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The primary aim of this project is to develop an objective functional tool for measuring the Upper Extremity (UE) movement of the body. The developed measuring tool will be based on analyzing UE movements using information extracted from coordination and variability of the body parts (segments, joints) involved in such movements such as arms, hands, trunk, shoulders, and wrists. The measures of coordination and coordination variability together with three dimensional joint kinematics will allow the researchers to define direction, location, and degree (angle) of movements of these body parts graphically. The aim will be achieved through the following objectives:
• Investigate the suitability and limitations of using current approaches to identify abnormal disorders in gait analysis in UE movement.
• Determine whether measures of coordination and variability in coordination can be used to distinguish healthy/unhealthy movement patterns in the upper extremity.
• Develop a new approach based on the measurement of coordination and coordination variability which would enable us to identify the location, direction and degree of abnormal movement and compare this with the traditional methods.
• Create a method to distinguish abnormal movement and test ability of method to capture improvements in movement and function by monitoring patients who are undergoing rehabilitation programs.

In order to achieve the above aim and objectives I need please write a literature review about the answer of following questions but not in the format of question and answer , the answer of them should come in different paragraphs but each paragraph related to each other in a way

1- What is the coordination and coordination variability
2- What is the problem of assessing Upper limb?
3- Which groups have benefit from our research ? for example help rehabilitation of patients, athletics
and etc.
4- Use of Dynamical system approaches in the lower limb or in sports ?

The analyzing approach: The development of the discrete relative phase technique, continuous relative, is aimed at evaluating the coupling and coordination between different body segments.

4- What is the importance of coordination variability and related to my project ?


Please read other pages here as well.

For example, one of the major problems in analysing the movement of the upper extremity (UE) is the large movement variability between repetitions and different subjects. Consequently, quantitative measures of UE movement and function are often unable to identify the presence, location, direction and degree of abnormalities. Studies in lower limb movement and sports biomechanics have shown that coordination variability (CV) is an important factor in achieving success in the execution of an action. Measures of coordination and CV have not been used in kinematic assessments of UE.

Locomotion at different speeds entails coordinating the many degrees of freedom, where control activity is involved in recruiting physiological mechanisms with high degrees of freedom into mechanical movement “templates” or models with low degrees of freedom[1].
The multiple degrees of freedom involved in the coordination and control of human movement are a potential source of variability. In Biomechanics and motor control, variability is traditionally equated with noise, is considered detrimental to system performance and typically eliminated from data as a source of error.
In assessment of coordination changes due to learning, aging and disease, the presence of variability is still regarded as one of the most powerful indicators of poor performance.
Coordination variability refers to the range of coordinative patterns that the organism exhibits while performing a movement. It also is often quantified as between-trial or between-gait cycle standard deviation of the movement.
Research in biomechanics is beginning to explore the role of variability in movement. An important emphasis is the role that variability plays in the biomechanics of sport, injuries and disease. When a movement is performed repetitively, the motions of the body’s segments will vary somewhat, even for a cyclical motion like running. As mentioned previously, the traditional view is that variability decreases with the level of skilled performance and increases with the level of injury or disease.
In summary, from a dynamical system perspective, variability is not inherently good or bad but rather indicates the range of coordination patterns used to complete the motor task.
It has recently been suggested that the coordination or coupling relationships between segments may be an important line of investigation owing to the fact that motor behaviors may be distinguished by the coordination between entire limbs and body segments.
Also, the coordination between adjacent anatomical structures, such as the subtalar and knee joints, has been implicated in the etiology of injuries. However, quantifying the coordination between two body segments is problematic.

