Analysing the contributions of lower limb muscles to eccentric cycling using musculoskeletal modeling and simulation.

Eccentric (ECC) cycling, compared to traditional concentric cycling, has been shown to improve muscle strength and neuromuscular control at a lower metabolic cost. Despite the popularity of this exercise in the sports and rehabilitation contexts, there is a gap in our knowledge of which muscles are behaving eccentrically during ECC cycling. To this end, we used a musculoskeletal model and computer simulations to calculate joint kinematics and muscle lengths during ECC cycling. Movements were recorded using 3D motion capture technology while cycling eccentrically on a custom-built semi-recumbent ergometer. The software Opensim was used to calculate joint kinematics and muscle lengths from recorded movements. We found that among the primary knee extensors, it was predominantly the Vastii muscles that acted eccentrically in the ECC cycling phase, with other lower limb muscles showing mixed eccentric/concentric activation. Additionally, the muscle force-length and force-velocity factors in the ECC phase suggest that changes to the participant's pose and pedaling speed may elicit larger active muscle forces. Our work provides an interesting application of musculoskeletal modeling to ECC cycling, and an alternative way to help understand in-vivo muscle mechanics during this activity.

Experimental Evaluation of a Hybrid Sensory Feedback System for Haptic and Kinaesthetic Perception in Hand Prostheses.

This study proposes a new hybrid multi-modal sensory feedback system for prosthetic hands that can provide not only haptic and proprioceptive feedback but also facilitate object recognition without the aid of vision. Modality-matched haptic perception was provided using a mechanotactile feedback system that can proportionally apply the gripping force through the use of a force controller. A vibrotactile feedback system was also employed to distinguish four discrete grip positions of the prosthetic hand. The system performance was evaluated with a total of 32 participants in three different experiments (i) haptic feedback, (ii) proprioceptive feedback and (iii) object recognition with hybrid haptic-proprioceptive feedback. The results from the haptic feedback experiment showed that the participants' ability to accurately perceive applied force depended on the amount of force applied. As the feedback force was increased, the participants tended to underestimate the force levels, with a decrease in the percentage of force estimation. Of the three arm locations (forearm volar, forearm ventral and bicep), and two muscle states (relaxed and tensed) tested, the highest accuracy was obtained for the bicep location in the relaxed state. The results from the proprioceptive feedback experiment showed that participants could very accurately identify four different grip positions of the hand prosthesis (i.e., open hand, wide grip, narrow grip, and closed hand) without a single case of misidentification. In experiment 3, participants could identify objects with different shapes and stiffness with an overall high success rate of 90.5% across all combinations of location and muscle state. The feedback location and muscle state did not have a significant effect on object recognition accuracy. Overall, our study results indicate that the hybrid feedback system may be a very effective way to enrich a prosthetic hand user's experience of the stiffness and shape of commonly manipulated objects.

Assessment of biomedical engineering knowledge using true-false questions.

The COVID-19 pandemic has caused a shift from on-campus to remote online examinations, which are usually difficult to invigilate. Meanwhile, closed-ended question formats, such as true-false (TF), are particularly suited to these examination conditions, as they allow automatic marking by computer software. While previous studies have reported the score characteristics in TF questions in conventional supervised examinations, this study investigates the efficacy of using TF questions in online, unsupervised examinations at the undergraduate level of Biomedical Engineering. We examine the TF and other question-type scores of 57 students across three examinations held in 2020 under online, unsupervised conditions. Our analysis shows significantly larger coefficient of variance (CV) in scores in TF questions (42.7%) than other question types (22.3%). The high CV in TF questions may be explained by different answering strategies among students, with 13.3 ± 17.2% of TF questions left unanswered (zero marks) and 16.4 ± 11.5% of TF questions guessed incorrectly (negative marks awarded). In unsupervised, open-book examination where sharing of answers among students is a potential risk; questions that induce a larger variation in responses may be desirable to differentiate among students. We also observed a significant relationship (r = 0.64, p < 0.05) between TF scores and the overall subject scores, indicating that TF questions are an effective predictor of overall student performance. Our results from this initial analysis suggests that TF questions are useful for assessing biomedical-theme content in online, unsupervised examinations, and are encouraging for their ongoing use in future assessments.

Human-device interface pressure measurement in prosthetic, orthotic and exoskeleton applications: A systematic review.

This study aimed to investigate normal and shear load sensor technology that has been characterised and used at the human-device interface in prosthetic, orthotic and exoskeleton applications. In addition to taking a cross-disciplinary view, this study expands on previous reviews by considering recently published papers, clinical translation of sensors, and development of the sensor technology itself. A search of MEDLINE, INSPEC, SCOPUS and Web of Science was performed up to 26 January 2021. A total of 33 studies were assessed for quality and their data extracted. The review found variable quality of published papers, with normal load being most commonly measured, and resistive sensor technology most commonly used. The translation to clinical environments was indicated in most studies, though the study population was not always made up of the target users. Studies could benefit from more direct comparison with clinically relevant load thresholds and by ensuring clinical testing is performed in the most realistic and representative way possible. Additionally, more focus on developing sensors that measure shear loads would enable further insights into conditions at the human-device interface. Finally, all researchers would benefit from better and more widespread anonymous data sharing practices to facilitate further experimentation.

Predicting the influence of hip and lumbar flexibility on lifting motions using optimal control.

