Muscle-Tendon Unit Length Measurement Using 3D Ultrasound in Passive Conditions: OpenSim Validation and Development of Personalized Models

This study investigated the validity of using OpenSim to measure muscle-tendon unit (MTU) length of the bi-articular lower limb muscles in several postures (shortened, lengthened, a combination of shortened and lengthened involving both joints, neutral and standing) using 3D freehand ultrasound (US), and to propose new personalized models. MTU length was measured on 14 participants and 6 bi-articular muscles (semimembranosus SM, semitendinosus ST, biceps femoris BF, rectus femoris RF, gastrocnemius medialis GM and gastrocnemius lateralis GL), considering 5 to 6 postures. MTU length was computed using OpenSim with three different models: OS (the generic OpenSim scaled model), OS + INSER (OS with personalized 3D US MTU insertions), OS + INSER + PATH (OS with personalized 3D US MTU insertions and path obtained from one posture). Significant differences in MTU length were found between OS and 3D US models for RF, GM and GL (from - 6.3 to 10.9%). Non-significant effects were reported for the hamstrings, notably for the ST (- 1.5%) and BF (- 1.9%), while the SM just crossed the alpha level (- 3.4%, p = 0.049). The OS + INSER model reduced the magnitude of bias by an average of 4% for RF, GM and GL. The OS + INSER + PATH model showed the smallest biases in length estimates, which made them negligible and non-significant for all the MTU (i.e. ≤ 2.2%). A 3D US pipeline was developed and validated to estimate the MTU length from a limited number of measurements. This opens up new perspectives for personalizing musculoskeletal models using low-cost user-friendly devices.

ARKOMA dataset: An open-source dataset to develop neural networks-based inverse kinematics model for NAO robot arms

The inverse kinematics plays a vital role in the planning and execution of robot motions. In the design of robotic motion control for NAO robot arms, it is necessary to find the proper inverse kinematics model. Neural networks are such a data-driven modeling technique that they are so flexible for modeling the inverse kinematics. This inverse kinematics model can be obtained by means of training neural networks with the dataset. This training process cannot be achieved without the presence of the dataset. Therefore, the contribution of this research is to provide the dataset to develop neural networks-based inverse kinematics model for NAO robot arms. The dataset that we created in this paper is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains 10000 input-output data pairs in which the end-effector position and orientation are the input data and a set of joint angular positions are the output data. For further application, this dataset was split into three subsets: training dataset, validation dataset, and testing dataset. From a set of 10000 data, 60 % of data was allocated for the training dataset, 20 % of data for the validation dataset, and the remaining 20 % of data for the testing dataset. The dataset that we provided in this paper can be applied for NAO H25 v3.3 or later.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Influence of marker weights optimization on scapular kinematics estimated with a multibody kinematic optimization

Scapular kinematic estimates are altered by soft tissue artefacts, therefore experimental and numerical methods should be developed to improve their accuracy. This study aimed to assess the influence of weights applied to the scapula markers within a closed-loop multibody kinematic optimization on scapular kinematic estimates. Fifteen healthy volunteers performed static postures mimicking analytical, daily living and sport movements. Scapulo-thoracic angles were computed either from a scapula locator as the reference, or from a closed-loop multibody-kinematic optimization (MKO) including a participant-specific point-on-ellipsoid scapulothoracic joint. Weights applied to scapula markers in the MKO were optimized to minimize the difference in scapular orientation from the reference. Optimizing weighting sets significantly (p < 0.0001) improved scapular orientation from 0.9° to 12.1° in comparison to scapular kinematics estimated with non-optimized weighting sets. The mean optimized weighting set contained no neglectable weight for all markers from the acromion to the medial border of the scapular spine but showed no significant difference (p = 0.547) compared to homogeneous weights. Optimized weighting sets were participant- and movement- specific. To conclude, homogenous weights applied on redundant markers located from acromion to scapular medial border spine are recommended when estimating scapular kinematics in upper limb MKO.

