The Link Segment Model Physical Education Essay

Past few years, there is remarkable growth is happening in the study of human movement because of low cost of digital system and cameras although it has been more advancement and their technology it quite still expensive storage and analysis of massive amounts of data that are required to accurately characterize complex motion. This growing interest in the study of human movement is coming from following predominate groups:

Basic scientists are interested in the control of human movement. How the nervous system controls the large number of degrees of freedom necessary to produce smooth, complex movements (or even simple ones!) is poorly understood. The study of the coordination of movement can be compared to the inverse problem faced by the roboticist. The roboticist develops computer programs to produce coordinated movements in a robot.

Human movement s are useful tool and it is studied to understand and treat or diagnose the pathology. Such as gait analysis is often used to help guide the physician contemplating surgery for children with cerebral palsy. The best choice for a tendon remove or muscle lengthening surgery can be predicted by using combinations of movement analysis and biomechanical modeling (e.g., Delp et al., 1996). It can also be used to observe the progression of the disease and the effectiveness of the treatment.

There are two fundamentally different approaches to studying the biomechanics of human movement:

forward dynamics and inverse dynamics. Either can be used to determine joint kinetics (e.g.,

estimate joint moments during movements).

INVERSE DYNAMICS:

INTRODUCTION:

Inverse dynamics is an inverse problem. It commonly refers to either inverse rigid body dynamics or inverse structural dynamics. Inverse rigid-body dynamics is a method for computing forces and/or moments of force (torques) based on the kinematics (motion) of a body and the body’s inertial properties (mass and moment of inertia). Typically it uses link-segment models to represent the mechanical behaviour of interconnected segments, such as the limbs of humans, animals or robots, where given the kinematics of the various parts, inverse dynamics derives the minimum forces and moments responsible for the individual movements. In practice, inverse dynamics computes these internal moments and forces from measurements of the motion of limbs and external forces such as ground reaction forces, under a special set of assumptions [Wikipedia]

An inverse dynamics analysis is frequently used to determine the joint torques and powers in the field of gait analysis and sports biomechanics

http://www.clinicalgaitanalysis.com/teach-in/link-segment.gif

FIG1: Inverse dynamic model of lower limb

MODELLING:

Link Segment Model

In this model lines are represented the body. The individual body segments are represented by rigid links that are characterized by their length, mass, mass centre location, and moment of inertia

FIG 2: Relationship between the anatomical model and link segment model

The technique which is used to finding the net forces and moments (torque) in the body (musculoskeletal system) is known as inverse dynamics.

By using the Newton’s 2nd law we can find joint reaction force

The “muscle moments” are found using the angular form of Newton’s 2nd Law

∑M = I a

Following are the force which acting on the line segment model.

Gravitational forces: forces which act downwards at the CM segment (equal to segment mass Ã- acceleration due to gravity (9.8 m/s2))

External forces: forces measured by a transducer and acting at the center of pressure of the distributed force (e.g., ground reaction force)

Muscle & ligament forces: the net effect of all structures that cross the joint is characterized by the net joint (muscle) moment

Joint reaction forces: It is represent the net force arising between two adjacent segments

FREE BODY DIAGRAM:

Free-body diagrams are diagrams used to show the relative magnitude and direction of all forces acting upon an object in a given situation. Make a free body diagram of the segment.

FIG 3: Relationship between link-segment and free-body diagram

Free body diagram consist of a sketch of the segment of interest, isolated from other segments All force vectors and moments that act on the segment.

RULE:

Sum of all forces which directly influence the free-body

Wherever free-body contacts the environment or another body add unknown force and moment

Simplify unknown forces when possible (i.e., does a force have a known direction, can force be assumed to be zero, is surface frictionless)

EQUATIONS OF MOTION:

FOOT:

Σ Fx = max:

Fx(ankle) = max(foot) – Fx(ground)

Σ Fy = may:

Fy(ankle) = may(foot) – Fy(ground) + mg

Σ Mz = I α:

Mz(ankle) = Ifoot α(foot) – [rankle Ã- Fankle]z – [rground Ã- Fground]z

LEG:

Σ Fx = max:

Fx(knee) – Fx(ankle) = max(leg)

Σ Fy = may:

Fy(knee) – Fy(ankle) – mg = may(leg)

Σ Mz = I α:

Mz(knee) + [rknee Ã- Fknee]z – Mz(ankle) + [rankle Ã- – Fankle]z = Ileg α(leg)

THIGH:

Σ Fx = max:

Fx(hip) – Fx(knee) = max(thigh)

Σ Fy = may:

Fy(hip) – Fy(knee) – mg = may(thigh)

Σ Mz = I α:

Mz(hip) + [rhip Ã- Fhip]z – Mz(knee) + [rknee Ã- -Fknee]z = Ithigh α (thigh)

LIMITATIONS:

