Chapter 3 The Role of Three Dimensional Analysis in the Assessment of Motor Expertise

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Starkes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 3 THE ROLE OF THREE DIMENSIONAL ANALYSIS IN THE ASSESSMENT OF MOTOR EXPERTISE HEATHER CARNAHAN Department of Kinesiology University of Waterloo, Waterloo, Ontario, N2L 3GI Many of us are interested in what makes one individual more skilled than another, and the factors that influence the development of motor expertise (see Allard & Starkes, 1989; Schmidt, 1988). There are many perspectives that can be taken to address the topic of skill ranging from examining the manner in which skills are learned, to comparing the motor performance of expert and novice individuals. Those who study motor skill learning have investigated the variables that influence the acquisition and retention of skill, for example, practice scheduling and the presentation of feedback (for reviews see Chamberlin & Lee, 1992; Salmoni, Schmidt & Walter, 1984). While this approach has examined variables that affect the end result or final product of a movement, little attention has been paid to how the form of a movement changes with learning. Gentile (1972) however, states that effective motor skill teaching requires an analysis and understanding of the nature or characteristics of the to be learned skill. Thus, before we can teach skills, we have to understand what is it about a movement that makes it "skilled". An alternative approach to looking at skilled performance is to look at expert and novice differences, where the perceptual abilities or cognitive strategies associated with skilled performance are examined and compared (Allard & Bumett, 1985; Allard, Graham & Paarsalu, 1980; Allard & Starkes, 1980; Charness, 1979; Starkes & Deakin, 1985). In this situation, expert and novice performers are often categorized based on years of experience or national and international competitive ranking; not on some objective measure of quality or form of movement. On what basis are we defining motor skill? Welford (1976) describes skill as a quality of performance which is developed through training, practice and experience. While this definition is acceptable, to actually apply this definition to the categorization of real movements is very difficult. Rarely do we objectively quantify the form of an expert performer when we investigate skill. The goal of this chapter is to review how our definitions of skill may be altered depending on how we define what constitutes a skilled movement. As well, I hope to touch on how information about the form of a movement may influence our thinking about what really is expert performance. When the acquisition of skilled performance is evaluated, or we want to investigate the

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role of perception or cognition in skilled performance we are left with one common problem; that is, how do we quantify skill? In real life, we may use summary statistics like batting average or runs batted in to describe the skill level of a baseball player (see James, 1991; Siwoff, Hirdt, Hirdt & Hirdt, 1992) . Entire volumes are published each year full of statistics which attempt to capture the essence of baseball skill. We want to be able to say that if one player has better stats than another player, then he/she is the more skilled player. This rationale even has legal precedent, and is used in arbitration settlements when professional athletes negotiate their salaries which are directly related to their playing skills. If we as researchers could also adequately define skill based on one summary performance number or statistic then our job would be much easier. When we assess skill acquisition in a laboratory setting, performance is often described in terms of the final result of a movement, by using measures such as reaction time, movement time, terminal accuracy, force output, etc. (see Schmidt, 1988). These measures tell us nothing about how the movement was performed, only about the end result of the action. However, it is quite possible to have very different movement patterns produce identical movement outputs. If the final output is the same, would both movement patterns represent equally skilled movement? Before we can look at the sorts of variables that influence skill, we need to define precisely what constitutes skilled movements. However, this is not a very straightforward task. Is skill based on outcome only, or does the form of the movement matter? Can an individual be considered skilled if their outcome is poor but their form is perfect? Most likely these two aspects of skill (outcome and form) are very highly related, that is, superior movement form will result in superior outcome. For example, the shot putter that can adequately coordinate the generation of forces in all their joints will most likely put the shot the farthest, or the skater with the best technique will jump the highest and spin the fastest. In these examples, superior form will generally result in superior outcome. However, this is not always m e . We can all think of examples where a performer is extremely successful in terms of outcome, with a particularly unconventional style or motor pattern. The converse is also possible, in which a performer has a consistent and acceptable motor pattern, but is unable to produce a highly successful movement outcome (Gentile 1972). Form also plays a critical role in activities like diving, dance, gymnastics, or skating where the actual form of the movement is the main objective of the skill. These types of skills can be categorized as "closed" skills, which are performed in a static, unchanging environment (Poulton, 1957). The evaluation of success in these types of skills is based on how the skills are performed. As Allard and Starkes (1992) point out, "for closed skills, motor patterns ARE the skill; it is critical that the performer be able to consistently and reliably reproduce a defined, standard pattern" (pp.127). In these types of closed skills, it is difficult to quantify and thus understand the movement attributes that characterize expert performance. For years, judges have attempted to quantify whether one movement form is superior to another. But, anyone who has watched diving, figure skating or gymnastics competitions knows that the classifications made by judges are not without question. It is difficult to not let prior experience, political views and opinions interfere with judgements of movement form (Ste-Marie & Lee, 1991). A

