Validity and practical utility of accelerometry for the measurement of in-hand physical activity in horses

Background: Accelerometers are valid, practical and reliable tools for the measurement of habitual physical activity (PA). Quantification of PA in horses is desirable for use in research and clinical settings. The objective of this study was to evaluate a triaxial accelerometer for objective measurement of PA in the horse by assessment of their practical utility and validity. Horses were recruited to establish both the optimal site of accelerometer attachment and questionnaire designed to explore owner acceptance. Validity and cut-off values were obtained by assessing PA at various gaits. Validation study- 20 horses wore the accelerometer while being filmed for 10 min each of rest, walking and trotting and 5 mins of canter work. Practical utility study- five horses wore accelerometers on polls and withers for 18 h; compliance and relative data losses were quantified. Results: Accelerometry output differed significantly between the four PA levels (P <0•001) for both wither and poll placement. For withers placement, ROC analyses found optimal sensitivity and specificity at a cut-off of <47 counts per minute (cpm) for rest (sensitivity 99.5 %, specificity 100 %), 967–2424 cpm for trotting (sensitivity 96.7 %, specificity 100 %) and ≥2425 cpm for cantering (sensitivity 96.0 %, specificity 97.0 %). Attachment at the poll resulted in optimal sensitivity and specificity at a cut-off of <707 counts per minute (cpm) for rest (sensitivity 97.5 %, specificity 99.6 %), 1546–2609 cpm for trotting (sensitivity 90.33 %, specificity 79.25 %) and ≥2610 cpm for cantering (sensitivity 100 %, specificity 100 %) In terms of practical utility, accelerometry was well tolerated and owner acceptance high. Conclusion: Accelerometry data correlated well with varying levels of in-hand equine activity. The use of accelerometers is a valid method for objective measurement of controlled PA in the horse

Validation of a cat activity monitor

Early detection of diseases and injuries in animals is crucial for their health and well-being. Early diagnosis can be assisted by objective registration of different types of physical activities or behaviour patterns. Monitoring specific parameters, such as changes in activity levels or habits, could serve as an indicator of underlying health issues. It can be challenging for pet owners to notice subtle changes in those characteristics at an early stage. It becomes more difficult in the case of parameters of a low frequency of occurrence, such as drinking and littering behaviours. Hydration status is extremely important in cats and changes in drinking and littering patterns could be early symptoms of potential disorders, in particular diabetes mellitus. There is a noticeable increase in owners’ awareness about the physical and mental health of their pets. With a growing demand for higher standards of tools to assess animals’ everyday habits, more technologies are being developed. Activity monitors utilizing accelerometers provide broad and continuous measures of physical activity, that enable remote and non-invasive monitoring of an individual’s actions. The aim of this study was to validate the registrations of an activity monitor. Specifically, the study aimed to assess the effectiveness of the activity monitor in detecting drinking and littering activities, which might suggest underlying health issues. To monitor these activities, this study used an activity monitor equipped with an accelerometer and attached to a collar. The validity and effectiveness of the activity monitor were established by comparing the measurements obtained from the activity collar to video recordings from the motion sensor camera. For forty-eight days, activity data on drinking and littering actions were collected from a single adult cat. Descriptive statistics were performed to summarize the main findings of the dataset to obtain key results. From the total of 5989 recordings registered by the motion sensor camera, 671 recordings containing actions of drinking and littering were selected for further analysis. Accordingly, 53 recordings were extracted from the activity monitor. This study found no correlation between the data obtained from the activity monitor and the video observations from the motion sensor camera. Further research is needed to investigate the reasons behind this lack of agreement and to improve methodologies for monitoring feline activities using activity monitors. Despite underwhelming findings, it should not rule out all potential applications in monitoring feline behaviors, managing health disorders, and promoting overall health remain promising

Dog behaviour classification with movement sensors placed on the harness and the collar

