Publications by authors named "Taku Komura"

25 Publications

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Reconstruction of Dexterous 3D Motion Data from a Flexible Magnetic Sensor with Deep Learning and Structure-Aware Filtering.

IEEE Trans Vis Comput Graph 2020 Oct 16;PP. Epub 2020 Oct 16.

We propose a novel approach to reconstructing 3D motion data from a flexible magnetic flux sensor array using deep learning and a structure-aware temporal bilateral filter. Computing the 3D configuration of markers (inductor-capacitor (LC) coils) from flux sensor data is difficult because the existing numerical approaches suffer from system noise, dead angles, the need for initialization, and limitations in the sensor array's layout. We solve these issues with deep neural networks to learn the regression from the simulation flux values to the LC coils' 3D configuration, which can be applied to the actual LC coils at any location and orientation within the capture volume. To cope with the influence of system noise and the dead-angle limitation caused by the characteristics of the hardware and sensing principle, we propose a structure-aware temporal bilateral filter for reconstructing motion sequences. Our method can track various movements, including fingers that manipulate objects, beetles that move inside a vivarium with leaves and soil, and the flow of opaque fluid. Since no power supply is needed for the lightweight wireless markers, our method can robustly track movements for a very long time, making it suitable for various types of observations whose tracking is difficult with existing motion-tracking systems. Furthermore, the flexibility of the flux sensor layout allows users to reconfigure it based on their own applications, thus making our approach suitable for a variety of virtual reality applications.
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http://dx.doi.org/10.1109/TVCG.2020.3031632DOI Listing
October 2020

Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks.

Med Image Anal 2020 07 26;63:101712. Epub 2020 Apr 26.

School of Informatics, University of Edinburgh, Edinburgh, UK.

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.
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http://dx.doi.org/10.1016/j.media.2020.101712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294240PMC
July 2020

Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.

Comput Med Imaging Graph 2020 01 27;79:101685. Epub 2019 Nov 27.

School of Informatics, University of Edinburgh, Edinburgh, UK.

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.
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http://dx.doi.org/10.1016/j.compmedimag.2019.101685DOI Listing
January 2020

Skeleton Filter: A Self-Symmetric Filter for Skeletonization in Noisy Text Images.

IEEE Trans Image Process 2019 Oct 7. Epub 2019 Oct 7.

Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images. Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images. The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions. We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton. Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.
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http://dx.doi.org/10.1109/TIP.2019.2944560DOI Listing
October 2019

Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map.

Front Aging Neurosci 2019 27;11:150. Epub 2019 Jun 27.

School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. However, its network architecture is high complex. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The best performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net by recognizing the shape of large WMH more accurately through multi-context learning. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which was the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest training time and the least number of parameters. In conclusion, based on our experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.
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http://dx.doi.org/10.3389/fnagi.2019.00150DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610522PMC
June 2019

Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme.

Front Neuroinform 2019 29;13:33. Epub 2019 May 29.

School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood's passage through the brain's vascular network. Therefore, it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions.
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http://dx.doi.org/10.3389/fninf.2019.00033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548861PMC
May 2019

Localization and Completion for 3D Object Interactions.

IEEE Trans Vis Comput Graph 2020 Aug 14;26(8):2634-2644. Epub 2019 Jan 14.

Finding where and what objects to put into an existing scene is a common task for scene synthesis and robot/character motion planning. Existing frameworks require development of hand-crafted features suitable for the task, or full volumetric analysis that could be memory intensive and imprecise. In this paper, we propose a data-driven framework to discover a suitable location and then place the appropriate objects in a scene. Our approach is inspired by computer vision techniques for localizing objects in images: using an all directional depth image (ADD-image) that encodes the 360-degree field of view from samples in the scene, our system regresses the images to the positions where the new object can be located. Given several candidate areas around the host object in the scene, our system predicts the partner object whose geometry fits well to the host object. Our approach is highly parallel and memory efficient, and is especially suitable for handling interactions between large and small objects. We show examples where the system can hang bags on hooks, fit chairs in front of desks, put objects into shelves, insert flowers into vases, and put hangers onto laundry rack.
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http://dx.doi.org/10.1109/TVCG.2019.2892454DOI Listing
August 2020

A Sampling Approach to Generating Closely Interacting 3D Pose-Pairs from 2D Annotations.

