Perinatal depression (PND) is a common mental health disorder associated with childbirth, which has high societal costs affecting up to 10% of individuals during pregnancy or postpartum. Whilst socially assistive robots (SARs) have recently proven to be useful tools in mental healthcare, and our previous work has investigated different stakeholders’ perspectives on SARs in PND screening through interview studies, gaps remain in understanding how primary users (i.e., prospective patients) perceive and interact with such technologies. In this article, we use a participatory design methodology with semi-structured interviews of women in Sweden with previous experience of PND to explore the roles that SARs could play in addressing PND challenges and identify design factors for SARs in PND screening. We design and evaluate in a user study a robot prototype in two new interaction contexts for SARs with different levels of human oversight. The results show that SARs are welcomed by most participants, who appreciated the potentially faster assessment process and felt more comfortable opening up with a robot versus a human clinician. However, we found that there is no single solution that fits all, as other participants preferred the flexibility of self-reported digital surveys or interaction with a human clinician. Moreover, results show that transparency and human oversight are crucial requirements to consider when implementing robot-delivered PND screening questionnaires and diagnostic interviews. We reflect on ethical considerations, provide design recommendations and urge HRI designers to carefully consider whom SARs benefit, whom they may not, and which safeguarding factors are necessary to prevent potential negative outcomes.

In this paper we investigate the possibility of socially assistive robots (SARs) supporting diagnostic screening for peripartum depression (PPD) within the next five years. Through a HRI/socio-legal collaboration, we explore the gender norms within PPD in Sweden, to inform a gender-sensitive approach to designing SARs in such a setting, as well as governance implications. This is achieved through conducting expert interviews and qualitatively analysing the data. Based on the results, we conclude that a gender-sensitive approach is a necessity in relation to the design and governance of SARs for PPD screening.

Antenatal depression impacts 7-20% of women globally, and can have serious consequences for both the mother and the infant. Preventative interventions are effective, but are cost-efficient only among those at high risk. As such, being able to predict and identify those at risk is invaluable for reducing the burden of care and adverse consequences, as well as improving treatment outcomes. While several approaches have been proposed in the literature for the automatic prediction of depressive states, there is a scarcity of research on automatic prediction of perinatal depression. Moreover, while there exist some works on the automatic prediction of postpartum depression using data collected in clinical settings and applied the model to a smartphone application, to the best of our knowledge, no previous work has investigated the automatic prediction of late antenatal depression using data collected via a smartphone app in the first and second trimesters of pregnancy. This study utilizes data measuring various aspects of self-reported psychological, physiological and behavioral information, collected from 915 women in the first and second trimester of pregnancy using a smartphone app designed for perinatal depression. By applying machine learning algorithms on these data, this paper explores the possibility of automatic early detection of antenatal depression (i.e., during week 36 to week 42 of pregnancy) in everyday life without the administration of healthcare professionals. We compare uni-modal and multi-modal models and identify predictive markers related to antenatal depression. With multi-modal approach the model reaches a BAC of 0.75, and an AUC of 0.82.

Researchers have developed various tools and techniques towards the vision of on-demand fabrication of custom, interactive devices. Recent work has 3D-printed artefacts like speakers, electromagnetic actuators, and hydraulic robots. However, these are non-trivial to instantiate as they require post-fabrication mechanical– or electronic assembly. We introduce AirLogic: a technique to create electronics-free, interactive objects by embedding pneumatic input, logic processing, and output widgets in 3D-printable models. AirLogic devices can perform basic computation on user inputs and create visible, audible, or haptic feedback; yet they do not require electronic circuits, physical assembly, or resetting between uses. Our library of 13 exemplar widgets can embed AirLogic-style computational capabilities in existing 3D models. We evaluate our widgets’ performance—quantifying the loss of airflow (1) in each widget type, (2) based on printing orientation, and (3) from internal object geometry. Finally, we present five applications that illustrate AirLogic’s potential.

Toolkits for shape-changing interfaces (SCIs) enable designers and researchers to easily explore the broad design space of SCIs. However, despite their utility, existing approaches are often limited in the number of shape-change features they can express. This paper introduces MorpheesPlug , a toolkit for creating SCIs that covers seven of the eleven shape-change features identified in the literature. MorpheesPlug is comprised of (1) a set of six standardized widgets that express the shape-change features with user-definable parameters; (2) software for 3D-modeling the widgets to create 3D-printable pneumatic SCIs; and (3) a hardware platform to control the widgets. To evaluate MorpheesPlug we carried out ten open-ended interviews with novice and expert designers who were asked to design a SCI using our software. Participants highlighted the ease of use and expressivity of the MorpheesPlug.