Dog owners' understanding of the daily behaviour of their dogs may be enhanced by movement measurements that can detect repeatable dog behaviour, such as levels of daily activity and rest as well as their changes. The aim of this study was to evaluate the performance of supervised machine learning methods utilising accelerometer and gyroscope data provided by wearable movement sensors in classification of seven typical dog activities in a semi-controlled test situation. Forty-five middle to large sized dogs participated in the study. Two sensor devices were attached to each dog, one on the back of the dog in a harness and one on the neck collar. Altogether 54 features were extracted from the acceleration and gyroscope signals divided in two-second segments. The performance of four classifiers were compared using features derived from both sensor modalities. and from the acceleration data only. The results were promising; the movement sensor at the back yielded up to 91 % accuracy in classifying the dog activities and the sensor placed at the collar yielded 75 % accuracy at best. Including the gyroscope features improved the classification accuracy by 0.7-2.6 %, depending on the classifier and the sensor location. The most distinct activity was sniffing, whereas the static postures (lying on chest, sitting and standing) were the most challenging behaviours to classify, especially from the data of the neck collar sensor. The data used in this article as well as the signal processing scripts are openly available in Mendeley Data, https://doi.org/10.17632/vxhx934tbn.1.Peer reviewe

Prediction of poor health in small ruminants and companion animals with accelerometers and machine learning

Global warming is one of the biggest challenge of our times, and significant efforts are being undertaken by academics, industries and other actors to tackle the problem. In the agricultural field precision farming is part of the solution to environmental sustainability and has been researched increasingly in recent years. Indeed, it has the potential to effectively increase livestock yield and decrease production carbon footprint while maintaining welfare. The thesis begins with a review of developments in automated animal monitoring and then moves on to a case study of a health monitoring system for small-ruminant in South Africa. As a demonstration and validation of the potential use case of the system, the method we propose is then applied to another study which aims to study feline health. Lower and Middle Income countries will be strongly affected by the changing climate and its impacts. We devise our method based on two South African small scale sheep and goat farms where assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazed small ruminants exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning for missing data imputation and classification, we find that this activity data can predict poorer health as well as those individuals that respond to treatment, with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health can be predicted mainly by the night-time activity levels in the sheep. Our study reveals behavioural patterns across two small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming. The validation of the proposed techniques with a different study group will be discussed in the latter part of the thesis. We used the accelerometry data of indoor cats equipped with wearable accelerometers in conjunction with their health status to detect signs of degenerative joint disease, and adapted our machine-learning pipeline to analyse bursts of high activity in the cats. We were able to classify high-activity events with precision up to 70% despite the relatively small dataset adding further evidence to the viability of animal health monitoring with accelerometers

Cognitive bias, personality and arousal in the domestic dog

The domestic dog has lived alongside humankind for many thousands of years, and in that time has undergone extensive selective breeding that has altered both morphology and behaviour. Our close relationship with dogs may to some extent be characterised by inter-specific communication, but this communication may lead to both understandings and misunderstandings. This thesis looks in part at how inter-specific misunderstandings may arise in the dog-human dyad, and how we may be able to both minimise misunderstandings and maximise understandings through our behaviour, the choices we make in training and husbandry practices, and the associations we expose dogs to. Also explored is dog personality, how this can be measured, and what measures of dog personality may mean for the health and welfare of individual dogs as well as patterns in behavioural tendencies. This is explored by way of a personality survey as well as with a cognitive bias task. Cognitive bias in animals has been investigated in recent years as a possible objective measure of positive and negative welfare by measuring the direction (positive or negative) of judgement bias – which refers to whether ambiguous signals are interpreted as predicting a positive or a negative outcome. Interpretation of cognitive bias results was explored and an index of judgement bias developed. The possible applications of a judgement bias index in conjunction with arousal to look at the role of emotional state on operant training procedures is also discussed

Feeding Cannabidiol (CBD)-Containing Treats Did Not Affect Canine Daily Voluntary Activity