Towards a validated musculoskeletal knee model to estimate tibiofemoral kinematics and ligament strains: comparison of different anterolateral augmentation procedures combined with isolated ACL reconstructions

BACKGROUND

Isolated ACL reconstructions (ACLR) demonstrate limitations in restoring native knee kinematics. This study investigates the knee mechanics of ACLR plus various anterolateral augmentations using a patient-specific musculoskeletal knee model.

MATERIALS AND METHODS

A patient-specific knee model was developed in OpenSim using contact surfaces and ligament details derived from MRI and CT data. The contact geometry and ligament parameters were varied until the predicted knee angles for intact and ACL-sectioned models were validated against cadaveric test data for that same specimen. Musculoskeletal models of the ACLR combined with various anterolateral augmentations were then simulated. Knee angles were compared between these reconstruction models to determine which technique best matched the intact kinematics. Also, ligament strains calculated by the validated knee model were compared to those of the OpenSim model driven by experimental data. The accuracy of the results was assessed by calculating the normalised RMS error (NRMSE); an NRMSE < 30% was considered acceptable.

RESULTS

All rotations and translations predicted by the knee model were acceptable when compared to the cadaveric data (NRMSE < 30%), except for the anterior/posterior translation (NRMSE > 60%). Similar errors were observed between ACL strain results (NRMSE > 60%). Other ligament comparisons were acceptable. All ACLR plus anterolateral augmentation models restored kinematics toward the intact state, with ACLR plus anterolateral ligament reconstruction (ACLR + ALLR) achieving the best match and the greatest strain reduction in ACL, PCL, MCL, and DMCL.

CONCLUSION

The intact and ACL-sectioned models were validated against cadaveric experimental results for all rotations. It is acknowledged that the validation criteria are very lenient; further refinement is required for improved validation. The results indicate that anterolateral augmentation moves the kinematics closer to the intact knee state; combined ACLR and ALLR provide the best outcome for this specimen.

A fast task planning system for 6R articulated robots based on inverse kinematics

Robots bring eventful impacts to the workplace, benefiting from the advantages of implementing any technology should be based on the premise of safety. This work proposes a systematic method to establish a postprocessor module for any specified 6R articulated robot. Instead of obsessively emphasizing achieving the desired locations and orientations by hand guiding grab and dragging a robotic arm for operation in teaching mode, the significant end-effector poses are calculated to form a new path for the joints efficiently in this study. Since robotic motion control is usually a complex system whose users must be well trained and acquainted with using them. There is a need for a GUI solution that can provide intuitive robotic motion control on the current location by the user independently, easy setup, arrangement, adjustment, and monitoring robot motion tasks. The proposed system simplifies the interaction between the technician and the industrial robotic arm in the case of robotic motion control and tracking at a distant location. The presented method is fully adapted to alternate between joint angles and end-effector poses on a graphic user interface system. After developing the capabilities of the solver, a functional postprocessor is programmed inside the proposed GUI system. Examples with specified posture and predefined movements are demonstrated for corroborating the algorithm. The results show considerable efficiency and reliability in task planning and are fully supported for automatic path generation.

Open-source software library for real-time inertial measurement unit data-based inverse kinematics using OpenSim

Background

Inertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim's functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics.

Methods

We developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub.

Results

A standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics.

Conclusions

The software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory.

[Follow control of upper limb rehabilitation training based on Kinect and NAO robot]

Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.

Motion envelopes: unfolding longitudinal rotation data from walking stick-figures

This work presents Motion Envelopes (ME), a simple method to estimate the missing longitudinal rotations of minimal stick figures, which is based on the spatial-temporal surface traced by line segments that connect contiguous pairs of joints. We validate ME by analyzing the gait patterns of 6 healthy subjects, comprising a total of 18 gait cycles. A strong correlation between experimental and estimated data was obtained for lower limbs and upper arms, indicating that ME can predict their longitudinal orientation in normal gait, hence, ME can be used to complement the kinematic information of stick figures whenever it is incomplete.

