilities, and they do not offer user-friendly mechanical testing across both ex vivo cultured small bones and in vivo animal models. To address this, we developed a portable loading device comprised of a linear actuator fixed within a stainless-steel frame equipped with suitable structures and interfaces. The actuator, along with the supplied control system, can achieve high-precision force control within the desired force and frequency range, allowing various load application scenarios. To validate the functionality of this new device, proof-of-concept studies were performed in ex vivo cultured rat bones of varying sizes. First, very small fetal metatarsal bones were microdissected and exposed to 0.4 N loading applied at 0.77 Hz for 30 s. When bone lengths were measured after 5 days in culture, loaded bones had grown less than unloaded controls (p < 0.05). Next, fetal rat femur bones were periodically exposed to 0.4 N loading at 0.77 Hz while being cultured ex vivo for 12 days. Interestingly, this loading regimen had the opposite effect on bone growth, i.e., loaded femur bones grew significantly more than unloaded controls (p < 0.001). These findings suggest that complex relationships between longitudinal bone growth and mechanical loading can be determined using this device. We conclude that our new portable mechanical loading device allows experimental studies in small bones of varying sizes, which may facilitate further preclinical studies exploring the potential clinical applications of mechanical loading.