Genomic Effects of Biomechanical Loading in Adolescent Human Growth Plate Cartilage: A Pilot Study.

Objective: The genomic effects of biomechanical loading on human growth plate cartilage are unknown so far. To address this, we used rare human growth plate biopsies obtained from children undergoing epiphysiodesis and exposed them to precisely controlled mechanical loading using a microloading device. The biopsies were cultured 24 hours after mechanical loading, followed by RNA-sequencing analyses to decipher the genomic regulation.

Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds.

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled.

Soft ankle exoskeleton to counteract dropfoot and excessive inversion.

Introduction: Wearable exoskeletons are emerging technologies for providing movement assistance and rehabilitation for people with motor disorders. In this study, we focus on the specific gait pathology dropfoot, which is common after a stroke. Dropfoot makes it difficult to achieve foot clearance during swing and heel contact at early stance and often necessitates compensatory movements.

Springs vs. motors: Ideal assistance in the lower limbs during walking at different speeds.

Recent years have witnessed breakthroughs in assistive exoskeletons; both passive and active devices have reduced metabolic costs near preferred walking speed by assisting muscle actions. Metabolic reductions at multiple speeds should thus also be attainable. Musculoskeletal simulation can potentially predict the interaction between assistive moments, muscle-tendon mechanics, and walking energetics.

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.

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.

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.

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.

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.

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.

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.

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.

Evaluation of musculoskeletal models, scaling methods, and performance criteria for estimating muscle excitations and fiber lengths across walking speeds.

Muscle-driven simulations have been widely adopted to study muscle-tendon behavior; several generic musculoskeletal models have been developed, and their biofidelity improved based on available experimental data and computational feasibility. It is, however, not clear which, if any, of these models accurately estimate muscle-tendon dynamics over a range of walking speeds. In addition, the interaction between model selection, performance criteria to solve muscle redundancy, and approaches for scaling muscle-tendon properties remain unclear.

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.

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning.

In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN).

Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning.

Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning.

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.

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

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.

Comparison of Joint and Muscle Biomechanics in Maximal Flywheel Squat and Leg Press.

The aim was to compare the musculoskeletal load distribution and muscle activity in two types of maximal flywheel leg-extension resistance exercises: horizontal leg press, during which the entire load is external, and squat, during which part of the load comprises the body weight. Nine healthy adult habitually strength-training individuals were investigated. Motion analysis and inverse dynamics-based musculoskeletal modelling were used to compute joint loads, muscle forces, and muscle activities.

The Effect of Step Width on Muscle Contributions to Body Mass Center Acceleration During the First Stance of Sprinting.

Background: At the beginning of a sprint, the acceleration of the body center of mass (COM) is driven mostly forward and vertically in order to move from an initial crouched position to a more forward-leaning position. Individual muscle contributions to COM accelerations have not been previously studied in a sprint with induced acceleration analysis, nor have muscle contributions to the mediolateral COM accelerations received much attention. This study aimed to analyze major lower-limb muscle contributions to the body COM in the three global planes during the first step of a sprint start.

A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion.

Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them.

Influence of loading direction due to physical activity on proximal femoral growth tendency.

Longitudinal bone growth is regulated by mechanical forces arising from physical activity, whose directions and magnitudes depend on activity kinematics and intensity. This study aims to investigate the influence of common physical activities on proximal femoral morphological tendency due to growth at the femoral head growth plate. A subject-specific femur model based on magnetic resonance images of one able-bodied 6-year old child was developed, and the directions of hip contact force were described as load samples at a constant magnitude.

Modeling and Simulation of a Human Knee Exoskeleton's Assistive Strategies and Interaction.

Exoskeletons are increasingly used in rehabilitation and daily life in patients with motor disorders after neurological injuries. In this paper, a realistic human knee exoskeleton model based on a physical system was generated, a human-machine system was created in a musculoskeletal modeling software, and human-machine interactions based on different assistive strategies were simulated. The developed human-machine system makes it possible to compute torques, muscle impulse, contact forces, and interactive forces involved in simulated movements.

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 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), 1 step width and block phase performance. Three-dimensional kinematic data (250 Hz) were collected from eleven competitive sprinters (100 m PB: 11.17 ± 0.