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On moving and being moved
Introduction: the value of seeing and believing
In 1882, the British photographer Eadweard Muybridge caused a sensation with his series of still photographs showing the âhorse in motionâ. To prove that, when galloping, a horse would indeed become airborne, he employed the then highly innovative method of installing a series of fifty cameras along the course of a racetrack. The shutter mechanism of each of the cameras was connected via a trip wire laid across the track. The moment the horse thundered past, each camera took a picture, resulting in a sequence of photographs that depicted in detail the movement sequence of a full gallop stride (Figure 1.1). Muybridge's series of photographs is often quoted today as the foundation of cinematography and it proved a revelation in terms of understanding and appreciating animal locomotion.1
Figure 1.1 The horse in motion by Eadweard Muybridge; the horse is âSallie Gardnerâ owned by Leland Stanford, running at a 1:40 gait over the Palo Alto track on 19 June 1878
Over the twentieth century, interest in biomechanics, locomotion and kinematics, and their link to performance in equestrian sports, continued to deepen. In fact, once the perception of the horse as an animal primarily used for agriculture, transport and warfare started to change towards it being a partner with which to pursue sports and leisure activities, the specifics of equine movement became increasingly more important â especially to the more competitive rider. Spectacular movement often seems to be valued above and beyond any other factors relevant to performance. However, notwithstanding some of the more ethical and welfare-related issues (on which we will touch throughout this book), the emphasis on equine movement is, at least in part, perfectly understandable. There can be little doubt that the biomechanical ability of horses is, together with their temperament and character, paramount in performance. The way that horses move in the disciplines of dressage, driving, reining and the first phase of eventing or how they clear a fence in the jumping-related disciplines will at least in part determine whether they will rise through the ranks of the competitive levels or primarily remain a âleisureâhorse. It is therefore not surprising that considerable research effort has been spent on investigating equine biomechanics, kinematics and locomotion.2
But what about the rider? To many in the equine industry, the way in which the rider moves often comes as a mere afterthought. It is the horse's natural ability that is considered the vital prerequisite for eventual top performances. Nevertheless, as important as equine natural ability may be, it is the correct shaping and training of horses, their gaits, reactions to rider commands and, ultimately, their self-carriage which will determine their future as either leisure or performance horses. But correct and effective equine training is only possible if riders and trainers move their own bodies in such a way as to elicit and maintain precisely the kind of responses they desire from their horses.
Arguably then, the way that humans move when interacting with horses is, in fact, one of the most important and fundamental elements to both horse and rider performance and safety. Yet even though the skill and ability of many of the world's top equestrian performers is undisputed, one of the most difficult elements is the exact and consistent analysis of the relevant principles of biomechanics that enable these riders to move their own bodies with such far-reaching effects. Luckily, the recent increase in the use of technology to measure and monitor different aspects relating to horse and rider allows for a more detailed and critical examination of how the functional system of the rider can be at its most effective on the back of a horse.
Empirical findings in the field of rider and horse-rider motor control are likely to go some way towards identifying functional parameters of rider movement and may subsequently help humans interacting with horses to develop more effective, safe ways of moving while on and around horses. This chapter aims to examine in more detail the broader principles relating to what constitutes human motor control, biomechanics, kinematics and kinetics and how they might be applied to the general context of equestrian sports. Furthermore, existing empirical knowledge on how riders do and ideally should move will be discussed in the light of more practical contexts and examples. Lastly, our current knowledge of rider influence on equine movement will be examined more closely, with a view to highlighting the variables most prevalent in promoting optimal safety and performance components.
