Coordination of thumb joints during opposition

Li, Zong Ming; Tang, Jie

doi:10.1016/j.jbiomech.2006.02.019

Cited by 93 publications

(79 citation statements)

References 37 publications

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“…Recently, noninvasive, marker-based video methods have been used to study advanced kinematic features thus providing a more accurate representation of thumb motion. 2,[8][9][10][11] Marker-based motion analyses were applied to study thumb pathokinematics in individuals with cervical myelopathy 12 and lower median nerve block; 13 the authors reported aberrant thumb kinematics during pinch movements.Thus far, our understanding of thumb motion by each muscle remains dominated by basic functional anatomy using unidirectional, simplified nomenclature such as flexors or extensors. Therefore, this study was undertaken to elucidate thumb joint kinematics generated by individual muscles.…”

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confidence: 99%

“…Kinematically, thumb motion results from complicated combinations of flexion/extension, abduction/ adduction, and pronation/supination (i.e., axial rotation) at the carpometacarpal (CMC), metacarpophalangeal (MCP), and interphalangeal (IP) joints. 1,2 The movement capability of the thumb was previously determined by measuring the linear distance between the thumb tip and the fingertips or other anatomical landmarks, 3,4 but thumb pathology can be characterized by examining the angular motion at the individual joints. Manual goniometry is commonly used in the clinic to evaluate statically joint mobility in a single plane.…”

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Complex, multidimensional thumb movements generated by individual extrinsic muscles

Tang

Chakan

et al. 2008

Journal Orthopaedic Research

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The objective of this study was to investigate three-dimensional thumb joint movements produced by individual extrinsic thumb muscles. Ten cadaveric arms were dissected to expose the musculotendinous junctions of the flexor pollicis longus (FPL), abductor pollicis longus (APL), extensor pollicis brevis (EPB), and extensor pollicis longus (EPL). Each muscle/tendon was loaded to 10% of its maximal force capability whereas three-dimensional angular movements of the carpometacarpal (CMC), metacarpophalangeal (MCP), and interphalangeal (IP) joints were obtained simultaneously. We found that each extrinsic muscle produced unique joint angular trajectories in multiple directions. The FPL, APL, EBP, and EPL generated two, two, three, and six movements, respectively. The extrinsic muscles all together generated eight movements among the multiple thumb joints. High interjoint coordination was shown between the MCP joint flexion and IP joint flexion by FPL loading, as well as between the MCP joint extension and IP joint extension by EPL loading. High intrajoint coordination was observed between extension and supination at the CMC joint by the APL, EPL, and EPB. We concluded that each muscle produces movements in multiple joints and/or in multiple anatomical directions. The findings provide a novel insight into the biomechanical roles of the extrinsic muscles of the thumb. The human thumb is exceptionally mobile, strong, and dexterous partially due to its sophisticated musculature. Kinematically, thumb motion results from complicated combinations of flexion/extension, abduction/ adduction, and pronation/supination (i.e., axial rotation) at the carpometacarpal (CMC), metacarpophalangeal (MCP), and interphalangeal (IP) joints. 1,2 The movement capability of the thumb was previously determined by measuring the linear distance between the thumb tip and the fingertips or other anatomical landmarks, 3,4 but thumb pathology can be characterized by examining the angular motion at the individual joints. Manual goniometry is commonly used in the clinic to evaluate statically joint mobility in a single plane. Electrogoniometers can be used to monitor dynamic joint motion, 5 but they can be too bulky to record simultaneous movements from multiple joints within the thumb and can interfere with delicate thumb movements. X-rays, 1,6 and CT 7 have been used to study in vivo thumb kinematics, but the amount of kinematic information is limited. Recently, noninvasive, marker-based video methods have been used to study advanced kinematic features thus providing a more accurate representation of thumb motion. 2,[8][9][10][11] Marker-based motion analyses were applied to study thumb pathokinematics in individuals with cervical myelopathy 12 and lower median nerve block; 13 the authors reported aberrant thumb kinematics during pinch movements.Thus far, our understanding of thumb motion by each muscle remains dominated by basic functional anatomy using unidirectional, simplified nomenclature such as flexors or extensors. Therefore, this ...

