The adoption of fitness tracker devices to monitor training in the equine market is in full expansion. However, the validity of most of these devices has not been assessed. The aim of this study was to examine the validity of heart rate (HR) and heart rate variability (HRV) measurements during high-intensity exercise by an integrated equine fitness tracker with an electrocardiogram (ECG) (Equimetre) in comparison to an ECG device (Televet). Twenty Thoroughbred racehorses were equipped with the two devices and completed a training session at the track. Data from 18 horses was readable to be analysed. Equimetre HR was compared to Televet HR derived from the corrected Televet ECG. HRV parameters were computed in a dedicated software (Kubios) on uncorrected and manually corrected ECG from both devices, and compared to the Televet corrected data. The HR was recorded on the entire training session and HRV parameters were calculated during the exercise and recovery periods. A strong correlation between the Equimetre HR and Televet HR on corrected data was found (Pearson correlation: r=0.992, P<0.001; root mean square error = 4.06 bpm). For HRV, the correlation was good for all parameters when comparing corrected Equimetre to corrected Televet data (Lin’s coefficient = 0.998). When comparing data obtained from uncorrected Equimetre data to the corrected Televet data, the correlation for HR was still good (Lin’s coefficient = 0.995) but the correlation for all HRV parameters was poor, except for the triangular index (Lin’s coefficient = 0.995). However, correlation between the uncorrected Televet HRV data and the corrected Televet data was equally poor (Lin’s coefficient <0.9). In conclusion, the integrated equine fitness tracker Equimetre satisfies validity criteria for HR monitoring in horses during high intensity exercise. When using corrected ECG data, it provides accurate HRV parameters as well.

Racehorses competing in short (i.e., ‘sprinters’), middle- or longer-distance (i.e., ‘stayers’) flat races are assumed to have natural variation in locomotion; sprinters having an innately shorter stride than stayers. No study has objectively tested this theory. Here, racehorses (n = 421) were categorised as sprinters, milers or stayers based on known race distance (n = 3269 races). Stride parameters (peak length and frequency) of those racehorses were collected from prior race-pace training sessions on turf (n = 2689; ‘jumpout’, n = 1013), using a locomotion monitoring device. Pedigree information for all 421 racehorses was extracted to three-generations. In training, sprinters had a shorter stride of higher frequency and covered consecutive furlongs faster than stayers (p < 0.001). Relatively short or longer stride did not predict race success, but stayers had greater race success than sprinters (p < 0.001). Peak stride length and frequency were moderately heritable (h² = 0.15 and 0.20, respectively). In conclusion, differences in stride were apparent between sprinters and stayers (e.g., shorter stride in sprinters) during routine training, even after accounting for their pedigree. Objective data on stride characteristics could supplement other less objectively obtained parameters to benefit trainers in the appropriate selection of races for each individual racehorse.

(2013). Intervertebral disc characterisation by elastography: a preliminary study. Computer Methods in Biomechanics and Biomedical Engineering: Vol. 16, No. sup1, pp. 275-277.

In-training racehorse physiological data can be leveraged to further explore race-day performance prediction. To date, no large retrospective, observational study has analysed whether in-training speed and heart rate recovery can predict racehorse success. Speed (categorised as ‘slow’ to ‘fast’ according to the time taken to cover the last 600 m from a virtual finish line) and heart rate recovery (from gallop to 1 min after exercise) of flat racehorses (n = 485) of varying age, sex and type according to distance (e.g., sprinter, miler and stayer) were obtained using a fitness tracker from a single racing yard in Australia. Race-pace training sessions on turf comprised ‘fast gallop’ (n = 3418 sessions) or ‘jumpout’ (n = 1419). A posteriori racing information (n = 3810 races) for all 485 racehorses was extracted and combined with training data. Race performance was categorised as win/not-win or podium or not, each analysed by logistic regression. Colts (p < 0.001), stayers (p < 0.001) and being relatively fast over the last 600 m of a benchmark test in training (p < 0.008) were all predictive of race performance. Heart rate recovery after exercise (p = 0.21) and speed recorded at 600 m of a 1 km benchmark test in training (p = 0.94) were not predictive. In-training physiological data analytics used along with subjective experience may help trainers identify promising horses and improve decision-making.

This paper focuses on the analysis of dynamic pressure fluctuations in hydro-electric power plants. Pressure fluctuations are usually measured with remote sensors and the dynamic behaviour of the remote tubing line modifies the readings of dynamic pressure and thus introduces a bias. A method to characterize the remote tubing line and to correct the actual pressure is proposed. The corrected measurements are compared with those obtained using intrusive flush-mounted sensors as well as non-intrusive sensors (PVDF wires and strain gauges). Then, the technique to separate the forward and backward components of the obtained pressure pulsations is outlined. It is further shown how the pulsations and the hoop stresses can be reconstructed along the penstock. Finally, measurements carried out in a hydropower plant are presented to demonstrate the proposed techniques applicability for the analysis of dynamic pressure in a penstock. The results show that a pressure profile can be mapped along the penstock length which allows a comparison with the direct pressure sensor measurements.