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.

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.

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.

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.

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.

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.

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.

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.

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.

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. This study aims to compare estimated muscle excitations and muscle fiber lengths, qualitatively and quantitatively, from several model combinations to experimental observations. We tested three generic models proposed by Hamner et al., Rajagopal et al., and Lai-Arnold et al. in combination with performance criteria based on minimization of muscle effort to the power of 2, 3, 5, and 10, and four approaches to scale the muscle-tendon unit properties of maximum isometric force, optimal fiber length, and tendon slack length. We collected motion analysis and electromyography data in eight able-bodied subjects walking at seven speeds and compared agreement between estimated/modelled muscle excitations and observed muscle excitations from electromyography and computed normalized fiber lengths to values reported in the literature. We found that best agreement in on/off timing in vastus lateralis, vastus medialis, tibialis anterior, gastrocnemius lateralis, gastrocnemius medialis, and soleus was estimated with minimum squared muscle effort than to higher exponents, regardless of model and scaling approach. Also, minimum squared or cubed muscle effort with only a subset of muscle-tendon unit scaling approaches produced the best time-series agreement and best estimates of the increment of muscle excitation magnitude across walking speeds. There were discrepancies in estimated fiber lengths and muscle excitations among the models, with the largest discrepancy in the Hamner et al. model. The model proposed by Lai-Arnold et al. best estimated muscle excitation estimates overall, but failed to estimate realistic muscle fiber lengths, which were better estimated with the model proposed by Rajagopal et al. No single model combination estimated the most accurate muscle excitations for all muscles; commonly observed disagreements include onset delay, underestimated co-activation, and failure to estimate muscle excitation increments across walking speeds.

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). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

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. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error ≤ 0.14 Nm/kg, normalized root mean square error ≤ 8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.

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 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. We also investigated the influence of step width on muscle contributions in both naturally wide sprint starts (natural trials) and in sprint starts in which the step width was restricted (narrow trials). METHOD: Motion data from four competitive sprinters (2 male and 2 female) were collected in their natural sprint style and in trials with a restricted step width. An induced acceleration analysis was performed to study the contribution from eight major lower limb muscles (soleus, gastrocnemius, rectus femoris, vasti, gluteus maximus, gluteus medius, biceps femoris, and adductors) to acceleration of the body COM. RESULTS: In natural trials, soleus was the main contributor to forward (propulsion) and vertical (support) COM acceleration and the three vasti (vastus intermedius, lateralis and medialis) were the main contributors to medial COM acceleration. In the narrow trials, soleus was still the major contributor to COM propulsion, though its contribution was considerably decreased. Likewise, the three vasti were still the main contributors to support and to medial COM acceleration, though their contribution was lower than in the natural trials. Overall, most muscle contributions to COM acceleration in the sagittal plane were reduced. At the joint level, muscles contributed overall more to COM support than to propulsion in the first step of sprinting. In the narrow trials, reduced COM propulsion and particularly support were observed compared to the natural trials. CONCLUSION: The natural wide steps provide a preferable body configuration to propel and support the COM in the sprint starts. No advantage in muscular contributions to support or propel the COM was found in narrower step widths.

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. Total exercise load (resultant ground reaction force; rGRF) and the knee-extension net joint moment (NJM) were slightly and considerably greater, respectively, in squat than in leg press (p ≤ 0.04), whereas the hip-extension NJM was moderately greater in leg press than in squat (p = 0.03). Leg press was performed at 11° deeper knee-flexion angle than squat (p = 0.01). Quadriceps muscle activity was similar in squat and leg press. Both exercise modalities showed slightly to moderately greater force in the vastii muscles during the eccentric than concentric phase of a repetition (p ≤ 0.05), indicating eccentric overload. That the quadriceps muscle activity was similar in squat and leg press, while rGRF and NJM about the knee were greater in squat than leg press, may, together with the finding of a propensity to perform leg press at deeper knee angle than squat, suggest that leg press is the preferable leg-extension resistance exercise, both from a training efficacy and injury risk perspective.

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. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.

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. Assistive strategies modeled as a rotational actuator, a simple pendulum model, and a damped pendulum model were applied to the knee exoskeleton during simulated normal and fast gait. We found that the rotational actuator-based assistive controller could reduce the user's required physiological knee extensor torque and muscle impulse by a small amount, which suggests that joint rotational direction should be considered when developing an assistive strategy. Compared to the simple pendulum model, the damped pendulum model based controller made little difference during swing, but further decreased the user's required knee flexor torque during late stance. The trade-off that we identified between interaction forces and physiological torque, of which muscle impulse is the main contributor, should be considered when designing controllers for a physical exoskeleton system. Detailed information at joint and muscle levels provided in this human-machine system can contribute to the controller design optimization of assistive exoskeletons for rehabilitation and movement assistance.

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. Finite element analysis was performed to predict growth rate and growth direction, and expected changes in neck-shaft angle and femoral anteversion were computed corresponding to circa 4 months of growth. For most loading conditions, neck-shaft angle and femoral anteversion decreased during growth, corresponding to the femur's natural course during normal growth. The largest reduction in neck-shaft angle and femoral anteversion was approximately 0.25° and 0.15°. Our results suggest that most common physical activities induce the expected morphological changes in normal growth in able-bodied children. Understanding the influence of contact forces during less common activities on proximal femoral development might provide improved guidelines and treatment planning for children who have or are at risk of developing a femoral deformity.