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more objective approach would be to quantify or measure movement form with some sort of tool. However, individuals studying motor expertise have rarely done this. This is partly because until recently, the technology necessary to accurately quantify natural human movement has not been available, either because the technology has simply not existed, or it has been too expensive. But, over recent years, two and three dimensional video and optoelectric systems have been developed that are now accessible to many motor behavior researchers. While these systems allow an objective and systematic analysis of movement patterns, they art not the only types of systems that can be used for quantifying movements. There are direct measurement techniques which rely on devices such as goniometers, accelerometers, graphics tablets or manipulanda hooked up to potentiometers. These systems generally constrain movements to one plane and will not be discussed in this chapter in any detail. For a discussion of these types of systems see Winter (1990). Instead I would like to focus on the quantification of natural unconstrained movements using imaging measurement, techniques which are most often used in the quantification of manual reaching and grasping, gait, and complex skills like diving, running and throwing.

How Are the Data Collected? Two and three dimensional imagery systems can be divided into three main types: cinematography, optoelecmc and video, with the latter two being the most frequently used. With video systems, video television cameras are used to record movement on a video tape, after which the video image of the movement is digitized and the position of the image recorded by a computer. With optoelecmc systems, small markers which emit infrared light are attached to the subject, and specialized infrared cameras record the position of the markers; no image of the actual subject is recorded. Each system has advantages and disadvantages which depend on the environmental conditions and type of movements to be measured. There are many parameters that must be considered in deciding what type of measurement system is preferred. Below are listed some of the most important ones: Sampling Rate The sampling rate of most video systems is limited to 60 Hz. (High speed video cameras can sample at higher frequencies but their cost is often prohibitive). This is generally acceptable for most human movement, which is usually low frequency (3 to 10 Hz). However, if higher sampling rates are needed optoelecmc systems can sample up to several thousand Hz. Higher sampling rates are preferable for monitoring a skill in which a high-impact is being monitored, such as hammering. Impact results in a high frequency component to the movement, and this is more effectively measured with higher sampling rates. Higher sampling rates are also useful if acceleration is going to be examined, essentially because the process of differentiation involves calculating differences between sample points, so with more samples the derivative is more representative. Sampling theorem states that the minimal sampling rate for a signal should be 2N+1, where N refers to the frequency component of the signal being measured. Thus, if human movement is 10 Hz, then a minimum of 21 Hz sampling rate will be adequate (Winter, 1990). However, a more generous estimate of 10 times the frequency component of the signal is probably more acceptable.