IEEE Trans Vis Comput Graph 2019 Jun 1;25(6):2217-2227. Epub 2018 May 1.

We introduce a data-driven method to generate a large number of plausible, closely interacting 3D human pose-pairs, for a given motion category, e.g., wrestling or salsa dance. With much difficulty in acquiring close interactions using 3D sensors, our approach utilizes abundant existing video data which cover many human activities. Instead of treating the data generation problem as one of reconstruction, either through 3D acquisition or direct 2D-to-3D data lifting from video annotations, we present a solution based on Markov Chain Monte Carlo (MCMC) sampling. Given a motion category and a set of video frames depicting the motion with the 2D pose-pair in each frame annotated, we start the sampling with one or few seed 3D pose-pairs which are manually created based on the target motion category. The initial set is then augmented by MCMC sampling around the seeds, via the Metropolis-Hastings algorithm and guided by a probability density function (PDF) that is defined by two terms to bias the sampling towards 3D pose-pairs that are physically valid and plausible for the motion category. With a focus on efficient sampling over the space of close interactions, rather than pose spaces, we develop a novel representation called interaction coordinates (IC) to encode both poses and their interactions in an integrated manner. Plausibility of a 3D pose-pair is then defined based on the IC and with respect to the annotated 2D pose-pairs from video. We show that our sampling-based approach is able to efficiently synthesize a large volume of plausible, closely interacting 3D pose-pairs which provide a good coverage of the input 2D pose-pairs.
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http://dx.doi.org/10.1109/TVCG.2018.2832097DOI Listing
June 2019

Widening Viewing Angles of Automultiscopic Displays Using Refractive Inserts.

IEEE Trans Vis Comput Graph 2018 04;24(4):1554-1563

Displays that can portray environments that are perceivable from multiple views are known as multiscopic displays. Some multiscopic displays enable realistic perception of 3D environments without the need for cumbersome mounts or fragile head-tracking algorithms. These automultiscopic displays carefully control the distribution of emitted light over space, direction (angle) and time so that even a static image displayed can encode parallax across viewing directions (Iightfield). This allows simultaneous observation by multiple viewers, each perceiving 3D from their own (correct) perspective. Currently, the illusion can only be effectively maintained over a narrow range of viewing angles. In this paper, we propose and analyze a simple solution to widen the range of viewing angles for automultiscopic displays that use parallax barriers. We propose the use of a refractive medium, with a high refractive index, between the display and parallax barriers. The inserted medium warps the exitant lightfield in a way that increases the potential viewing angle. We analyze the consequences of this warp and build a prototype with a 93% increase in the effective viewing angle.
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http://dx.doi.org/10.1109/TVCG.2018.2794599DOI Listing
April 2018

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Comput Med Imaging Graph 2018 06 17;66:28-43. Epub 2018 Feb 17.

School of Informatics, University of Edinburgh, Edinburgh, UK.

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI. We also introduce a way to incorporate spatial information in convolution level of CNN for WMH segmentation named global spatial information (GSI). Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data. Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Our proposed approach of incorporating GSI also successfully helped CNN to achieve better automatic WMH segmentation regardless of network's settings tested. The mean Dice Similarity Coefficient (DSC) values for LST-LGA, SVM, RF, DBM, CNN and CNN-GSI were 0.2963, 0.1194, 0.1633, 0.3264, 0.5359 and 5389 respectively.
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http://dx.doi.org/10.1016/j.compmedimag.2018.02.002DOI Listing
June 2018

Fast Neural Style Transfer for Motion Data.

