9,402 research outputs found
Biomechanics
Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists
Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
The mechanical characterization of biological samples is a fundamental issue in biology
and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases.
In this paper, a novel approach for the identification of the stiffness and damping coefficients
of biosamples is introduced. According to the proposed method, a MEMS-based microgripper
in operational condition is used as a measurement tool. The mechanical model describing the
dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper,
and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based
on recursive least square (RLS) methods are implemented for the estimation of the mechanical
coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS
algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach.
Results confirm the feasibility of the method that enables the ability to perform simultaneously two
tasks: sample manipulation and parameters identification
Learning a Pose Lexicon for Semantic Action Recognition
This paper presents a novel method for learning a pose lexicon comprising
semantic poses defined by textual instructions and their associated visual
poses defined by visual features. The proposed method simultaneously takes two
input streams, semantic poses and visual pose candidates, and statistically
learns a mapping between them to construct the lexicon. With the learned
lexicon, action recognition can be cast as the problem of finding the maximum
translation probability of a sequence of semantic poses given a stream of
visual pose candidates. Experiments evaluating pre-trained and zero-shot action
recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets
were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and
Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
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Methodological and anatomical modifiers of Achilles tendon moment arm estimates implications for biomechanical modelling: Implications for biomechanical modelling
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.Moment arms are important in many contexts. Various methods have been used to estimate
moment arms. It has been shown that a moment arm changes as a function of joint angle and contraction state. However, besides the influence of these anatomical factors, results from recent studies suggest that the estimation of moment arm is also dependent
on the methods employed. The overall goal of this thesis was to explore the interaction between the methodological
and anatomical influences on moment arm and their effect on estimates of muscle-tendon
forces during biomechanical modelling. The first experiment was a direct comparison
between two different moment arm methods that have been previously used for the estimation of Achilles tendon moment arm. The results of this experiment revealed a significant difference in Achilles tendon moment arm length dependent on the moment arm
method employed. However, besides the differences found, results from both methods
were well correlated. Based on these results, methodological differences between these two methods were compared across different joint angles and contraction states in study two. Results of experiment two revealed that Achilles tendon moment arms obtained using
both methods change in a similar way as a function of joint angle and contraction state. In the third experiment, results from the first two experiments were used to determine how methodological and anatomical influences on Achilles tendon moment arm would change muscle-tendon forces during the task of submaximal cycling. Results of the third experiment showed the importance of taking the method, ankle angle and contraction state dependence of Achilles tendon moment arm into account when using biomechanical modelling techniques.
Together, these findings emphasis the importance of carefully considering methodological and anatomical modifiers when estimating Achilles tendon moment arm
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Synergy-based Hand Pose Sensing: Reconstruction Enhancement
Low-cost sensing gloves for reconstruction posture provide measurements which
are limited under several regards. They are generated through an imperfectly
known model, are subject to noise, and may be less than the number of Degrees
of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of
the hand posture is an ill-posed problem, and performance can be very poor.
This paper examines the problem of estimating the posture of a human hand
using(low-cost) sensing gloves, and how to improve their performance by
exploiting the knowledge on how humans most frequently use their hands. To
increase the accuracy of pose reconstruction without modifying the glove
hardware - hence basically at no extra cost - we propose to collect, organize,
and exploit information on the probabilistic distribution of human hand poses
in common tasks. We discuss how a database of such an a priori information can
be built, represented in a hierarchy of correlation patterns or postural
synergies, and fused with glove data in a consistent way, so as to provide a
good hand pose reconstruction in spite of insufficient and inaccurate sensing
data. Simulations and experiments on a low-cost glove are reported which
demonstrate the effectiveness of the proposed techniques.Comment: Submitted to International Journal of Robotics Research (2012
Kinematics and Robot Design IV, KaRD2021
This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects
Computational Techniques to Predict Orthopaedic Implant Alignment and Fit in Bone
Among the broad palette of surgical techniques employed in the current orthopaedic practice, joint replacement represents one of the most difficult and costliest surgical procedures. While numerous recent advances suggest that computer assistance can dramatically improve the precision and long term outcomes of joint arthroplasty even in the hands of experienced surgeons, many of the joint replacement protocols continue to rely almost exclusively on an empirical basis that often entail a succession of trial and error maneuvers that can only be performed intraoperatively. Although the surgeon is generally unable to accurately and reliably predict a priori what the final malalignment will be or even what implant size should be used for a certain patient, the overarching goal of all arthroplastic procedures is to ensure that an appropriate match exists between the native and prosthetic axes of the articulation.
To address this relative lack of knowledge, the main objective of this thesis was to develop a comprehensive library of numerical techniques capable to: 1) accurately reconstruct the outer and inner geometry of the bone to be implanted; 2) determine the location of the native articular axis to be replicated by the implant; 3) assess the insertability of a certain implant within the endosteal canal of the bone to be implanted; 4) propose customized implant geometries capable to ensure minimal malalignments between native and prosthetic axes. The accuracy of the developed algorithms was validated through comparisons performed against conventional methods involving either contact-acquired data or navigated implantation approaches, while various customized implant designs proposed were tested with an original numerical implantation method.
It is anticipated that the proposed computer-based approaches will eliminate or at least diminish the need for undesirable trial and error implantation procedures in a sense that present error-prone intraoperative implant insertion decisions will be at least augmented if not even replaced by optimal computer-based solutions to offer reliable virtual “previews” of the future surgical procedure. While the entire thesis is focused on the elbow as the most challenging joint replacement surgery, many of the developed approaches are equally applicable to other upper or lower limb articulations
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