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confidence: 99%

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confidence: 99%

Complex, multidimensional thumb movements generated by individual extrinsic muscles

Tang

Chakan

et al. 2008

Journal Orthopaedic Research

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“…This concept can be understood in a similar way to the formulation introduced by Li [53,54], who pointed out that in the case of human joints, the motor coordination establishes functional relations among the joint variables. The same idea has been also considered by Page and coauthors, who determined the optimal path traced by the instantaneous screw axis of human joints in cyclical motions with one functional degree of freedom [55,56].…”

Section: Resultsmentioning

confidence: 93%

Modeling of the condyle elements within a biomechanical knee model

et al. 2011

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The development of a computational multibody knee model able to capture some of the fundamental properties of the human knee articulation is presented. This desideratum is reached by including the kinetics of the real knee articulation. The research question is whether an accurate modeling of the condyle contact in the knee will lead to reproduction of the complex combination of flexion/extension, abduction/adduction, and tibial rotation observed in the real knee. The model is composed by two anatomic segments, the tibia and the femur, whose characteristics are functions of the geometric and anatomic properties of the real bones. The biomechanical model characterization is developed under the framework of multibody systems methodologies using Cartesian coordinates. The type of approach used in the proposed knee model is the joint surface contact conditions between ellipsoids, representing the two femoral condyles, and points, representing the tibial plateau and the menisci. These elements are closely fitted to the actual knee geometry. This task is undertaken by considering a parameter optimization process to replicate experimental data published in the literature, namely that by Lafortune and his coworkers in 1992. Then kinematic data in the form of flexion/extension patterns are imposed on the model corresponding to the stance phase of the human gait. From the results obtained, by performing several computational simulations, it can be observed that the knee model approximates the average secondary motion patterns observed in the literature. Because the literature reports considerable inter-individual differences in the secondary motion patterns, the knee model presented here is also used to check whether it is possible to reproduce the observed differences with reasonable variations of bone shape parameters. This task is accomplished by a parameter study, in which the main variables that define the geometry of condyles are taken into account. It was observed that the data reveal a difference in secondary kinematics of the knee in flexion versus extension. The likely explanation for this fact is the elastic component of the secondary motions created by the combination of joint forces and soft tissue deformations. The proposed knee model is, therefore, used to investigate whether this observed behavior can be explained by reasonable elastic deformations of the points representing the menisci in the model.

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“…In this approach, each time series is used as a variable whose observations are the recorded values at each time for one or more trials (2,6,8,9). The obtained principal modes do not quantify the variability across subjects, although aggregated patterns of movement or classification processes can be performed from the individual principal modes, implicitly assuming that the structure of such eigenpostures is the same for all subjects (7).…”

Section: Discussionmentioning

confidence: 99%

“…PCA has been widely used in the field of Biomechanics to describe continuous waveforms (1), (2).Their applications includes fields such diverse as gait analysis (1,(3)(4)(5)(6)(7), equilibrium control (8), coordination of thumb joints (9), analysis of lifting techniques (10, number of discrete variables (almost a thousand), which then needs a high number of PC's to explain a representative percentage of the original variance. On the other hand, in these applications, PCA is used from a multivariate perspective: a finite set of discrete variables are obtained from one or several continuous time series by sampling at arbitrary time intervals.…”

Section: Introductionmentioning

confidence: 99%

Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Epifanio

Ávila

Page

et al. 2008

Med Biol Eng Comput

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This paper presents an application of Functional Principal Component Analysis (FPCA) to describe inter-subject variability of multiple waveforms. This technique was applied to the study of sit-to-stand movement in two groups of people, osteoarthritic patients and healthy subjects.Although STS movement has not been much applied to the study of knee osteoarthritis, it can provide relevant information about the effect of osteoarthritis disease on knee joint function.Two waveforms, knee flexion angle and flexion moment, were simultaneously analysed. Instead of using the common multivariate approach we used the functional one, which allows working with continuous functions without neither discretization nor time scale normalization.The results show that time-scale normalization can alter the FPCA solution. Furthermore, FPCA presents a better discriminatory power compared to the classical multivariate approach. Then, this technique can be applied as a functional assessment tool, allowing the identification of relevant variables to discriminate heterogeneous groups, such as healthy and pathological subjects.

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Coordination of thumb joints during opposition

Cited by 93 publications

References 37 publications

Complex, multidimensional thumb movements generated by individual extrinsic muscles

Complex, multidimensional thumb movements generated by individual extrinsic muscles

Modeling of the condyle elements within a biomechanical knee model

Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

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