IEEE Comput Graph Appl 2017 ;37(4):42-49

Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization.
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http://dx.doi.org/10.1109/MCG.2017.3271464DOI Listing
October 2018

Learning Inverse Rig Mappings by Nonlinear Regression.

IEEE Trans Vis Comput Graph 2017 03 11;23(3):1167-1178. Epub 2016 Nov 11.

We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
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http://dx.doi.org/10.1109/TVCG.2016.2628036DOI Listing
March 2017

An Energy-Driven Motion Planning Method for Two Distant Postures.

IEEE Trans Vis Comput Graph 2015 Jan;21(1):18-30

In this paper, we present a local motion planning algorithm for character animation. We focus on motion planning between two distant postures where linear interpolation leads to penetrations. Our framework has two stages. The motion planning problem is first solved as a Boundary Value Problem (BVP) on an energy graph which encodes penetrations, motion smoothness and user control. Having established a mapping from the configuration space to the energy graph, a fast and robust local motion planning algorithm is introduced to solve the BVP to generate motions that could only previously be computed by global planning methods. In the second stage, a projection of the solution motion onto a constraint manifold is proposed for more user control. Our method can be integrated into current keyframing techniques. It also has potential applications in motion planning problems in robotics.
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http://dx.doi.org/10.1109/TVCG.2014.2327976DOI Listing
January 2015

Interactive formation control in complex environments.

IEEE Trans Vis Comput Graph 2014 Feb;20(2):211-22

University of Edinburgh, Edinburgh.

The degrees of freedom of a crowd is much higher than that provided by a standard user input device. Typically, crowd-control systems require multiple passes to design crowd movements by specifying waypoints, and then defining character trajectories and crowd formation. Such multi-pass control would spoil the responsiveness and excitement of real-time control systems. In this paper, we propose a single-pass algorithm to control a crowd in complex environments. We observe that low-level details in crowd movement are related to interactions between characters and the environment, such as diverging/merging at cross points, or climbing over obstacles. Therefore, we simplify the problem by representing the crowd with a deformable mesh, and allow the user, via multitouch input, to specify high-level movements and formations that are important for context delivery. To help prevent congestion, our system dynamically reassigns characters in the formation by employing a mass transport solver to minimize their overall movement. The solver uses a cost function to evaluate the impact from the environment, including obstacles and areas affecting movement speed. Experimental results show realistic crowd movement created with minimal high-level user inputs. Our algorithm is particularly useful for real-time applications including strategy games and interactive animation creation.
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http://dx.doi.org/10.1109/TVCG.2013.116DOI Listing
February 2014

Simulating multiple character interactions with collaborative and adversarial goals.

IEEE Trans Vis Comput Graph 2012 May;18(5):741-52

RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan, 351- 0198.

This paper proposes a new methodology for synthesizing animations of multiple characters, allowing them to intelligently compete with one another in dense environments, while still satisfying requirements set by an animator. To achieve these two conflicting objectives simultaneously, our method separately evaluates the competition and collaboration of the interactions, integrating the scores to select an action that maximizes both criteria. We extend the idea of min-max search, normally used for strategic games such as chess. Using our method, animators can efficiently produce scenes of dense character interactions such as those in collective sports or martial arts. The method is especially effective for producing animations along story lines, where the characters must follow multiple objectives, while still accommodating geometric and kinematic constraints from the environment.
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http://dx.doi.org/10.1109/tvcg.2010.257DOI Listing
May 2012

Indexing and retrieving motions of characters in close contact.

IEEE Trans Vis Comput Graph 2009 May-Jun;15(3):481-92

University of Edinburgh, Edinburgh, UK.