Biomechanics, kinematics, kinetics, human motor control: principles of movement analysis
What does studying the various principles of human â and rider â movement actually entail? As is the case in virtually all scientific disciplines, understanding starts with terminology. Movement of all objects, whether animate or inanimate, is subject to the same mechanical laws. However, when applied to humans or animals, these laws need to be applied more carefully, as a living body is composed of a number of articulate segments, all working together in complex unison. Biomechanics is therefore concerned with the principles of mechanics that govern the movement and structure of living organisms. Two complementary methods assist in the study of organisms in motion: kinematics and kinetics. Kinematics is concerned with changes in position of a particular body segment through time and consequently expresses relevant movement parameters, such as time, displacement, velocity and acceleration through linear and angular variables (Barrey 2008). Kinetics, on the other hand, helps to determine the cause of motion through forces applied to a body, the energy that is released or the mass that is distributed and work that is expended. Traditionally, research efforts in the field of equitation science have focused primarily on using kinematic means of investigation to explain and describe movement (e.g. Byström et al. 2009; Lovett et al. 2004; Peham et al. 2004; Schils et al. 1993), in all likelihood because parameters such as displacement or velocity are easier to measure than, for example, energy released or forces applied (Barrey 2008). However, an increase in the use of more sophisticated measuring equipment in recent years has also allowed for the study of kinetic variables as indicators of horse-rider interaction (e.g. Belock et al. 2012; de Cocq et al. 2009a; Peham et al. 2010).
Lastly, the umbrella term âhuman motor controlâ focuses on how people are able to control (i.e. initiate, stabilize or alter) any kind of movement, ranging from speech or handwriting, through to high-performance athletic pursuits. To improve the performance and safety of horse-rider dyads, the field of human motor control holds considerable promise. Even though empirical evidence remains relatively sparse, efforts to date have included determining the impact of rider movement on equine gaits (e.g. Peham et al. 1998, 2001, 2004; Schöllhorn et al. 2006), on jumping parameters (e.g. Lewczuk et al. 2006) or establishing differences in movement patterns between advanced and novice riders (e.g. Lagarde et al. 2005).
The structured analysis of motor control
But, to tackle existing questions relating to rider movement and motor control in a structured and consistent manner, it is important to understand what, precisely, is being studied. The British neuroscientist and psychologist, David Marr, came up with what is commonly known as Marr's tri-level hypothesis (Marr 1982). He initially developed his theory to understand human vision as a complex information processing system at three distinct levels of analysis. However, movement or motor control may be considered an essentially similar information processing system as vision and Marr's theory is therefore thought to apply in equal terms (Rosenbaum 2010).
Marr identified the first level of analysis as the computational level, which includes an often mathematical description of the different functions that a system has to achieve. Applied to the context of horse riding, the computational level of, say, a rider jumping an obstacle, might be expressed through a number of diagrams depicting the exact angles between a rider's torso and upper leg or between lower and upper arm, while adopting the two-point position over the fence. In essence then, this computational level is primarily concerned with the theoretical foundations of an action or movement. The second level of Marr's (1982) analysis is called the algorithmic or procedural level and describes how a specific movement is executed in real time. From an equestrian point of view, this would include how far riders fold their upper body and rotate their shoulders forward to follow the shape of the bascule of their horses over the fence. Lastly, the implementation level determines which biological systems are activated to facilitate the execution of the movement. To equestrians, this would include, for example, the activation of the larger muscles rectus abdominis (the stomach muscles) and the latissimus dorsi (the back muscles), as well as the deeper oblique muscles, to stabilize the upper body; the shoulder muscles, such as the pectoralis major, and the deltoid muscle to allow for the movement of the shoulder joints to accommodate the forward movement of the upper arms; and the thigh muscles (e.g. quadriceps and hamstrings) to enable extension and flexion of the knee during take off and landing.
Essentially then, Marr's three levels of analysis allow for an analysis and understanding of movement from a theoretical, behavioral and applied level. Any future research activities investigating rider biomechanics and motor control should clearly aim to differentiate between these three levels to allow for the appropriate interpretation of results and integration into practical settings.