Although it appears relatively simple, upper-limb movement is biologically complex. In order to complete a desired action, the muscles are required to coordinate so that movement permitted at each joint is controlled. Joints can be classed in different ways depending on the planes around which they allow movement. The shoulder, for example, is capable of producing; Pitch – rotation in the sagittal plane, Yaw – rotation in the coronal plane and Roll – rotation in the transverse plane (Singh, 2014). Each of these rotations is a Degree of Freedom (DoF) and every joint within a system allows for an additional one to three which need to be controlled.
To be repeatedly successful at a task, a person must be able to recreate the same finishing parameters every time they complete the process. If the task involves throwing then an identical release point, velocity and velocity vector will conclude in the same result because, as pointed out by Kudo et al (2000), these parameters “inevitably determine the trajectory of the projectile”. With this information, movement analysists monitored subjects to find out how the body recreated identical finishing parameters, and why some people were less capable of repeated success. The explanation they were hoping to find would be useful in two ways; the first to help in rehabilitation of patients and to monitor their progress, the second within a sporting interest to understand what makes an athlete successful at a skill and how to coach or train the body to reach maximum potential.
At first, it was theorised that in order to achieve identical finish parameters, the subject would have to practice the motion frequently to the point that intra-variability was removed (Newell and Corcos, 1993). This way the coordinated joint DoFs could be controlled precisely every time, and therefore, the subject would repeat the successful result. However, it was discovered that even within elite athletes movement variability (MV) was still detected (Bartlett, Wheat, and Robins, 2007; Davids et al., 2003). At first this was explained through equipment and monitoring technique faults creating noise around the data. But, even as systems and methods improved, the variation or ‘noise’ was never removed and there became a need for a better fitting theory as to why.
Recently the dynamic systems approach was applied to human movement study. This is because the constraints on an individual and the environmental factors are so complex, MV in biological systems is unavoidable (Hamill et al, 1997; Davids et al., 2003). Instead, through practice, people develop a range of accessible movement criteria to draw upon so successful finish parameters can be recreated (Davids et al., 2003; Bartlett, Wheat, and Robins, 2007). It appears that between set maxima and minima skilled and highly coordinated participants can compensate the task parameters against each other, continually adjusting due to sensor feedback to achieve the same final result.
To find this ‘envelope’ of successful parameters, a trial of multiple coordination patterns is required. The subject explores the possible combinations and identifies within which range they can produce success. They achieve this by freezing and unfreezing the various DoF involved in the action (Davids et al, 2003). By slowly releasing joint constraints, new patterns are tested revealing new
combinations and their effectiveness. This infers that in learning a new skill there is no ‘wrong’ action, even in an unsuccessful attempt rather that it is an important part of the selection process to find the desired maxima and minima (Latash and Anson, 1996).
So what is the importance of coordination variability? In a clinical setting, it can be used to compare the movement of healthy subjects to patients. Hamill et al (1999) conducted a kinematic motion analysis study to compare individuals with and without patellofemoral pain (PFP). They found that those with PFP produced a higher repeatability within their respective segmental coordination patterns than those without. They further concluded that this inflexibility may cause an “overuse situation” where instead of varying the stress on the joint soft tissues, it focuses it. This risks initiating wear on the focused tissues and could result in more pain or degenerative changes.
Moreover, the study of MV can have implications on the way rehab is conducted and progress is
reported. Davids et al (2003) called for a shift away from the ‘medical model’ as the accepted norm. Instead, they suggested the changes in behaviour should be viewed as an adaptation to the internal and external constraints placed upon the individual. Allowing the patient to adapt may encourage them to find a new range of successful coordination patterns which can coincide with the changes
caused by injury, age and disease. This view describes a person’s optimal ‘envelope’ as individual, and casts out the one size fits all theory.
As well as this, sports biomechanics have been applying it within their field. In a competitive environment athletes and coaches are constantly trying to find the best ways to carry out a skill. By studying motion analysis of elite athletes conclusions can be drawn as to what components are needed to produce a successful product. In a paper on MV in the basketball free-throw, Button et al (2003) found that joint-space variability reduced with increasing experience, but that elbow-wrist joint coordination variability distinctly peaks towards the end of the action, most notably in the highest skilled participant. These findings suggest that experience through practice and repetition allows elite athletes to home in towards the successful range of coordination patterns reducing their joint-space variability. However, it also shows that “players need to maintain a functional level of joint- space adaption” in order to react to and counteract changes created by the internal and external environment (Button et al., 2003).
This prompts Button et al to advise coaches and teachers to accept variability and the role it plays as athletes experiment to define their successful range. The conclusions are similar to those of Preatoni et al (2010) who also found better flexibility in skilled race walkers and predicted these athletes were better suited to dealing with changes in the environment. The results of the study lead the authors to believe that this analysis could refine “performance technique, training/rehabilitation procedures, motor learning and underlying injury”.
Due to the research mentioned above, MV is an accepted explanation for intra-subject variance or ‘noise’. In
the analysis of data comparing skilled and unskilled subjects, researchers have drawn
conclusions about how different abilities display different degrees of variability (Button et al., 2003; Preatoni et al, 2010). Some have gone as far as to study the dissimilarities in coordination profiles between the positions and roles of players (Brétigny et al., 2011). This suggests the patterns displayed by a subject are therefore correlated to performance. Although many of the papers infer the use of MV for rehabilitation purposes, one has yet to be published confirming this. The following study aims to use the dominant (skilled) and non-dominant (unskilled) arms to determine whether coordination measures can be used to assess performance. Furthermore, early trials will be compared with late trials for the non-dominant sides in an attempt to assess whether improvement in coordination has occurred over the testing period and whether these methods are proficient in measuring improvement.