Human motion indexing and retrieval are important for animators due to the need to search for motions in the database which can be blended and concatenated. Most of the previous researches of human motion indexing and retrieval compute the Euclidean distance of joint angles or joint positions. Such approaches are difficult to apply for cases in which multiple characters are closely interacting with each other, as the relationships of the characters are not encoded in the representation. In this research, we propose a topology-based approach to index the motions of two human characters in close contact. We compute and encode how the two bodies are tangled based on the concept of rational tangles. The encoded relationships, which we define as TangleList, are used to determine the similarity of the pairs of postures. Using our method, we can index and retrieve motions such as one person piggy-backing another, one person assisting another in walking, and two persons dancing / wrestling. Our method is useful to manage a motion database of multiple characters. We can also produce motion graph structures of two characters closely interacting with each other by interpolating and concatenating topologically similar postures and motion clips, which are applicable to 3D computer games and computer animation.
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http://dx.doi.org/10.1109/TVCG.2008.199DOI Listing
May 2009

Optimal coordination of maximal-effort horizontal and vertical jump motions--a computer simulation study.

Biomed Eng Online 2007 Jun 1;6:20. Epub 2007 Jun 1.

Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK.

Background: The purpose of this study was to investigate the coordination strategy of maximal-effort horizontal jumping in comparison with vertical jumping, using the methodology of computer simulation.

Methods: A skeletal model that has nine rigid body segments and twenty degrees of freedom was developed. Thirty-two Hill-type lower limb muscles were attached to the model. The excitation-contraction dynamics of the contractile element, the tissues around the joints to limit the joint range of motion, as well as the foot-ground interaction were implemented. Simulations were initiated from an identical standing posture for both motions. Optimal pattern of the activation input signal was searched through numerical optimization. For the horizontal jumping, the goal was to maximize the horizontal distance traveled by the body's center of mass. For the vertical jumping, the goal was to maximize the height reached by the body's center of mass.

Results: As a result, it was found that the hip joint was utilized more vigorously in the horizontal jumping than in the vertical jumping. The muscles that have a function of joint flexion such as the m. iliopsoas, m. rectus femoris and m. tibialis anterior were activated to a greater level during the countermovement in the horizontal jumping with an effect of moving the body's center of mass in the forward direction. Muscular work was transferred to the mechanical energy of the body's center of mass more effectively in the horizontal jump, which resulted in a greater energy gain of the body's center of mass throughout the motion.

Conclusion: These differences in the optimal coordination strategy seem to be caused from the requirement that the body's center of mass needs to be located above the feet in a vertical jumping, whereas this requirement is not so strict in a horizontal jumping.
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http://dx.doi.org/10.1186/1475-925X-6-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1896168PMC
June 2007

Simulating pathological gait using the enhanced linear inverted pendulum model.

IEEE Trans Biomed Eng 2005 Sep;52(9):1502-13

City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong.

In this paper, we propose a new method to simulate human gait motion when muscles are weakened. The method is based on the enhanced version of three-dimensional linear inverted pendulum model that is used for generation of gait in robotics. After the normal gait motion is generated by setting the initial posture and the parameters that decide the trajectories of the center of mass and angular momentum, the muscle to be weakened is specified. By minimizing an objective function based on the force exerted by the specified muscle during the motion, the set of parameters that represent the pathological gait was calculated. Since the number of parameters to describe the motion is small in our method, the optimization process converges much more quickly than in previous methods. The effects of weakening the gluteus medialis, the gluteus maximus, and vastus were analyzed. Important similarities were noted when comparing the predicted pendulum motion with data obtained from an actual patient.
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http://dx.doi.org/10.1109/TBME.2005.851530DOI Listing
September 2005

Contribution of non-extensor muscles of the leg to maximal-effort countermovement jumping.

Biomed Eng Online 2005 Sep 6;4:52. Epub 2005 Sep 6.

Computational Biomechanics Unit, RIKEN, Hirosawa 2-1, Wako, Saitama, 351-0198, Japan.

Background: The purpose of this study was to determine the effects of non-extensor muscles of the leg (i.e., muscles whose primary function is not leg extension) on the kinematics and kinetics of human maximal-effort countermovement jumping. Although it is difficult to address this type of question through experimental procedures, the methodology of computer simulation can be a powerful tool.