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Distribution of tremor among the major degrees of freedom of the upper limb in subjects with Essential Tremor

OBJECTIVE

Although Essential Tremor is one of the most common movement disorders, we do not currently know which muscles are most responsible for tremor. Determining this requires multiple steps, one of which is characterizing the distribution of tremor among the degrees of freedom (DOF) of the upper limb.

METHODS

Upper-limb motion was recorded while 22 subjects with ET performed postural and kinetic tasks involving a variety of limb configurations. We calculated the mean distribution of tremor among the seven DOF from the shoulder to the wrist, as well as the effect of limb configuration, repetition, and subject characteristics (sex, tremor onset, duration, and severity) on the distribution.

RESULTS

On average, kinetic tremor was greatest in forearm pronation-supination and wrist flexion-extension, intermediate in shoulder internal-external rotation and wrist radial-ulnar deviation and then shoulder flexion-extension and elbow flexion-extension, and least in shoulder abduction-adduction. The average distribution of postural tremor was similar except for forearm pronation-supination, which played a smaller role than in kinetic tremor. Limb configuration and subject characteristics did significantly affect tremor, but practically only in forearm pronation-supination and wrist flexion-extension. There were no significant differences between repetitions, indicating that the distribution was consistent over the duration of the experiment.

CONCLUSIONS

This paper presents a thorough characterization of tremor distribution from the shoulder to the wrist.

SIGNIFICANCE

Understanding which DOF exhibit the most tremor may lead to more targeted peripheral tremor suppression.

Design of a multi-arm concentric-tube robot system for transnasal surgery

Concentric tube robot (CTR) has gradually attracted the attention of researchers on the basis of its small size and curved shape control ability. However, most of current experimental prototypes of CTR are single-arm structure, which can only carry out simple operation such as drug delivery or monitoring. In this paper, design and analysis of a three-arm CTR system is proposed. It has a four-DOF vision arm and two six-DOF manipulator arms, which equipped with special end effectors to achieve different surgical operations. Finally, a mean motion accuracy of 0.33 mm has been obtained quantitatively through teleoperation experiments. Moreover, tissue excision experiment in skull model is carried out to prove the effectiveness and feasibility of the proposed CTR system in nasopharyngeal carcinoma surgery. Graphical Abstract Platform of the proposed Multi-Arm Concentric Tube Robot system. (a) Configuration of the end-effectors with the CTR system. (b) The setup of the tissue removal experiment in a skull model.

Deeply-learnt damped least-squares (DL-DLS) method for inverse kinematics of snake-like robots

Recently, snake-like robots are proposed to assist experts during medical procedures on internal organs via natural orifices. Despite their well-spelt advantages, applications in radiosurgery is still hindered by absence of suitable designs required for spatial navigations within clustered and confined parts of human body, and inexistence of precise and fast inverse kinematics (IK) models. In this study, a deeply-learnt damped least squares method is proposed for solving IK of spatial snake-like robot. The robot's model consists of several modules, and each module has a pair of serial-links connected with orthogonal twists. For precise control of the robot's end-effector, damped least-squares approach is used to minimize error magnitude in a function modeled over analytical Jacobian of the robot. This is iteratively done until an apt joint vector needed to converge the robot to desired positions is obtained. For fast control and singularity avoidance, a deep network is built for prediction of unique damping factor required for each target point in the robot's workspace. The deep network consists of 11 x 15 array of neurons at the hidden layer, and deeply-learnt with a huge dataset of 877,500 data points generated from workspace of the snake robot. Implementation results for both simulated and actual prototype of an eight-link model of the robot show the effectiveness of the proposed IK method. With error tolerance of 0.01 mm, the proposed method has a very high reachability measure of 91.59% and faster mean execution time of 9.20 (±16.92) ms for convergence. In addition, the method requires an average of 33.02 (±39.60) iterations to solve the IK problem. Hence, approximately 3.6 iterations can be executed in 1 ms. Evaluation against popularly used IK methods shows that the proposed method has very good performance in terms of accuracy and speed, simultaneously.