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Range and Accuracy Video systems generally have a larger range. Optoelectric systems are limited in both potential distance away from the camera and working space volume, based on the power or range of the light emitted from the diodes. Although it depends on calibration of the system and the size of the volume being used, optoelecmc systems are currently more accurate than video based systems. Some optoelecmc systems (e.g. Optotrak) are accurate to the fraction of a millimeter at close volumes. However, for most types of motor skills that may be of interest, this degree of accuracy is probably not necessary. A carefully calibrated video system should provide adequate accuracy (e.g. 2 to 5 mm error). Rotation Video systems are a little more forgiving of markers rotating out of view of the cameras than are optoelecmc systems. With an automatic digitizing video system, if a marker goes out of view, the position of the marker can be hand digitized to replace the missing portions of data. This is because a video image of the subject exists and the human operator can "guesstimate" the position of the missing marker even if it is not in view. The disadvantage of this of course is that a human operator is necessary during the digitization process and this can be extremely time consuming. As well, error is introduced into the system. With an optoelecmc system, data is automatically digitized. However, if a marker if obscured or rotates out of view, the data can only be replaced with interpolation techniques where missing portions of curves are reconstructed using various splines. However, if the appropriate order of spline is not used, the reconstructed data could misrepresent the original missing part of the curve. Regardless of how the data are gathered (and even when using 3 dimensional systems), movements generally have to be planar for all the markers to be seen by all the cameras. Once a subject rotates away from the camera, the markers on their body become obscured from camera view. However, with improved software and multiple camera systems, this is less of a problem. With enough cameras, a marker can be tracked from one set of cameras to an adjacent set. However, this is an expensive solution since additional cameras are needed, as well as complicated software to integrate the information from the various camera pairs. This approach has been successfully used by biomechanicians, however, because of the complexity of the procedure, has not yet been adopted by those looking at skilled performance from a cognitive perspective

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Physical Constraints With video systems, small adhesive markers are placed on the subject demarcating points of interest such as the wrist, fingers, elbow, knee etc. The subject is free to move naturally after the markers have been positioned. With optoelecuic systems, small light emitting diodes are taped to the body. Subjects are constrained to some extent by the wires leading to the light emitting diodes, yet the impact of this restraint will depend on the type of activity subjects perform. Another constraint to consider is that of the actual physical or geographical location of the data collection session. Video systems are generally more flexible in where they can be used. For example, they can be set up out of doors or in actual competitive settings. Actual

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diving competitions have been filmed and later digitized for biomechanical analysis of the dives (Miller. Hennig, Pizzimenti, Jones & Nelson.1989; Miller, Jones, Pizzimenti, Hennig & Nelson, 1990). Optoelecmc systems are generally constrained to the indoor laboratory setting. Since they depend on detecting the location of small infrared lights, any additional infrared light in the testing environment (like that produced by the sun) will interfere with the accuracy of the system. As well, an optoelectric system could not be used for quantifying aquatic activities such as swimming or diving because of the electric feed required to power the light emitting diodes which are placed on the subject. Volume of Data An important factor to consider when assessing the merits of motion analysis is the volume of data generated with video and optcelectric systems (Winter, 1990; 1991). If movement time or accuracy is used to describe a movement, then one or two numbers can be used to represent the performance. However when kinematic or kinetic information are used to describe movement many hundreds or even thousands of data points are collected. For example, if only one marker was placed on the arm to represent the three dimensional translational motion of a reaching movement, which took one second, and was sampled at 100 Hz,there will be 300 data points to represent that skill. You can imagine how many data points are involved in representing the movement of an entire arm and hand where markers are placed on the fingers, wrist, elbow and shoulder. These large volumes of data increase the cost of research because the volumes of numbers are time consuming to deal with, require powerful computers, and take up large amounts of disk space etc. There is a need to evaluate whether the added expense in dollars and time is providing sufficient unique information to warrant the investment. Will this abundance of information be used to develop new theories regarding motor skills, or will we get caught in the fashionable urge to collect volumes of data, for its own sake? While I may have painted a somewhat discouraging scenario, it is my opinion that this approach is warranted, and that as additional data are collected, patterns will emerge which will direct theoretical development.