Inverse dynamics is a very powerful technique for understanding movement, but it does have some inherent limitations:

it relies on assumptions that are not always valid – specifically:

there may be friction at the joint (e.g. in arthritis)

the distribution of mass in the segment is not uniform, and certainly not concentrated at one point

estimating the joint center of rotation is prone to error (Holden & Stanhope, 1998)

the typical models (e.g. Helen Hayes) used rely heavily on anthropometry to define the hip joint center (because it is deep and so can’t be directly defined by a marker)

the joint center of rotation may also (and often does) move during motion, especially at the knee

some models (e.g. Cleveland model and six degree of freedom model used at NIH using marker triads) make less assumptions in this respect

measurement error (Holden et al, 1997)

the worst of these tends to be inaccuracies in co-alignment of the force platform and motion analysis system

marker motion on the skin – especially “wand” type markers on sticks

motion at the skin-bone interface

marker tracking is sometimes contaminated by errors  due to interpolation when markers go missing and data from some frames is lost

body segment parameters (anthropometry) are approximations and generalizations

very thin or overweight people, children and patients with wasted legs may have different proportions

note that this will mainly affect the swing phase – during stance the ground reaction forces are dominant and accelerations are minimal

special consideration must be given to amputees, in order to use values appropriate to the prosthesis components

error propagation (the errors of the distal joint calculations affect those at more proximal calculations)

co-contraction of antagonistic muscles will cancel out – important in spastic conditions such as cerebral palsy and stroke

it cannot differentiate between different muscles

e.g. we can determine that the joint moment is flexor, but not the relative activity of each flexor muscle

For the kinetic analysis in sagittal plane has been useful tool in understanding mechanisms of normal and pathological gait (Olney et al., 1991; Winter et af., 199Oa). However this can only be provide some part of information, specifically at the hip joint where the hip abductors are essential for the balancing control of the trunk in the frontal plane (MacKinnon and Winter, 1993).

Humans have the ability to running at different speed,but to find the changes in joint kinetics

With the change of speed remains unknow. Although , many of paper have been examined the effect of walking speed on joint kinetics [Murray MP, et al], this descriptive evidence is incomplete for running [4,5 (Alkjaer, Simonsen and Dyhre-Poulsen 2001)]. Running is the principal gait used in play, recreation, sports, and exercise, but little is known about how faster running speeds are achieved, or if there is a typical alteration in joint kinetic patterns to achieve faster running speeds.

As an observer when a person viewing a jogging (running at a moderate pace) and a person sprinting, there would be apparent differences between the two modes of locomotion beyond the obvious difference in speed. A gait has been defined as a pattern of locomotion characteristic of a limited range of speeds described by quantities of which one or more change discontinuously at transitions to other gaits (Alexander, 1989). Although there is agreement that there are differences between running and sprinting, it is not clear whether running and sprinting actually represent two distinct gaits, or whether sprinting is merely “fast” running. Previous studies (Mann & Hagy, 1980; Novacheck, 1995; Stefanyshyn & Nigg, 1997) have reported significant differences in joint moment and power patterns of the lower extremity joints between running and sprinting. All of these studies, however, have compared a single “running” speed to a single “sprinting” speed. It is not clear whether the observed differences occur on a continuum as speed increases, or if there is an abrupt change at some discrete speed. The purpose of this study was to compare ankle and knee joint moment and power patterns over a range of running and sprinting speeds. It was hypothesized that one or more variables would change abruptly as speed increases, thereby distinguishing a run from a sprint.

Sprint is an increased form of running, below figures show the basic parameters like maximum knee extensor moments, peak ankle power generation, and knee power absorption and generation. All of these variables increases show a slight continuum, as none of these variables used to distinguish between a sprint and run.

The transition between walk to run gait has been “triggered” by high levels of dorsiflexors activity (Hreljac et al., 2001). The study shows that there is no variable expected to trigger a change from a run to a sprint, but there was an expectation that variables which could distinguish between a run and a sprint could be found. The current study employed on the basis of discrete point analysis (i.e. maxima and minima) have not shown any adequate differences between the run and sprint behaviours.to distinguish between a run and a sprint, analysis of intralimb coordination (see Stergiou et al., 2001) could be utilized.

The research done by Orendiff and tulchin on joint kinetics of lower limb at different speed of running or more or less sprinting, shows that subjects have a considerable window to choose speed run from predefine values such as 2.5 ± 0.5 m/s, 3.0± 0.7 m/s, 3.5± 0.6 m/s and 4.0 ± 0.5 m/s. Statistically we can observed the effect significantly the across speeds of stance phase of power generation of the hip (p < 0.0001), knee (p < 0.004), and ankle (p < 0.0001).

FIG 4: Sagittal plane joint power for the hip, knee and ankle at four running speeds

This study was conducted on small number of participant which has relatively small range of speed. In this small study, a slight increase in peak power generation in sagittal plane and absorption at the hip and knee and the peak power generation at the ankle are appeared to be connected with constant increase in running speed. However, according to running speed some of all peak sagittal moment patterns cannot be change systematically. Several key strategies appeared after examining each individual’s moment data: Some runners chose a hip-dominant pattern with increasing hip extensor moments as running speed increased. Others chose an ankledominant pattern with increasing ankle plantarflexor moments as running speed increased; some chose a combined hip and ankle pattern. Most runners had increasing knee extensor moments with faster running speeds. This corresponds with previous work showing increased vertical COM excursion and greater vertical forces at faster running speeds. Overall, increasing knee flexor moments in early stance suggesting striding appeared more pronounced at faster running speeds in runners. This coincided with an increase in the COM ΔV, with the fastest speed having a 2% (~8 cm/s) speed change during stance phase.