Modified conventional gait model versus cluster tracking: Test-retest reliability, agreement and impact of inverse kinematics with joint constraints on kinematic and kinetic data

BACKGROUND

Three-dimensional gait analysis is often used to assess kinematics and kinetics to discriminate gait patterns and examine change over time. Test-retest reliability is therefore imperative; however, many variations of gait models currently exist.

RESEARCH QUESTION

This study examined the test-retest reliability of, and agreement between, two commonly used methods of gait modelling, a modified Conventional Gait Model and cluster-based model, using both six degrees-of-freedom or inverse kinematics computational methods in Visual3D.

METHODS

Thirty healthy participants attended two identical sessions. The data for both models were collected concurrently and analysed in Visual3D using either six degrees-of-freedom or inverse kinematics computational methods. Outcomes were taken as the peak measurements for kinematics (joint angles and angular velocity) and kinetics (joint moments and power) for the hip, knee and ankle. Intraclass correlation coefficients were used to examine reliability, with the standard error of measurement and minimal detectable change also calculated. Agreement between models was examined with Pearson correlations and intraclass correlation coefficients.

RESULTS

Test-retest reliability was good to excellent for all models for lower limb kinematics and kinetics. The inverse kinematic models had slightly lower reliability across outcomes compared to the six degrees-of-freedom models. Agreement between the Conventional Gait Model and cluster model was mostly good to excellent. Comparison of the two modified CGMs (with six degrees-of-freedom and inverse kinematics) showed much higher agreement against the comparison of the two cluster-based models (with six degrees-of-freedom and inverse kinematics).

SIGNIFICANCE

This study provides a comprehensive assessment of the test-retest reliability and agreement between two gait models with various computational methods. Future research may use these results to guide their decision making for the gait model and outcomes of interest to be used.

A method for characterizing essential tremor from the shoulder to the wrist

BACKGROUND

Despite the pervasive and devastating effect of Essential Tremor (ET), the distribution of ET throughout the upper limb is unknown. We developed a method for characterizing the distribution of ET and performed a preliminary characterization in a small number of subjects with ET.

METHODS

Using orientation sensors and inverse kinematics, we measured tremor in each of the seven major degrees of freedom (DOF) from the shoulder to the wrist while ten patients with mild ET assumed 16 different postures. We described the tremor in each DOF in terms of power spectral density measures and investigated how tremor varied between DOF, postures, gravitational torques, and repetitions.

FINDINGS

Our method successfully resulted in tremor measures in each DOF, allowing one to compare tremor between DOF and determine the distribution of tremor throughout the upper limb, including how the distribution changes with posture. In our small number of subjects, we found that the amount of power in the frequency band associated with ET (4-12Hz) was lowest in the shoulder and greatest in the wrist. Similarly, the existence and amplitude of peaks in this band increased from proximal to distal. Although the amount of tremor differed significantly between postures, we did not find any clear patterns with changes in posture or gravitational torque.

INTERPRETATION

This method can be used to characterize the distribution of tremor throughout the upper limb. Our preliminary characterization suggests that the amount of tremor increases in a proximal-distal manner.

Effects of hip joint centre mislocation on gait kinematics of children with cerebral palsy calculated using patient-specific direct and inverse kinematic models

Joint kinematics can be calculated by Direct Kinematics (DK), which is used in most clinical gait laboratories, or Inverse Kinematics (IK), which is mainly used for musculoskeletal research. In both approaches, joint centre locations are required to compute joint angles. The hip joint centre (HJC) in DK models can be estimated using predictive or functional methods, while in IK models can be obtained by scaling generic models. The aim of the current study was to systematically investigate the impact of HJC location errors on lower limb joint kinematics of a clinical population using DK and IK approaches. Subject-specific kinematic models of eight children with cerebral palsy were built from magnetic resonance images and used as reference models. HJC was then perturbed in 6mm steps within a 60mm cubic grid, and kinematic waveforms were calculated for the reference and perturbed models. HJC perturbations affected only hip and knee joint kinematics in a DK framework, but all joint angles were affected when using IK. In the DK model, joint constraints increased the sensitivity of joint range-of-motion to HJC location errors. Mean joint angle offsets larger than 5° were observed for both approaches (DK and IK), which were larger than previously reported for healthy adults. In the absence of medical images to identify the HJC, predictive or functional methods with small errors in anterior-posterior and medial-lateral directions and scaling procedures minimizing HJC location errors in the anterior-posterior direction should be chosen to minimize the impact on joint kinematics.

