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 dataset is 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.

The tests were conducted in a dog sporting hall in a testing arena of 10m × 18m covered with artificial turf. The test sequence consisted of seven tasks where the owner was instructed to guide the dog accordingly. Three of the tasks were static tasks (i.e. sitting, standing, lying down) and four were dynamic tasks (i.e. trotting, walking, playing, and treatsearching), each task lasted for three minutes. The whole procedure was repeated after a short break while changing the order of the tasks. Dogs performed tasks sequentially, alternating between static and dynamic tasks. Treat search was always performed as the final task of the sequence and it consisted of searching small pieces of dry dog food spread on the ground (area of 4m × 4m) by sniffing.

Dogs wore two ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) activity sensors including 3-axis accelerometer and 3-axis gyroscope sensors (sampling rate 100 Hz). One sensor was placed inside a tight pocket made of neoprene on the back belt of the dog’s harness, referred to as the back. The other sensor was attached tightly with an adhesive tape on the ventral side of the neck collar and is referred to as the collar sensor.

Tasks feature values:

• play , lie down , treat-search , sit , walk , stand and trot

Ethogram of the behaviors (targets):

The actual behavior of the dogs during the assigned tasks were annotated using video recordings with two video cameras positioned on the opposite lateral walls and facing towards the testing arena. The post hoc annotation of the video recordings was done using the Observer XT 10.5 software (Noldus, The Netherlands). Only segments longer than one second were included in the annotation. Dynamic behaviors (i.e.Walking, Trotting, Galloping, Sniffing; Table below) were only encoded if unambiguous, i.e. if there was only one obvious, continuous dynamic behavior without the dog leaning towards the handler or pulling the leash, thus affecting the gait pattern or the body position. Galloping was annotated only during the play task and sniffing during the treat search task (see Table below for the ethogram). Static behaviors consisted of still postures (i.e. Lying on chest, Sitting, Standing) and annotated when limbs did not move and there was no physical contact between the handler and the dog, except if a treat was given.

Behaviour | Description

• Galloping | 3- or 4-beat gait where the dog lifts and puts down both front and rear extremities in a coordinated manner, in 1− 2-3-beat gait (canter) or in 1− 2-3− 4 beat gait (gallop). All four extremities are simultaneously in the air at some point in every stride. Galloping occurred only during Playing task.

• Lying on chest | The dog’s torso is touching the ground and hips are in the same level as shoulders. The dog can change balance point without using limbs.

• Sitting | The dog has four extremities and rump on the ground. The dog can change balance point from central to hip or vice versa.

• Sniffing | The dog has its head below its back line and moves its muzzle close to the ground. The dog walks, stands or performs another slow movement, but its chest and bottom do not touch the ground. Taking food from the ground and eating it can be included (eating was not coded separately).

• Standing |The dog has the four extremities on the ground, without the dog’s torso touching the ground.

• Trotting | 2-beat gait where the dog lifts and puts down extremities in diagonal pairs at a speed faster than walking.

• Walking | 4-beat gait where the dog moves extremities at slow speed, legs are moved one by one in the order: left hind leg, left front leg, right hind leg, and right front leg. The dog moves straight forward or at maximum in 45 degrees angle

Inspiration

• Use Machine Learning algorithms and feature engineering to classify defined behaviors (table 2) or based on features: Behavior_1 to Behavior_3 (The key challenge of this task would be feature engineering )