How Are the Data Analyzed? Once the data are collected there are many ways they can be represented (see Enoka, 1988; Winter, 1990; 1991). At the first level of analysis are the temporal measures; these measures deal with the timing aspects (e.g., movement time) of the whole movement. AS previously mentioned, these are the types of measures that have typically been used to quantify skilled movement. At the next level of analysis are the kinematic measures, which describe linear or angular motion, but do not consider the forces involved in the movement. Imaging measurement systems will provide an output of displacement as a function of time, and other kinematic variables such as velocity, acceleration or jerk can be derived from displacement. More detail regarding how kinematic information can be used to describe skill will be outlined later. The next level of analysis involves kinetic variables, which describe movement in terms of the forces required to generate the motion. Kinetic variables can be calculated from the kinematic information. Of primary concern in this type of analysis are the individual muscle forces or moments of force generated by the muscles across a joint. A related level of analysis

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is termed energetics. One energetic variable, power, is the rate of doing work or the rate of energy change, and is the product of both a kinetic (force) and kinematic (velocity) variable. Power patterns reveal the rate of generation or absorption of force by the muscles (see McFadyen, 1990; 1991; Winter, 1990; 1991). Kinematics There are many stages to data analysis and of course the nature of the analysis chosen depends on how the data were collected, and the questions being asked. In his recent book Winter (1991) does an excellent job of describing the most common kinematic parameters measured for gait. The approach described by Winter could be applied in the analysis of most action patterns. Below, is a description of how the most common data analysis stages are applied to the analysis of reaching and grasping movements, since currently most movement analysis research involves the upper limbs or fine manual skills. Data Smoothing The first stage of data analysis involves removing the noise or unwanted portion of the signals. Polynomial fitting, harmonic analyses, spline curve fitting and filtering will all remove noise, with filtering probably being the most satisfactory (Winter, 1991). Smoothing the data is usually necessary if the curves are going to be differentiated to examine velocity (Winter, Quanbury, Hobson,Sidwall, Reimer, Trenholm, Steinke & Shlosser, 1974) . However, with any type of signal processing, because small distortions may be introduced into the signal, the nature of the distortion is dependent on the characteristics of the raw signal and the specific technique used to process the signal. Caution must be used when interpreting processed data, because a deviation in a curve may not be due to a particular physiological process (e.g. visually based correction in a movement) but instead could be a signal processing artifact caused by something like an underdamped filter. A recent trend in the evaluation of the role of visual feedback in the control of manual aiming has been to evaluate the number and nature of oscillations in acceleration profiles of arm movements. These deviations have then been interpreted as indications of visual feedback processing (van Donkelaar & Franks,l991; Young, Allard & Marteniuk,l988). However, it has not yet been clearly established that the deviations in acceleration profiles are actually corrections. A post-hoc approach has been used to define corrections; that is, if there is a deviation in a profile then feedback must have been used to modify the trajectory. However, an alternative explanation is that a motor program is used to generate an aiming movement with little use of feedback. However, as the movement evolves, noise is introduced into the motor system, resulting in trajectory deviations. Recent evidence has shown however, that in situations where visual feedback is available, there are more trajectory deviations, when compared to no visual feedback situations (Chua, 1992). Thus, although there is mounting evidence that oscillations in acceleration curves are associated with feedback corrections, it has still not been clearly established what causes a deviation in a movement trajectory. The speed at which expert and novice volleyball players can use visual information has

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been examined in a study in which players were required to detect the presence of a ball in a slide of a game situation (Allard & Starkes, 1980). These researchers found that skilled volleyball players were faster than non players at visually detecting balls in slides of volleyball settings. Subjects indicated their perception of the ball with a verbal response. While a verbal indicator of perception was used in this study, in a real game situation, a physical response would be required (most likely a movement directed toward the ball). It is possible that similar, and potentially more sensitive measures of perception could be determined by monitoring how the kinematics of part of the skill (for example the volley action, or the leg action) is affected by perceptual skill level. How the use of visual information changes with training and improved skill could perhaps be investigated by examining the trajectory deviations in skilled and unskilled movement. This approach was used in a recent laboratory study where subjects were required to reach towards and grasp small illuminated dowels in a semi-darkened mom ( Paulignan, MacKenzie, Marteniuk & Jeannerod, 1990). Unexpectedly, on some mals as the subjects initiated their reach towards the dowel, the small light beneath the dowel was extinguished and a light under another dowel, which was located either to side of the original dowel was illuminated. This gave the illusion to the subject that the dowel position has actually jumped to a new position. Paulignan et al. (1991) found that modifications made to the reaching movement in response to this visual perturbation occurred very early in the movement trajectory, even though subjects reported that the dowel seemed to move just before the hand actually reached the target. Applying these findings to the volleyball situation, it is possible that kinematic modifications to reaching or spiking movements in response to visual stimuli (the ball) could be occurring much sooner than would be recorded if subjects only produced a verbal report. This is another example, where information about how a movement is performed may provide unique insights into cognitive abilities or strategies.

