*2.2. Pre-Surface Condition Data to Test Whether There Is a Correlation with Injuries*

It is argued above that the sand moisture content and density affect the dynamics behavior of sports sand surfaces. It is also seen in a mathematical model of greyhounds that subtle changes in these two values significantly change the forces exposed to animals limb (mainly the hind-leg) [25]. Accordingly, measures should be in-place to correlate the sports sand surfacing moisture content and firmness with the probability of the injuries, which is also advised for other sport arenas, such as horse racing [40].

In Australian greyhound racing industry the sand moisture content and firmness are measured using a portable moisture meter (The instrument to measure track moisture content is typically the TDR 350 Soil Moisture Meter which measures the Volumetric Water Content of the sand as a percentage of retained moisture.) and penetrometer device (The instrument to measure track firmness is usually the FieldScout TruFirm turf firmness meter. The unit on measurement is depth of travel in either inches or mm.). Twenty-four readings are taken at 8 equidistant positions around the track and 3 locations across the track. The three locations across the track are 0.5 m from the inner rail, middle of the track and 0.5 m from the outside rail. These data mUst be collected pre-race, based on the compulsory minimum standards. The moisture content range should fall within the 26.0–33.6%. The sand firmness value should fall within the sand firmness range 15–40 mm. Both values are subjective to the type of the sand used in the sport arena.

In this section, surface condition data for a de-identified greyhound racing track are analysed for a duration of one year, July 2019 to July 2020, when an increase in the rate of catastrophic incidents

was observed. The hypothesis was that the moisture and firmness range would not fall within the recommended range.

Moreover, any inconsistency on the track surface is dangerous and can cause an injury [8,26,28] as the greyhound is not capable of adjusting its gait based on changing surface conditions [27]. Apart from assessing whether the moisture and firmness data fall within the recommended range, the fluctuation between inside and middle track readings should be calculated at different vicinity of the track. It is hypothesised that high fluctuation in theses values suggests irregular surface properties, which might contribute to injuries.

The injury heat-map (the approximate locations on each track for each race distance where clusters of injuries occurred) are generated, based on the injury data provided from the industry, race video and the Stewart reports, and given in the later section. This would assist in finding a correlation between the surface condition data and locations of the track with high rate of injuries.

## *2.3. Use of Accelerometry to Study the Limb-Surface Interactions of Sprinters*

Accelerometry or in other words use of accelerometer to record the locomotion dynamics of athletes are gaining attention as they are cheap, user friendly and provides fundamental information about the gaits. They are usually attached to subject joints and fused with each other for post-processing. In this section, the most recent accelerometry study on racing greyhounds is reviewed [4].

To study the effect of surface compliance on the galloping dynamics of racing greyhounds, an IMU, equipped with tri-axis accelerometer (sampling rate of 185 Hz , was used on two tracks), was used to measure the associated galloping accelerations in racing greyhounds. It was hypothesised the greyhound galloping dynamics are different on different surface types (sand surface vs grass surface).

The Anterior-Posterior (fore-aft) and Dorsal-ventral signals (vertical) acceleration signals, recorded via the IMU, were analysed, to see whether the surface type affect the locomotion dynamics of greyhounds. To do so, signals of galloping on straight sections of the sport arena, are compared with each other.

The recorded Dorsal-Ventral acceleration due to hind-leg strikes was more than triple that of the fore-leg strikes (15 G vs. 5 G). These results were in consistent with the role of hind-legs in powering the locomotion as well as their higher rates of injuries than fore-legs.

The mechanical properties of the sand and grass surface, mainly the impact deceleration (G*max*) measured via a Clegg hammer of the sand surface were three times higher than that of the grass track [4]. Accordingly, it was expected to see higher acceleration on running on the sand surfacing than the grass one. However, the IMU data (the average of peaks of dorsal-ventral and anterior-posterior acceleration) for sand versus grass surfaces were not significantly different.

There might be different reason associated to the observed results that is, not significant difference in IMU signals despite the significant difference on surface type. Firstly, the IMU in this study is mounted on animal's neck (Figure 5a) and the signals would be damped while traveling through the body of the animal. Ideally, the IMU should be attached on animal's foot to sense the real impact load. Secondly, the applied signal processing method in this work are those usually used for linear time-series signals. It is hypothesised that applying nonlinear-time-series-analysis would identify different features in galloping over sand and grass. These methods are currently under the investigation by the same author of this work.
