Identification of human joint mechanical properties from single trial data

Yu, Xu; Hollerbach, John M.

doi:10.1109/10.704874

Cited by 33 publications

(7 citation statements)

References 13 publications

(26 reference statements)

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“…Figure 4a depicts actual and fitted mean trajectories for the three target levels of three subjects with damping indexes corresponding to different behavior; INDEX ~ 1 for critical-damping (subject #4-DARK, UPRIGHT, DOWN), INDEX < 1 for under-damping (subject #1-LIGHT, UPRIGHT, UP), and INDEX > 1 for over-damping (subject #6-DARK, INVERTED, UP). Interestingly, when averaging across subjects, note that the under-damped performances detected previously in illuminated environments for manual tracking (Xu and Hollerbach 1998;Engel et al 2000), elbow joint movements to perturbations (Lacquaniti et al 1982), and goal-directed movements (Bennett et al 1992) were also observed in Table 2 Correlations with mean error r values are provided in cases where significant correlations (P < .05) were observed for the comparisons for all subjects for the given body orientation (ORIENT = UPRIGHT or INVERTED), starting arm position (ARM = DOWN or UP), and target distance for LIGHT and DARK visual conditions combined (ns not significant). The number in parentheses indicates the number of individual subjects with corresponding significant same signed correlations across individual trials (or expected signed correlation for ns outcomes).…”

Section: Modeling and Damping Indexmentioning

confidence: 92%

“…The angular change of the fingertip during manual tracking of moving targets with direction changes was modeled as an under-damped response for most directional changes and approached a critically damped response for the largest directional changes of 150° or more (Engel et al 2000). Under-damped responses were also reported for elbow joint movements during manual tracking (Xu and Hollerbach 1998), to perturbations (Lacquaniti et al 1982) and for goal-directed movements (Bennett et al 1992). In each of these cases, subjects reached or exceeded the targeted goal.…”

Section: Movement Modelingmentioning

confidence: 99%

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Different damping responses explain vertical endpoint error differences between visual conditions

et al. 2016

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Upright people making goal-directed movements in dark environments often vertically undershoot remembered target locations when compared to performances in illuminated environments. In this study, we wanted to determine whether influences of the gravitational pull and/or type of muscle activation could explain differences in vertical endpoint precision between movements to visually remembered target locations with and without allocentric cues available. We also used a simple damping model for movement trajectories to describe potential differences in behavior between visual conditions. Subjects performed straight arm pointing movements to REAL target locations or remembered target locations in darkness (DARK) or normal room lighting (LIGHT). Performances were made from UPRIGHT and INVERTED (upside down) body orientations. Starting arm position (UP by the ear; DOWN on the thigh) also varied so that eccentric or concentric muscle contractions for arm flexion or extension movements occurred primarily along the earth-fixed vertical either with or against the gravitational pull. Effects of visual condition (LIGHT, DARK), body orientation (UPRIGHT, INVERTED), starting arm position (UP, DOWN), and target level (Near, Middle, Far) on elevation endpoint errors revealed that subject's errors in the DARK were more negative than those in the LIGHT. Errors correlated well with movement displacement to reveal the common vertical undershooting bias in darkness exacerbated by inverting the body or requiring greater movement excursions. Although influences of gravitational pull and muscle activation type could not explain differences between visual conditions, modeling revealed critically damped behavior in the DARK and under-damped behavior in the LIGHT to indicate muscle energy dissipation without vision.

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Section: Modeling and Damping Indexmentioning

confidence: 92%

Section: Movement Modelingmentioning

confidence: 99%

Different damping responses explain vertical endpoint error differences between visual conditions

et al. 2016

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“…These approaches have been used to estimate both mechanical [4, 5] and physiological systems [6–10]. However, parametric methods require knowledge of the system structure, thus may not be useful for physiological systems where little is known a priori .…”

Section: Introductionmentioning

confidence: 99%

System Identification of Physiological Systems Using Short Data Segments

Ludvig

Perreault

2012

IEEE Trans. Biomed. Eng.

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System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Non-parametric techniques can be useful for identifying system structure, but these typically assume stationarity, and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying, non-parametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.

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“…Accordingly, in defining impedance characteristics, stiffness and damping coefficients have been mostly assumed to be variable [61, 167] with resulting dependencies upon deformation and rate of deformation [14]. …”

Section: Factors That Affect Mechanical Impedancementioning

confidence: 99%

Mechanical Impedance and Its Relations to Motor Control, Limb Dynamics, and Motion Biomechanics

Mizrahi

2015

J. Med. Biol. Eng.

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The concept of mechanical impedance represents the interactive relationship between deformation kinematics and the resulting dynamics in human joints or limbs. A major component of impedance, stiffness, is defined as the ratio between the force change to the displacement change and is strongly related to muscle activation. The set of impedance components, including effective mass, inertia, damping, and stiffness, is important in determining the performance of the many tasks assigned to the limbs and in counteracting undesired effects of applied loads and disturbances. Specifically for the upper limb, impedance enables controlling manual tasks and reaching motions. In the lower limb, impedance is responsible for the transmission and attenuation of impact forces in tasks of repulsive loadings. This review presents an updated account of the works on mechanical impedance and its relations with motor control, limb dynamics, and motion biomechanics. Basic questions related to the linearity and nonlinearity of impedance and to the factors that affect mechanical impedance are treated with relevance to upper and lower limb functions, joint performance, trunk stability, and seating under dynamic conditions. Methods for the derivation of mechanical impedance, both those for within the system and material–structural approaches, are reviewed. For system approaches, special attention is given to methods aimed at revealing the correct and sufficient degree of nonlinearity of impedance. This is particularly relevant in the design of spring-based artificial legs and robotic arms. Finally, due to the intricate relation between impedance and muscle activity, methods for the explicit expression of impedance of contractile tissue are reviewed.

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Identification of human joint mechanical properties from single trial data

Abstract: Abstract-

Cited by 33 publications

References 13 publications

Different damping responses explain vertical endpoint error differences between visual conditions

Different damping responses explain vertical endpoint error differences between visual conditions

System Identification of Physiological Systems Using Short Data Segments

Mechanical Impedance and Its Relations to Motor Control, Limb Dynamics, and Motion Biomechanics

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