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.

The present scoping review investigated the current state of the art concerning factors affecting physical and mental health and well‐being of workers using collaborative robots (cobots) in manufacturing industries. Each identified factor was classified using the SHELLO (Software‐Hardware‐Environment‐Liveware‐Liveware‐Organization) conceptual model. Strengths and limitations of such an approach were outlined. A total of 53 papers were included in the scoping review and analyzed following PRISMA guidelines. In 35 papers at least one risk factor referred to the SHELLO Liveware‐Hardware interaction, followed by factors concerning Liveware‐Software (16 papers), Liveware‐Liveware (11 papers), Liveware intrinsic factor (10 papers), Liveware‐Organization (8 papers), and Liveware‐Environment (8 papers). This work highlighted that methodological research is still primarily focused on traditional risk assessment and physical safety. However, several research directions concerning the design of cobots as active collaborators were identified, promoting workers' mental health and well‐being, too. The SHELLO model proved to effectively highlight human factors relevant for the design of cobots and can provide a systemic approach to investigate human factors in other complex sociotechnical systems. To the best of our knowledge, this is the first time the model is applied in the field of human–cobot interaction.

This paper introduces the control architecture of a platform aimed at promoting good mental health for workers interacting with collaborative robots (cobots). The platform aim is to render industrial production cells capable of automatically adapting their behavior in order to improve the operator’s quality of experience and level of engagement and to minimize his/her psychological strain. In order to achieve such a goal, an extremely rich and complex framework is required. Starting from the identification of the parameters that could influence the collaboration experience, the envisioned human- driven control structure is presented together with a detailed description of the components required to implement such an automated system. Future works will include proper tuning of control parameters with dedicated experimental sessions, together with the definition of organizational and technical guidelines for the design of a mental-health-friendly cobot-based manufacturing workplace.