Controlling horse heart rate as a basis for training improvement

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Abstract

Equine training methods, and consequently, performance times have improved little since the last decades. With advances in measuring signals on-line by means of several new technologies and analytical procedures, and processing these signals immediately with strong and compact processors, it may be possible to develop new training methods. In this research, the objective was to explore the possibilities of using modern model-based algorithms to control the heart rate of horses (bpm) on-line by means of the control input running speed (km/h). Forty-five experiments with five horses and four riders were carried out to generate measurements of physiological status during running. The dynamical characteristics of each horse were quantified using linear discrete transfer function models. The dynamic response of heart rate to step changes in running speed were accurately described. In 90% of the cases, a first-order model gave the best fit. For 69% of the models, the r2 was higher than 0.90 and for 34% of the models, the r2 was even higher than 0.95. In a next step, the model-based algorithm was evaluated by controlling cardiac responses of two horses (horses 2 and 4) to a pre-defined trajectory. The model parameters were kept constant. On average, the error between the defined target trajectory in heart rate and the actual controlled heart rate ranged between 0.2 and 1.4 bpm for the whole target heart rate trajectory. During the steady-state part of the trajectory the average error was maximum 1.1 bpm. In the transient from one steady-state heart rate to another level, the error could increase on average up to 5 bpm. In the future, the combination of on-line measured bioresponses with real-time analysis can be used for adjusting the work load of the horse, during training, directly to the immediate needs of horse (welfare) and trainer (performance).

Introduction

Until the 1980s, limited innovations had been introduced in equine training methods (Gabel et al., 1983) and consequently, athletic horse performances has improved only a little in the last century (Fregin and Thomas, 1983). During the last decades however, the evolution in sensors and sensing techniques has created new possibilities and nowadays it is possible to measure variables such as heart rate and speed on-line on animals in general (e.g. Kettlewell et al., 1997) and on horses more specifically (e.g. Rietmann et al., 2004, Hebenbrock et al., 2005), even in field conditions. For the athletic horse, lactate concentrations together with heart rate responses to speed are important indicators for determining the level of training, health status and physical fitness of horses (Art et al., 1994, Couroucé, 1999, Couroucé et al., 2000, Hebenbrock et al., 2005). With the development of these sensors it becomes more and more important to develop integrated systems which can collect, process and utilise the information (Frost et al., 1997, Aerts et al., 2003b). By coupling sensors for on-line measurement with compact wearable processors (e.g. PDA) and on-line modelling/control algorithms, it should be possible to monitor and even control physiological variables in real-time (Aerts et al., 2003a, Aerts et al., 2003b, Jovanov et al., 2003). Applied to the athletic horse, such an advanced control approach could be used to help trainers and riders in training their horses in a more optimal way in terms of welfare and performance.

In this research, the objective was to explore the possibilities of using modern model-based algorithms in combination with on-line measurements to control the heart rate of horses (bpm) on-line by means of the control input running speed (km/h).

Section snippets

Horses

In this research, in total five horses were used. During the period of the experiments, all horses were kept on the pasture. In winter time, each horse was fed additionally with 4 kg roughage and 1.5 kg concentrates per day. Water was available ad libitum. The first horse (horse 1, brown Belgian Warmblood mare, aged 5 years) was ridden three to four times a week by Riders 2 and 3 (cf. Riders) and was used for dressage, jumping and military. The second horse (horse 2) was a brown Selle Français

Modelling heart rate response

Many authors have investigated the relationship between heart rate and speed in horses. In most cases, these relationships were quantified in a statistical or descriptive way (e.g. Manohar, 1994) or based on (non-) linear regression analysis (e.g. Evans and Rose, 1988). Although dynamic responses to changes in effort levels were measured (Muñoz et al., 1999) the transients in heart rate were not quantified as such. For model-based controlling purposes however, the model should be able to

Conclusion

In this work, it was demonstrated that the combination of on-line measurements of biological signals with advanced control algorithms enable control of complex physiological processes such as heart rate. In the future, such an approach could be used for adjusting the work load of the horse during training, better balancing the immediate needs of horse (welfare) and trainer (performance).

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