The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study.

Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64-76% and 70-86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.

What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture.

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

Gastrointestinal nematode infection affects overall activity in young sheep monitored with tri-axial accelerometers.

Animals suffering from parasitism typically display altered grazing behaviour and a voluntary reduction in feed intake. These changes are potentially important as indicators of disease. Recent advances in sensor technologies provide the opportunity to objectively measure animal activity while on pasture. Tri-axial accelerometers measure body movement in terms of acceleration, which can then be used to estimate physical activity over time. This study investigated if tri-axial measures of overall activity can be used to assess the impact of gastrointestinal nematode (GIN) infection in young sheep. To address this, the overall activity, faecal nematode egg count (FEC) and body weight of two treatment groups of Romney X Suffolk ram lambs were compared. Animals were monitored for four days using tri-axial accelerometer sensors mounted on a ram mating harness after 42-days grazing on contaminated pasture. On Day 0, all lambs were given anthelmintics. Subsequently, a Suppressive Treatment Group (n = 12) was treated with anthelmintics every two weeks. An Untreated Group (n = 12) did not receive further anthelmintics. Overall activity levels were monitored from Day 42 - 46. Activity level was calculated as vectorial dynamic body acceleration (VeDBA). Anthelmintic treatment had a significant effect on FEC but there was no evidence found for a treatment effect on body weight growth over the 42-day period. An effect of treatment and lamb starting weight on overall activity was found (beta = -0.74, 95 % CI -1.17 to -0.30, p =  0.002), identifying a negative impact of parasitism on activity in heavier animals. These results highlight the usefulness of this approach in assessing the effect of GIN parasitism on sheep monitored remotely. If a threshold value of activity could be determined, it could provide a useful tool for farmers and managers that serves as an early indicator of parasitism in sheep.