Angles One way to describe the motion of the limb is to define joint angles and to measure how each angle changes and varies with the other. An angle can be defined by any three markers, with the middle marker being placed on the axis of joint rotation. One problem with this, however, is that external markers never truly represent the joint center, thus adding error to the calculation of the true angle. A complicated but more accurate alternative is to place several markers on the two limb segments involved in the angle, and use this information to mathematically define them as rigid tubes. Then, an instantaneous joint center can be calculated to provide a more accurate measure of the angle. This approach however, is computationally much more difficult, so a researcher may choose to accept the error associated with the easier method. The amount of error acceptable in calculating a joint angle is of course dependent on the reason it is being measured and the goal of the study . Independent of how the markers are placed on a limb to calculate the angle, the angle can be defined several ways: First, the angle can be described relative to itself, which means that regardless of how the joint angle is oriented in space, the angle described by two joined

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segments remains the same. Terms like flexion or extension describe angles in these terms. Alternatively, joint angles can be described in terms of a world based reference or planes, that is sagittal, frontal or horizontal planes. In this case, the orientation of the joint angle relative to external spatial coordinates is important. Once again, the way one chooses to describe a joint angle depends on the type of inferences one is attempting to make from the data. Figure 3.1 illustrates wrist and elbow angle changes when the subject throws a small ball.

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Figure 3.1. This Figure shows the wrist joint angle (created by markers placed on the knuckles, wrist and elbow) and elbow joint angle (created by markers place on the wrist, the elbow, and the shoulder) of a subject throwing a small ball. The angles are measured relative to themselves. You can see that as the wrist is drawn into flexion, the elbow angle remains unchanged, then both the elbow and wrist joints extend.

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Cross Correlation Once the joint angles of a limb arc defined, they can be compared to each other by a technique called cross correlation. Two angle curves (the curve is the plot of joint angle as a function of time) are correlated to each other. One curve can then be shifted in the time domain, and the remaining overlapping points are then correlated. Using this procedure, the phase shift at which the curves are the most highly related can be determined. This type of analysis can quantify similarity in the shape of two curves, and will reveal at what time lag the similarity in shape is maximal. It is a useful technique to use when you suspect that the shapes of two curves are similar, but they are phase shifted in the time domain. Figure 3.2 illustrates cross correlation.

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Figure 3.2. This Figure shows how two angles (in this case the wrist and elbow angles plotted in Figure 3.1) can be crosscorrelated to examine similarity in trendr. The overlapping points along the two curves are correlated, then one curve is shifted, and the remaining overlapping points are again correlated.

The results of a cross correlational analysis can be interpreted in two different ways. With the first perspective, the assumption is made that if a movement is highly skilled, the movements of the joint angles are highly related. This approach has been used in the comparison of skilled and unskilled dart throwers (Leavitt, Marteniuk & Camahan, 1988). In