It is hypothesised that the comparison of the CV and coordination measures from both sides of the body will reveal trends which can be used to differentiate performance levels. Furthermore, coordination patterns of the non-dominant side will change from the start of the experiment to the end. These changes could be characterised as progression.

These focuses on the motion of the upper limb in some specific tasks related to activities of daily living. Clinicians regularly assess the quality of life of patients with upper limb disability or injury through either a questionnaire or demonstrations relating to the patient’s ability to perform a specific task. Tasks which relate to independent living such as feeding, washing and dressing are the usual focus of these assessments.
Current research into this area use either simulated tasks (van Andel et al., 2008), such as touching the mouth instead of feeding with a spoon, and actual tasks with props (Doorenbosch, Harlaar and Veeger, 2003) to understand the range of motion and co-ordination patterns in activities of daily living. In the studies using simulated tasks it is not clear as to whether participants were aware of what the task was meant to be simulating, however most specifically point out the fact that the task is being used to represent a daily activity. There is no clear explanation as to the difference in choice of activity but one can assume that the simulated task is chosen in order to reduce variability of motion between subjects. It is not clear however, how simplifying these activities to such basic movements affects the results in terms of joint angles and co-ordination patterns. This study will be used to compare the effectiveness of using tasks which simulate the activity of daily living to the use of the actual activity with props, for the assessment of upper limb movement function. Healthy subjects will be used to assess the normal co-ordination patterns in each trial task. An optoelectronic system with passive markers will be used to measure the precise progression of the upper limb during these tasks. The motion of the trunk, shoulder, arm, forearm and hand will all be measured and analysed.
Over the past couple of decades, the dynamical system has become the accepted framework when describing coordination patterns in human movement. Evidently human motor control processes are fundamentally noisy, and within each action a person must continuously sense and adjust the motion in order to produce successful results repeatedly (Davids, et al., 2003). This positive interpretation of variability needs to be understood by professionals and incorporated into modern teaching, as well as diagnostic and rehabilitation procedures. As of yet, however, no work has been undertaken into quantification or simplification of upper body CV analysis for this purpose.
Studies such as those of Button, et al. (2003) have determined that the levels of CV change as a function of skill level and Kudo, et al. (2000) suggested that skilled participants are those with “active and functional compensatory processes”. Further sport related developments have progressed to use CV to produce profiles that categorise field hockey players by position (Brétigny, et al., 2011). There have also been developments highlighting the benefits of variability in reducing pain, and the potential it has to help avoid injuries if identified early (Hamill, et al., 1999).
Findings like these have prompted many papers to suggest a change in approach by learners, coaches and clinicians (Bartlett, et al., 2007; Davids, et al., 2003; Preatoni, et al., 2013). In order for this transition to occur, several developments need to take place. Firstly, a better understanding of the variability levels which define a ‘skilled’ person is required. Secondly, an understanding of how CV develops throughout the process of learning, and finally whether the same findings can be identified within a normal population completing ADLs.

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