As Pakistan’s population ages and traditional care structures evolve, there is increasing interest in technological solutions for supporting older adults at home. This study investigates the potential of social assistive robots (SARs) in Pakistani homes, focusing on cultural values, inter-generational living, and limited access to such technologies. In a 3-day mixed-methods home study, 14 older adults interacted with a SAR communicating in Urdu. Through observations, interviews, and questionnaires, we evaluated the robot’s acceptability, engagement, and cultural compatibility. The results highlighted the need for culturally sensitive design, emphasising the role of robots as companions rather than replacements for human care, and the importance of robot’s ability to communicate in Urdu. We discuss how SARs could be designed to reflect the characteristics of Pakistani households, including faith, family values, everyday routines, and environmental factors. Our design considerations can benefit research on deploying SARs to support older adults in Pakistan and similar cultures.

As robots are increasingly becoming part of daily life worldwide, it is important to ensure that they are inclusive and culturally sensitive to accommodate users from different backgrounds. In particular, many countries in the Global South (GS) have yet to explore the integration and benefits of robots, especially for underrepresented language groups such as Urdu. We present an exploratory mixed-methods study that investigates how robots' language affects the social interaction, acceptability and overall perception of robots within Pakistani Urdu-speaking individuals. The findings highlight the importance of language and cultural adaptation, and how these factors influence the acceptance of robots, emphasising the need for more inclusive and religion sensitive technologies designed for the GS users.

Existing work on detecting deception has mainly relied on collecting datasets evolving from contrived user interactions. We argue that naturally occurring deception behaviours can inform more reliable datasets and improve detection rates. Therefore, in this paper, we discuss the findings of two experiments which enabled participants to freely and naturally engage in deceptive and truthful behaviours in a game environment. We collected physiological and oculomotor behaviour (PB, & OB) data including electrodermal activity, blood volume pulse, heart rate, skin temperature, blinking rate, and blinking duration during the deceptive and truthful states. We investigate the changes in both PB and OB across repeated interactions and explore the potential of incremental transfer learning in detecting deception. We found significant differences in electrodermal activity, and skin temperature between deception and non-deception groups in both studies. The incremental transfer learning method with a logistic regression classifier detected deception with 80% accuracy, outperforming previous research. These results highlight the importance of collecting data from multiple sources and promote the use of incremental transfer learning to accurately detect deception in real time.