Non-iterative geometric approach for inverse kinematics of redundant lead-module in a radiosurgical snake-like robot

BACKGROUND

Snake-like robot is an emerging form of serial-link manipulator with the morphologic design of biological snakes. The redundant robot can be used to assist medical experts in accessing internal organs with minimal or no invasion. Several snake-like robotic designs have been proposed for minimal invasive surgery, however, the few that were developed are yet to be fully explored for clinical procedures. This is due to lack of capability for full-fledged spatial navigation. In rare cases where such snake-like designs are spatially flexible, there exists no inverse kinematics (IK) solution with both precise control and fast response.

METHODS

In this study, we proposed a non-iterative geometric method for solving IK of lead-module of a snake-like robot designed for therapy or ablation of abdominal tumors. The proposed method is aimed at providing accurate and fast IK solution for given target points in the robot's workspace. n-1 virtual points (VPs) were geometrically computed and set as coordinates of intermediary joints in an n-link module. Suitable joint angles that can place the end-effector at given target points were then computed by vectorizing coordinates of the VPs, in addition to coordinates of the base point, target point, and tip of the first link in its default pose. The proposed method is applied to solve IK of two-link and redundant four-link modules.

RESULTS

Both two-link and four-link modules were simulated with Robotics Toolbox in Matlab 8.3 (R2014a). Implementation result shows that the proposed method can solve IK of the spatially flexible robot with minimal error values. Furthermore, analyses of results from both modules show that the geometric method can reach 99.21 and 88.61% of points in their workspaces, respectively, with an error threshold of 1 mm. The proposed method is non-iterative and has a maximum execution time of 0.009 s.

CONCLUSIONS

This paper focuses on solving IK problem of a spatially flexible robot which is part of a developmental project for abdominal surgery through minimal invasion or natural orifices. The study showed that the proposed geometric method can resolve IK of the snake-like robot with negligible error offset. Evaluation against well-known methods shows that the proposed method can reach several points in the robot's workspace with high accuracy and shorter computational time, simultaneously.

Predicting tibiotalar and subtalar joint angles from skin-marker data with dual-fluoroscopy as a reference standard

Evidence suggests that the tibiotalar and subtalar joints provide near six degree-of-freedom (DOF) motion. Yet, kinematic models frequently assume one DOF at each of these joints. In this study, we quantified the accuracy of kinematic models to predict joint angles at the tibiotalar and subtalar joints from skin-marker data. Models included 1 or 3 DOF at each joint. Ten asymptomatic subjects, screened for deformities, performed 1.0m/s treadmill walking and a balanced, single-leg heel-rise. Tibiotalar and subtalar joint angles calculated by inverse kinematics for the 1 and 3 DOF models were compared to those measured directly in vivo using dual-fluoroscopy. Results demonstrated that, for each activity, the average error in tibiotalar joint angles predicted by the 1 DOF model were significantly smaller than those predicted by the 3 DOF model for inversion/eversion and internal/external rotation. In contrast, neither model consistently demonstrated smaller errors when predicting subtalar joint angles. Additionally, neither model could accurately predict discrete angles for the tibiotalar and subtalar joints on a per-subject basis. Differences between model predictions and dual-fluoroscopy measurements were highly variable across subjects, with joint angle errors in at least one rotation direction surpassing 10° for 9 out of 10 subjects. Our results suggest that both the 1 and 3 DOF models can predict trends in tibiotalar joint angles on a limited basis. However, as currently implemented, neither model can predict discrete tibiotalar or subtalar joint angles for individual subjects. Inclusion of subject-specific attributes may improve the accuracy of these models.