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this study expert and novice dart throwers were required to throw darts at a regulation dart board. Movements of the throwing arm were recorded with an optoelecmc system, and angles of the wrist, elbow and shoulder (in the sagittal plane) were crosscorrelated. Higher correlation values between the joints were found for the expert when compared to the novice dart throwers. Thus, the assumption was made that the more skilled an individual is, the more highly correlated the joint angles will be. However, an alternative perspective has also been used to describe skill; expert performers possess the ability to uncouple movements of the joints. In a recent study, Swinnen, Walter, Beirihckx, and Meugens (1991) had subjects perform skills in which the two hands were required to move together, at different tempos and in opposite directions. In this situation, expert performance was manifest by an uncoupling of the two limbs, or in other words a controlled discoordination. Others (Kelso, Putnam & Goodman, 1983; Kelso, Southard & Goodman, 1979; Marteniuk, MacKenzie & Baba, 1984) have shown that when making bimanual movements, the trajectories of the two hands are coupled. However, Swinnen et al. (1991) showed that with practice, movements of the limbs can be uncoupled. Subjects in the Swinnen et al. study were required to generate unsynchronous flexion and extension movements about the left and right elbows. Although this was difficult early in practice and tended to be very "unnatural", with practice subjects were able to achieve the skill. In the Swinnen et al. (1991) study , skill was defined by a dyscoordination or uncoupling of the motion in the left and right elbow joint angles. That is, the elbow joints were eventually able to move at differing tempos. Thus, it appears that depending on the nature of the skill, skilled performance can be defined by either the coupling or uncoupling of the limbs or joint angles. This apparent contradiction can be found in real life examples as well. For example, in a situation where maximum force is required, such as throwing an implement, it makes biomechanical sense to have the angles of the elbow, wrist and shoulder extend in unison to generate maximal force. However, there are different kinds of skills where it is important to have independence of the effectors, such as playing a piano, where the fingers of the hands must play different tempos, and flex and extend independently.

The Grasp (Aperture) Within the past ten years, many researchers have become involved in investigating how reaching and grasping movements are controlled (see Jeannerod. 1988 for a review). The initial step in these investigations was to determine how typical prehension movements unfold. It was proposed that prehension movements are comprised of two phases, the transport and the grasp (Jeannerod, 1984). The grasp phase is usually quantified by measuring the hand aperture, or the distance between the thumb and the forefinger. This measure is used to represent how the hand is opening up and closing around an object. Jeannerod (1984) has shown that the size of the peak aperture is highly correlated with the size of the object subjects are reaching towards to grasp. Thus, it seems to be a relatively effective measure. However, it has been suggested that it would be more fruitful to examine the grasping characteristics of the entire hand. The preshaping and grasping posture of all the fingers should be quantified in order to really understand grasp fomiation (Proteau, 1992). Normal healthy adults appear to be very expert at grasp formation. However, one might not think of simple reaching as a skilled activity since we all seem to be very good at it. But, when an individual has some type of brain injury or

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nervous system disorder, the ability to generate the appropriate aperture size when grasping can be lost. After stroke it has been shown that subtle disruptions in reaching performance can be quantified by monitoring the kinematics of reaching, grasping and pointing movements (Charlton, Roy, Marteniuk & MacKenzie, 1988; Fisk and Goodale,1988; Goodale, Milner, Jakobson & Carey, 1990). Goodale et al. (1990) have argued that kinematic analyses of reaching movements reveal differences between patients and normals that may not be clinically observable, and that this approach could be used to evaluate recovery of function. Thus, a measure as simple as hand aperture can be used to quantiiy the "skill " inherent in reaching and grasping. To illustrate this Figure 3.3 is data on hand aperture for a normal subject reaching to grasp an object.

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Figure 3.3. This shows an aperture profile of a subject reaching to grasp a stationary object located on a table in front of him. These data were collected by an optoelectric system at 200 H z , and were filtered at 7 H z with a dual pass buttenvorth filter.

Transport The transport phase of a reaching movement describes how the limb moves through space to reach a target location. When the kinematics of the transport phase of a reaching movement are evaluated, distinct differences can again be found between skilled and abnormal movement (caused by brain injury). A dependent measure that is often used to describe the