Learning how to move: principles of motor learning
To what extent does knowing how to study human movement patterns help in achieving actual performance? Is it merely a matter of observing what more experienced riders do and replicating the kind of movements they make? Anyone who has ever attempted to master a new skill, whether in or out of the saddle, knows that the acquisition of new motor skills can be rather complex. In fact, learning new motor skills often requires a combination of processes, ranging from what is referred to as perceptual-motor integration through to various forms of motor learning. Yet before we discuss salient theories of motor learning, a couple of additional definitions relating to motor skills seem in order. Firstly, motor skills may be defined according to the level of precision of movements and are thought to be situated on a continuum ranging from âgrossâ to âfineâmotor skills (Stallings 1973). Gross motor skills involve large muscle movements which are generally not very precise. Fundamental movement sequences, such as walking, running or jumping, are considered examples of gross motor skills. Fine motor skills, on the other hand, are intricate, precise movements involving smaller muscle groups and often include high levels of coordinative effort. Clearly, especially at the more advanced level, horse riding relies heavily on fine motor control. While some gross motor skills are involved in stabilizing the rider, the fundamental elements of communication with the horse occur primarily through the execution of fine motor skills. Secondly, motor skills may also be categorized according to their interaction with the environment, also referred to as the âopen-closedâ skill continuum (Knapp 1967). Closed skills generally take place in stable, predictable environments and motor performance sequences remain largely unchanged and are likely to have a clear beginning and end. Open skills, on the other hand, are very much dependent on and executed to cope with ever-changing environmental demands (Schmidt 1975). By their very nature, horse sports are primarily open skilled, as the horse, regardless of its level of training, remains essentially unpredictable. In many ways, this makes mastering equestrian sports particularly challenging, as the conditions that determine the execution of the skills are ever-changing.
The action-perception link: perceiving is doing!
The current literature on perception in human motor control and motor learning is vast and so this book does not permit a detailed examination but rather only allows for an overview of some of the most important topics.3 To begin with, one of the most prevalent issues is that, to perform certain movements gracefully and effortlessly, humans (and indeed animals) are dependent on sensory feedback;4 for example, from the object(s) with which they are engaging or from the environment that surrounds them (e.g. Paillard 1982). In fact, motor control can be viewed as the link between sensory input and movement output (Wolpert et al. 2001). Most riders are likely to recognize the impact of sensory feedback on their own motor performance; for instance, they will be able to judge the gait they are riding in (e.g. walk as opposed to canter) on the basis of how they feel their horses move underneath them and will decide on what to do next depending on whether they wish to stay in that particular gait or execute an upwards or downwards transition. When training horses, riders are at all times dependent on the sensory feedback they receive from their horses, as the content of such feedback will determine the next aids, i.e. specific movements of their seat, legs and hands.
Closed and open feedback loops
Closed-loop theory, as proposed by Adams (1971), postulates that motor learning occurs through the continuous refinement of what he calls perceptual-motor feedback loops. Initially, when someone is still learning a task, movements are clumsy and stilted. With each repetition, the sensory feedback that is received develops a number of perceptual reference points, which guide successful performances of movement patterns. Adams (1971) refers to these reference points as âperceptual tracesâ and argues that motor learning includes the development of such perceptual traces, which gradually lead to more successful performance outcomes. However, this also means that, during initial motor learning, people are dependent on receiving sensory feedback to confirm initial movements, before performing the next set of movements. In terms of motor performance, this can be a rather tedious process as the fastest reaction time to external stimuli is measured at a relatively slow 200 milliseconds (Schmidt 1988). This explains why learning how to perform, for example, the relatively complex set of aids for canter, can be so disconcerting for many riders. Initially, they will attempt to move their limbs independently of one another, âwaitingâ as it were for perceptual affirmation that they have placed first their outside leg then their inside leg in the correct position, then soften their inner rein to allow the horse to initiate the first canter stride forward. Frequently, however, the horse will have already responded to the increase in leg pressure and will have started to trot faster. Beginner riders are literally thrown off balance, resulting in their bouncing up and down in the saddle and being unable to complete the canter aid. Finally, the horse might start to canter just to restore its own balance.
We know, of course, that, with practice, most riders will eventually master the transition to canter without even having to think about or wait for confirmatory perceptual feedback. From a motor learning perspective, this can be explained by the concept of âmotor programmesâ; that is, âpre-packaged sequences of actionsâ (Anderson 2000: 326). Motor programmes consist of open-loop segments of behavior that do not require sensory feedback after every isolated movement but instead rely on a fixed sequence of motor behaviors (Schmidt 1988). Incidentally, research has shown that once instructions to execute a certain open-loop sequence of motor behaviors have been sent to the muscles, they cannot be stopped. Studies by Salthouse (1985, 1986) have shown that, even after having been instructed to stop, skilled typists keep typing the last few characters of a word. The existence of such open-loop motor programmes help in explaining why experts in any given task are able to execute certain sequences of motor behavior much faster than the relatively slow reaction time of 200 milliseconds, in response to a certain stimulus. In a riding-related context, this means that, as riders progress from novice to experienced, systems of ...
