In human-robot collaboration (HRC), operators work side by side with collaborative robots (cobots), a new type of machines able to function safely on tasks shared with humans. Despite the increasing presence of cobots in factories, the quality of experience associated by workers with HRC is an underexplored topic. This review is focused on the mental workload (MWL) reported by operators interacting with cobots, its major sources, and the potential solutions to optimize it during HRC. Out of 165 papers identified, 23 were selected as specifically devoted to the exploration of workers’ MWL in a HRC activity. Cobot motion, predictability, task organization and communication patterns emerged as the major factors contributing to operators’ MWL during HRC. Endowing cobots with the capacity to meet both task demands and human needs through modulation of motion rhythm, flexible physical interaction, and more efficient communication patterns may contribute to mitigating workers’ MWL.

‘BenEssere Mamma’ app is a mobile self-help intervention containing mindfulness meditations and ‘savoring the present moment’ exercises for use during pregnancy. The goal of this study is to investigate the effectiveness of this app in enhancing the psychological well-being of healthy childbearing women. A quasi-experimental controlled study was conducted with 74 pregnant women randomly assigned to experimental group (APP – mobile app and antenatal care) or control group (routine antenatal care). Participants were assessed on their psychological well-being before, after the 4 weeks of use of the app, and after the childbirth, using Ryff’s Psychological Well-Being Scale. Women’s acceptance and user experience with the app were also assessed through an ad hoc questionnaire. Experimental group reported an increase in sense of autonomy after intervention and after childbirth, and greater self-acceptance after the childbirth compared to the control group. Results are promising and future investigations are needed to understand if a more interactive or a longer intervention could lead to more effective results and if other populations could benefit of this opportunity. Furthermore, to take advantage of potentialities of mobile apps for promoting well-being in pregnant women, the integration of these tools within a wide public health project is encouraged.

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.