Methods: A skeletal model that has nine rigid body segments and twenty degrees of freedom was developed. Two sets of muscle models were attached to this skeletal model: all (most of) major muscles in the leg ("All Muscles" model) and major extensor muscles in the leg (i.e., muscles whose primary function is leg extension; "Extensors Only" model). Neural activation input signal was represented by a series of step functions with a step duration of 0.05 s. Simulations were started from an identical upright standing posture. The optimal pattern of the activation input signal was searched through extensive random-search numerical optimization with a goal of maximizing the height reached by the mass centre of the body after jumping up.

Results: The simulated kinematics was almost two-dimensional, suggesting the validity of two-dimensional analyses when evaluating net mechanical outputs around the joints using inverse dynamics. A greater jumping height was obtained for the "All Muscles" model (0.386 m) than for the "Extensors Only" model (0.301 m). For the "All Muscles" model, flexor muscles developed force in the beginning of the countermovement. For the "All Muscles" model, the sum of the work outputs from non-extensor muscles was 47.0 J, which was 13% of the total amount (359.9 J). The quantitative distribution of the work outputs from individual muscles was markedly different between these two models.

Conclusion: It was suggested that the contribution of non-extensor muscles in maximal-effort countermovement jumping is substantial. The use of a computer simulation model that includes non-extensor muscles seems to be more desirable for the assessment of muscular outputs during jumping.
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http://dx.doi.org/10.1186/1475-925X-4-52DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1215494PMC
September 2005

Force, work and power output of lower limb muscles during human maximal-effort countermovement jumping.

J Electromyogr Kinesiol 2005 Aug;15(4):367-76

Computational Biomechanics Unit, RIKEN, Hirosawa 2-1, Wako, Saitama 351-0198, Japan.

The purpose of this study was to simulate human maximal-effort countermovement jumping with a three-dimensional neuromusculoskeletal model. The specific aim was to investigate muscle force, work and power output of major lower limb muscles during the motion. A neuromusculoskeletal model that has nine rigid body segments, 20 degrees of freedom, 32 Hill-type lower limb muscles was developed. The neural activation input signal was represented by a series of step functions with step duration of 0.05 s. The excitation-contraction dynamics of the contractile element, the tissues around the joints to limit the joint range of motion, as well as the foot-ground interaction were implemented. A simulation was started from a standing posture. Optimal pattern of the activation input signal was searched through numerical optimization with a goal of maximizing the height reached by the mass center of body after jumping up. As a result, feasible kinematics, ground reaction force profile and muscle excitation profile were generated. It was found that monoarticular muscles had major contributions of mechanical work and power output, whereas biarticular muscles had minor contributions. Hip adductors, abductors and external rotator muscles were vigorously activated, although their mechanical work and power output was minor because of their limited length change during the motion. Joint flexor muscles such as m. iliopsoas, m. biceps femoris short head and m. tibialis anterior were activated in the beginning of the motion with an effect of facilitating the generation of a countermovement.
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http://dx.doi.org/10.1016/j.jelekin.2004.12.006DOI Listing
August 2005

Evaluation of the influence of muscle deactivation on other muscles and joints during gait motion.

J Biomech 2004 Apr;37(4):425-36

Department of Computer Engineering and Information Technology, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong.

