Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Auditory Modulation of Multisensory Representations

Motor control and motor learning as well as interpersonal coordination are based on motor perception and emergent perceptuomotor representations. At least in early stages motor learning and interpersonal coordination are emerging heavily on visual information in terms of observing others and transforming the information into internal representations to guide owns behavior. With progressing learning, also other perceptual modalities are added when a new motor pattern is established by repeated physical exercises. In contrast to the vast majority of publications on motor learning and interpersonal coordination referring to a certain perceptual modality here we look at the perceptual system as a unitary system coordinating and unifying the information of all involved perceptual modalities. The relation between perceptual streams of different modalities, the intermodal processing and multisensory integration of information as a basis for motor control and learning will be the main focus of this contribution. Multi-/intermodal processing of perceptual streams results in multimodal representations and opens up new approaches to support motor learning and interpersonal coordination: Creating an additional perceptual stream adequately auditory movement information can be generated suitable to be integrated with information of other modalities and thereby modulating the resulting perceptuomotor representations without the need of attention and higher cognition. Here, the concept of a movement defined real-time acoustics is used to serve the auditory system in terms of an additional movement-auditory stream. Before the computational approach of kinematic real-time sonification is finally described, a special focus is directed to the level of adaptation modules of the internal models. Furthermore, this concept is compared with different approaches of additional acoustic movement information. Moreover, a perspective of this approach is given in a broad spectrum of new applications of supporting motor control and learning in sports and motor rehabilitation as well as a broad spectrum of joint action and interpersonal coordination between humans but also concerning human-robot-interaction. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-01692-0_20.European Commission/H2020- FETPROACT-2014/641321/E