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transport phase of a reaching movement is the velocity of the limb. The velocity profile is usually characterized by a bell shaped curve (Hollerbach & Atkeson, 1987 ) with the skewness of the curve being affected by such factors as the accuracy of the task (MacKenzie, Marteniuk. Dugas, Liske & Eickmeier,l987; Marteniuk, MacKenzie, Jeannerod, Athenes & Dugas, 1987). In individuals with brain damage, the smoothness of the curve can be used to quantify the magnitude of the motor control deficit Deviations in a kinematic profile can be interpreted to suggest patients are relying on visual feedback as opposed to preprogrammed control, or alternatively, deviations can suggest patients have more "noise" in their neural system. Measures like the velocity of the limb or closure of the hand around an object are very sensitive measures, and may be used as tools for assessing cognitive processes in performing skilled movement. Marteniuk et al. (1987) have shown that the symmetry of the velocity profile during reaching is influenced by the context of an object or task. For example, when individuals generate reaching movements towards objects of similar visual impact, a tennis ball and a light bulb, very different reaches are generated. Subjects use the information they already have acquired through experience regarding the fragility of objects when picking up the light bulb and spend a larger proportion of their movement trajectory slowing down so that they can make a very controlled grasp of the bulb. Conversely, when individuals pick up the tennis ball, they spend a smaller proportion of the trajectory slowing down before contact with the object. All of these adjustments are made prior to contact with the object, and are based on subject's prior experience and expectations in dealing with these types of objects. This is just one example of how a kinematic approach can be used to gain insight into cognitive processes, and the types of information individuals use in planning and controlling a reaching movement. Figure 3.4 shows wrist velocity changes in a reaching and grasping task, while Figures 3.5 and 3.6 arc the same task completed by a patient with a neural disorder.

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Figure 3.4. This shows a wrist resultant velocity profile for the same trial shown in Figure 3.3. The Formulae for calculating resultanr is 2 = 2 + )? + 2.

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Statistical Approaches As shown in Figures 3.1 to 3.5, kinematic variables are often represented as curves.

While there is a lot of information inherent in a curve, there are a limited number of statistical approaches that can be used. One approach is to use variables such as coefficient of variation to describe the variability associated with a group of curves, however inferential statistics such as analysis of variance (ANOVA) are not used to make comparisons between groups of curves. An alternative approach is to summarize a curve by picking off landmark points (e.g., peak velocity, peak aperture, time to peak velocity etc.) and entering these data into an ANOVA. MANOVA or similar analysis. This procedure provides a way to statistically deal with the variability between mals or subjects, but by doing so, information about the rest of the curve is lost (Winter, 1987). Peaks should not be picked in isolation; instead they should be analyzed in conjunction with qualitative analysis of the curve shapes. ._1

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Figure 35. The Figure shows several velociry and aperture profiles for a patient wirh an undiagnosed neural disorder, as he reached and grasped a small object on a table top. These curves are for the parienr's leji hand which was severely affected.

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Figure 3.6. These curves show the right hand (which was normal) of the same patient in Figure 3.5. Kinetics A moment of force is the sum of muscular, ligament and friction forces which act on the angular rotation of a joint (Winter, 1991). However, friction and ligament forces are assumed to be negligible, so the net moment is generally considered to reflect the forces due to muscular activity. Thus, moments of force reflect the muscular activity that causes the kinematic patterns we observe. Put another way, they are one step closer to the neural signal. Several variables go into the calculation of moment of force; ground reaction forces (which for gait are derived from a force plate imbedded in the ground and for free upper limb reaching moments are considered to be zero), kinematic information for the linked segments involved in the analysis, and tabled information from an anthropometric model which includes lengths and masses of the