When any muscle in the human musculoskeletal system is damaged, other muscles and ligaments tend to compensate for the role of the damaged muscle by exerting extra effort. It is beneficial to clarify how the roles of the damaged muscles are compensated by other parts of the musculoskeletal system from the following points of view: From a clinical point of view, it will be possible to know how the abnormal muscle and joint forces caused by the acute compensations lead to further physical damage to the musculoskeletal system. From the viewpoint of rehabilitation, it will be possible to know how the role of the damaged muscle can be compensated by extra training of the other muscles. A method to evaluate the influence of muscle deactivation on other muscles and joints is proposed in this report. Methodology based on inverse dynamics and static optimization, which is applicable to arbitrary motion was used in this study. The evaluation method was applied to gait motion to obtain matrices representing (1) the dependence of muscle force compensation and (2) the change to bone-on-bone contact forces. These matrices make it possible to evaluate the effects of deactivation of one of the muscles of the musculoskeletal system on the forces exerted by other muscles as well as the change to the bone-on-bone forces when the musculoskeletal system is performing the same motion. Through observation of this matrix, it was found that deactivation of a muscle often results in increment/decrement of force developed by muscles with completely different primary functions and bone-on-bone contact force in different parts of the body. For example, deactivation of the iliopsoas leads to a large reduction in force by the soleus. The results suggest that acute deactivation of a muscle can result in damage to another part of the body. The results also suggest that the whole musculoskeletal system must go through extra retraining in the case of damage to certain muscles.
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http://dx.doi.org/10.1016/j.jbiomech.2003.09.022DOI Listing
April 2004

Effects of the length ratio between the contractile element and the series elastic element on an explosive muscular performance.

J Electromyogr Kinesiol 2004 Apr;14(2):197-203

Department of Computer Engineering and Information Technology, City University of Hong Kong, Kowloon, Hong Kong.

Effects of the length ratio between the contractile element (CE) and the series elastic element (SEE) on the behavior of the muscle tendon complex were investigated during stretch-shortening cycles. A computer simulation model of the Hill-type muscle tendon complex was constructed. The proximal end of the CE was affixed to a point in the gravitational field, and a massless supporting object was affixed to the distal end of the SEE. A mass was held on the supporting object. Initially, the muscle tendon complex was fixed at a certain length, and the CE was activated at 100%. Through this process, the CE contracted as much as the SEE was stretched. Thereafter, the supporting object was released, which caused the muscle tendon complex to propel the mass upward, simulating a stretch-shortening cycle. The length ratio between the CE and the SEE, the size of the mass and the initial length of the CE were sequentially changed. As a result, it was found that a higher performance is obtained with a longer SEE when the mass is small, while with a shorter SEE when the mass is large.
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http://dx.doi.org/10.1016/S1050-6411(03)00085-3DOI Listing
April 2004

Longer moment arm results in smaller joint moment development, power and work outputs in fast motions.

J Biomech 2003 Nov;36(11):1675-81

Center for BioDynamics, Boston University, Boston, MA, USA.

Effects of moment arm length on kinetic outputs of a musculoskeletal system (muscle force development, joint moment development, joint power output and joint work output) were evaluated using computer simulation. A skeletal system of the human ankle joint was constructed: a lower leg segment and a foot segment were connected with a hinge joint. A Hill-type model of the musculus soleus (m. soleus), consisting of a contractile element and a series elastic element, was attached to the skeletal system. The model of the m. soleus was maximally activated, while the ankle joint was plantarflexed/dorsiflexed at a variation of constant angular velocities, simulating isokinetic exercises on a muscle testing machine. Profiles of the kinetic outputs (muscle force development, joint moment development, joint power output and joint work output) were obtained. Thereafter, the location of the insertion of the m. soleus was shifted toward the dorsal/ventral direction by 1cm, which had an effect of lengthening/shortening the moment arm length, respectively. The kinetic outputs of the musculoskeletal system during the simulated isokinetic exercises were evaluated with these longer/shorter moment arm lengths. It was found that longer moment arm resulted in smaller joint moment development, smaller joint power output and smaller joint work output in the larger plantarflexion angular velocity region (>120 degrees/s). This is because larger muscle shortening velocity was required with longer moment arm to achieve a certain joint angular velocity. Larger muscle shortening velocity resulted in smaller muscle force development because of the force-velocity relation of the muscle. It was suggested that this phenomenon should be taken into consideration when investigating the joint moment-joint angle and/or joint moment-joint angular velocity characteristics of experimental data.
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http://dx.doi.org/10.1016/s0021-9290(03)00171-4DOI Listing
November 2003
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