A physiologically based hypothesis for learning proprioception and in approximating inverse kinematics

A long-standing problem in muscle control is the "curse of dimensionality". In part, this problem relates to the fact that coordinated movement is only achieved through the simultaneous contraction and extension of multitude muscles to specific lengths. Couched in robotics terms, the problem includes the determination of forward and inverse kinematics. Of the many neurophysiological discoveries in cortex is the existence of position gradients. Geometrically, position gradients are described by planes in Euclidean space whereby neuronal activity increases as the hand approaches locations that lie in a plane. This work demonstrates that position gradients, when coupled with known physiology in the spinal cord, allows for a way to approximate proprioception (forward kinematics) and to specify muscle lengths for goal-directed postures (inverse kinematics). Moreover, position gradients provide a means to learn and adjust kinematics as animals learn to move and grow. This hypothesis is demonstrated using computer simulation of a human arm. Finally, experimental predictions are described that might confirm or falsify the hypothesis.

Modularity for Motor Control and Motor Learning

How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.

Cerebellum-inspired neural network solution of the inverse kinematics problem

The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.

Comparison between overweight due to pregnancy and due to added weight to simulate body mass distribution in pregnancy

The assessment of biomechanical loading in the musculoskeletal system of the pregnant women is particularly interesting since they are subject to morphological, physiological and hormonal changes, which may lead to adaptations in gait. The purpose of this study was to analyze the effect of the increased mass in the trunk associated to pregnancy on the lower limb and pelvis, during walking, on temporal-distance parameters, joint range of motion and moments of force, by comparing a pregnant women group to a non-pregnant group, and to this group while carrying a 5 kg additional load located in the abdomen and breasts during walking, to understand which gait adaptations may be more related with the increased trunk mass, or if may be more associated with other factors such as the girth of the thigh. The subjects performed a previous 12 min training adaption to the added load. To calculate ankle, knee and hip joint angles and moments of force, a three-dimensional biomechanical model was developed. The inverse dynamics method was used to estimate net joint moments of force. The increased mass of the anterior trunk associated with second trimester of pregnancy may influence some gait variables such as the left step time, left and right stance times, double limb support time, maximum hip extension, maximum pelvic right obliquity, pelvic obliquity range of motion, maximum transversal left rotation and peak hip flexion moments of force.

How reliable are knee kinematics and kinetics during side-cutting manoeuvres?

INTRODUCTION

Side-cutting tasks are commonly used in dynamic assessment of ACL injury risk, but only limited information is available concerning the reliability of knee loading parameters. The aim of this study was to investigate the reliability of side-cutting data with additional focus on modelling approaches and task execution variables.

METHODS

Each subject (n=8) attended six testing sessions conducted by two observers. Kinematic and kinetic data of 45° side-cutting tasks was collected. Inter-trial, inter-session, inter-observer variability and observer/trial ratios were calculated at every time-point of normalised stance, for data derived from two modelling approaches. Variation in task execution variables was regressed against that of temporal profiles of relevant knee data using one-dimensional statistical parametric mapping.

RESULTS

Variability in knee kinematics was consistently low across the time-series waveform (≤5°), but knee kinetic variability was high (31.8, 24.1 and 16.9 Nm for sagittal, frontal and transverse planes, respectively) in the weight acceptance phase of the side-cutting task. Calculations conveyed consistently moderate-to-good measurement reliability. Inverse kinematic modelling reduced the variability in sagittal (∼6 Nm) and frontal planes (∼10 Nm) compared to direct kinematic modelling. Variation in task execution variables did not explain any knee data variability.

CONCLUSION

Side-cutting data appears to be reliably measured, however high knee moment variability exhibited in all planes, particularly in the early stance phase, suggests cautious interpretation towards ACL injury mechanics. Such variability may be inherent to the dynamic nature of the side-cutting task or experimental issues not yet known.