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segments. The most extensive kinetic analyses have been conducted on walking, which at first glance might not be thought of as "skilled' activity. Often when we consider expert performance we tend to imagine sporting or work activities. But, we should not let this preclude the examination of more everyday activities such as grasping and walking, where the most thorough kinetic analyses have been conducted. However, sports biomechanicianshave compiled kinetic descriptions of athletes performing many skills, and perhaps this information should be examined by those individuals interested in the development of expertise (e.g.. de Koning, de Groot & van Ingen Schenau, 1991; Miller et al., 1989; 1990; Schot & Knutzen, 1992). At each progressive level of analysis (temporal, kinematic, or kinetic) the complexity of the analysis and interpretation increases. Although we may see one particular pattern of kinematics, there are a multitude of patterns of muscle force that could create that pattern (Winter, 1984). Winter has shown that kinematic gait patterns can be produced by very different muscle patterns and forces about the leg. It is apparent that in trying to define or describe skill, there are many levels of analysis that can be used. When assessing sports like gymnastics or diving we tend to see a particular predefined kinematic pattern and label that as a skilled performance. However, one must be aware that more than one pattern of muscle activation can be adopted to produce a single kinematic pattern. There has not been enough research examining kinetic patterns during skilled performance or learning to satisfactorily address this issue. However, in a recent study, the kinematic patterns of the leg were evaluated during the learning of a kicking task (Young, 1990). Subjects in this experiment were required to generate time constrained (400 ms) kicking movements, with a 1.67 kg weight strapped to their foot. An optoelecmc imaging system was used to record movement kinematics for the leg. As you would expect, the temporal accuracy of the movements increased over trial blocks. More interesting however, was the finding that kinematic variability did not decrease as a function of practice. Variability We tend to associate skill with consistency of performance, especially if we are thinking in terms of kinematic patterns (see Roy, Brown & Hardie, in press). However, the opposite might be m e . Perhaps motor skill involves the ability to utilize various differing muscle patterns to generate similar kinematic outputs. Variability. or the ability to respond to it in the environment, might be an important atmbute of skill. If this is m e , then the way we think of the cognitive processing associated with skill would need to change. Perhaps a skilled performer does not plan to be consistent. Instead, a skilled performer could ,with experience, develop a repertoire of movement strategies, or the ability to deal quickly with various sources of feedback to amend movements in response to both environmental and internal perturbation. This suggestion is not really very different from Abbs, Gracco and Cole's (1984) updated description of a motor program, "a program is more likely the representation of the dynamic processes whereby the appropriate sensorimotor contingencies are set up to ensure cooperative complementary contribution of the multiple actions to a common, predetermined goal" (pp.214215).

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The notion of motor equivalence addresses the observation that there are many kinematic solutions to a particular movement goal. That is, a single movement goal can be reached in a multitude of ways and from various different starting points. If the movement goal is to reach a particular position in space with the hand (as in picking up a ball), there are many different ways the arm can do this, because of its multiple degrees of freedom (Bernstein, 1967). This is especially helpful if there is an obstacle between an individual's starting position and their reaching target (Cruse, 1986). A temporal or spatial (accuracy) analysis would not be sensitive to this flexibility. For example, movement time or accuracy may not reflect the type of path chosen to reach a goal, or the muscles selected to move the arm. However, if the kinematics or kinetics describing the movement are examined, then the functional variability in achieving a seemingly consistent movement goal becomes apparent. Perhaps skill is the ability to successfully vary kinematic and kinetic patterns in response to physical and cognitive influences. It has been demonstrated that kinematic trajectories can change as a function of the intent of a movement. Maneniuk et al. (1987) showed that subjects spent a larger proportion of their reaching trajectories slowing down before picking up an object when their intent was to place it carefully after picking it up, as opposed to throwing it into a large box. Even though the object and the environment remained the same, the objective of the two tasks differed, and this resulted in altered kinematic patterns. Which Comes First, Kinematics o r Skill? When we describe a motor act as being skilled, generally this judgement is based on the outcome of the movement. That is, the athlete that jumped the furthest, hit the most home runs, or shot the arrow the most accurately is the most skilled performer. Our strategy has then been to use kinematic or kinetic measures to more fully describe the movement characteristics of the skilled performance. However, when enough normative data have been collected, and "kinematic norms" have been established, it may be possible to then predict whether or not a movement will be successful based on a particular kinematic pattern. It may even become possible to successfully alter existing kinematic patterns to resemble ideal movement patterns in order to facilitate the development of skill. This approach is being used in gait research, where movement parameters of pathological gait can be compared to a pool of nonnative data (Winter, 1991). While using kinematics in this manner may be a long way off, the flexibility exhibited by kinematic and kinetic patterns may reflect control strategies utilized by skilled performers and should be considered in theories of cognition and motor skill.

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