Computational models of the human body coupled with optimization can be used to predict the influence of variables that cannot be experimentally manipulated. Here, we present a study that predicts the motion of the human body while lifting a box, as a function of flexibility of the hip and lumbar joints in the sagittal plane. We modeled the human body in the sagittal plane with joints actuated by pairs of agonist-antagonist muscle torque generators, and a passive hamstring muscle. The characteristics of a stiff, average and flexible person were represented by co-varying the lumbar range-of-motion, lumbar passive extensor-torque and the hamstring passive muscle-force. We used optimal control to solve for motions that simulated lifting a 10 kg box from a 0.3 m height. The solution minimized the total sum of the normalized squared active and passive muscle torques and the normalized passive hamstring muscle forces, over the duration of the motion. The predicted motion of the average lifter agreed well with experimental data in the literature. The change in model flexibility affected the predicted joint angles, with the stiffer models flexing more at the hip and knee, and less at the lumbar joint, to complete the lift. Stiffer models produced similar passive lumbar torque and higher hamstring muscle force components than the more flexible models. The variation between the motion characteristics of the models suggest that flexibility may play an important role in determining lifting technique.

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model.

Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem (OCP) is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution toward bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

Patient-specific bone geometry and segment inertia from MRI images for model-based analysis of pathological gait.

Patient-specific modeling is a vital component in the translation of computational multibody dynamics into clinical practice. Recent research has focused on ways to derive such models from medical imaging, but the process is usually time consuming and sensitive to operator skill. Here, we present methods to derive kinematic and inertial properties of body segments from MRI images, and condense them into a dynamically consistent patient-specific multibody model (PSM). We develop a semi-automated tool chain to classify bone, muscle and fat in the lower body and use optimization and geometrical methods to derive personalized bone meshes and segment inertial properties. The tool chain is applied to investigate the gait of a 12-yr old female with bone deformities. The patient-specific results are compared to those arising from generic scaled models with parameters based on regression equations. We found several kinematic and inertial differences between the two models, and overall the PSM resulted in markedly smaller angular and force residuals. The PSM was able to capture vital aspects of this patient׳s gait in the transverse plane that were overlooked by the generic model. These results are relevant to the use of multibody dynamics in the planning of surgical interventions, and form the basis for developing efficient and automatic methods to create patient-specific models.

Modeling and Identification of a Realistic Spiking Neural Network and Musculoskeletal Model of the Human Arm, and an Application to the Stretch Reflex.

This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.

Walking paths to and from a goal differ: on the role of bearing angle in the formation of human locomotion paths.

The path that humans take while walking to a goal is the result of a cognitive process modulated by the perception of the environment and physiological constraints. The path shape and timing implicitly embeds aspects of the architecture behind this process. Here, locomotion paths were investigated during a simple task of walking to and from a goal, by looking at the evolution of the position of the human on a horizontal (x,y) plane. We found that the path while walking to a goal was not the same as that while returning from it. Forward-return paths were systematically separated by 0.5-1.9m, or about 5% of the goal distance. We show that this path separation occurs as a consequence of anticipating the desired body orientation at the goal while keeping the target in view. The magnitude of this separation was strongly influenced by the bearing angle (difference between body orientation and angle to goal) and the final orientation imposed at the goal. This phenomenon highlights the impact of a trade-off between a directional perceptual apparatus-eyes in the head on the shoulders-and and physiological limitations, in the formation of human locomotion paths. Our results give an insight into the influence of environmental and perceptual variables on human locomotion and provide a basis for further mathematical study of these mechanisms.

Walking straight into circles.

Common belief has it that people who get lost in unfamiliar terrain often end up walking in circles. Although uncorroborated by empirical data, this belief has widely permeated popular culture. Here, we tested the ability of humans to walk on a straight course through unfamiliar terrain in two different environments: a large forest area and the Sahara desert. Walking trajectories of several hours were captured via global positioning system, showing that participants repeatedly walked in circles when they could not see the sun. Conversely, when the sun was visible, participants sometimes veered from a straight course but did not walk in circles. We tested various explanations for this walking behavior by assessing the ability of people to maintain a fixed course while blindfolded. Under these conditions, participants walked in often surprisingly small circles (diameter < 20 m), though rarely in a systematic direction. These results rule out a general explanation in terms of biomechanical asymmetries or other general biases [1-6]. Instead, they suggest that veering from a straight course is the result of accumulating noise in the sensorimotor system, which, without an external directional reference to recalibrate the subjective straight ahead, may cause people to walk in circles.

Walking along curved paths of different angles: the relationship between head and trunk turning.

Walking along a curved path requires coordinated motor actions of the entire body. Here, we investigate the relationship between head and trunk movements during walking. Previous studies have found that the head systematically turns into turns before the trunk does. This has been found to occur at a constant distance rather than at a constant time before a turn. We tested whether this anticipatory head behavior is spatially invariant for turns of different angles. Head and trunk positions and orientations were measured while participants walked around obstacles in 45 degrees, 90 degrees, 135 degrees or 180 degrees turns. The radius of the turns was either imposed or left free. We found that the head started to turn into the direction of the turn at a constant distance before the obstacle (approximately 1.1 m) for turn angles up to 135 degrees . During turns, the head was consistently oriented more into the direction of the turn than the trunk. This difference increased for larger turning angles and reached its maximum later in the turn for larger turns. Walking speeds decreased monotonically for increasing turn angles. Imposing fixed turn radii only affected the point at which the trunk started to turn into a turn. Our results support the view that anticipatory head movements during turns occur in order to gather advance visual information about the trajectory and potential obstacles.