Growing public interest in the use of cannabidiol (CBD) for companion animals has amplified the need to elucidate potential impacts. The purpose of this investigation was to determine the influence of CBD on the daily activity of adult dogs. Twenty-four dogs (18.0 ± 3.4 kg, 9 months−4 years old) of various mixed breeds were utilized in a randomized complete block design with treatments targeted at 0 and 2.5 mg (LOW) and at 5.0 mg (HIGH) CBD/kg body weight (BW) per day split between two treats administered after twice-daily exercise (0700–0900 and 1,700–1,900 h). Four hours each day [1,000–1,200 h (a.m.) and 1,330–1,530 h (p.m.)] were designated as times when no people entered the kennels, with 2 h designated as Quiet time and the other 2 h as Music time, when calming music played over speakers. Quiet and Music sessions were randomly allotted to daily a.m. or p.m. times. Activity monitors were fitted to dogs\u27 collars for continuous collection of activity data. Data were collected over a 14-day baseline period to establish the activity patterns and block dogs by activity level (high or low) before randomly assigning dogs within each block to treatments. After 7 days of treatment acclimation, activity data were collected for 14 days. Data were examined for differences using the MIXED procedure in SAS including effects of treatment, day, session (Quiet or Music), time of day (a.m. or p.m.), and accompanying interactions. CBD (LOW and HIGH) did not alter the total daily activity points (P = 0.985) or activity duration (P = 0.882). CBD tended (P = 0.071) to reduce total daily scratching compared with the control. Dogs were more active in p.m. sessions than in a.m. sessions (P \u3c 0.001). During the p.m. session, dogs receiving HIGH tended (P = 0.091) to be less active than the control (CON). During the a.m. and p.m. sessions, CBD reduced scratching compared with CON (P = 0.030). CBD did not affect the activity duration during exercise periods (P = 0.143). These results indicate that, when supplemented with up to 4.5 mg CBD/kg BW/day, CBD does not impact the daily activity of adult dogs, but may exert an antipruritic effect

The use of animal sensor data for predicting sheep metabolisable energy intake using machine learning

The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems. The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake (MEI) of sheep by using temperature, pitch angle, roll angle, distance, speed, and grazing time data obtained directly from wearable sensors on the sheep. A Deep Belief Network (DBN) algorithm was used to predict MEI, which to our knowledge, has not been attempted previously. The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone. The mean square error (MSE) values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets, respectively. We also evaluated the influential sensor data variables, i.e., distance and pitch angle, for predicting the MEI. Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data. We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management, as wearable sensors become widely used by livestock producers

Automatic detection of signalling behaviour from assistance dogs as they forecast the onset of Epileptic seizures in humans

Epilepsy or the occurrence of epileptic seizures, is one of the world's most well-known neurological disorders affecting millions of people. Seizures mostly occur due to non-coordinated electrical discharges in the human brain and may cause damage, including collapse and loss of consciousness. If the onset of a seizure can be forecast then the subject can be placed into a safe environment or position so that self-injury as a result of a collapse can be minimised. However there are no definitive methods to predict seizures in an everyday, uncontrolled environment. Previous studies have shown that pet dogs have the ability to detect the onset of an epileptic seizure by scenting the characteristic volatile organic compounds exuded through the skin by a subject prior a seizure occurring and there are cases where assistance dogs, trained to scent the onset of a seizure, can signal this to their owner/trainer. In this work we identify how we can automatically detect the signalling behaviours of trained assistance dogs and use this to alert their owner. Using data from an accelerometer worn on the collar of a dog we describe how we gathered movement data from 11 trained dogs for a total of 107 days as they exhibited signalling behaviour on command. We present the machine learning techniques used to accurately detect signalling from routine dog behaviour. This work is a step towards automatic alerting of the likely onset of an epileptic seizure from the signalling behaviour of a trained assistance dog