Autonomous service robots and drones are increasingly deployed in urban spaces, yet evaluating their designs in situ is constrained by permits, logistics, and the cost of repeated user studies. We contribute SHOWTIME, a generative video-fusion framework that synthesizes realistic, controllable evaluation clips by combining structured prompts, encoding navigation behavior, proxemic policies, and task context with real urban scenes. Across two studies, participants rated SHOWTIME’s generative videos on par with physical recordings for realism, behavioral plausibility, ecological appropriateness, and comfort, and could not reliably distinguish real from generated footage; perceptual differences were driven by environmental structure and crowd density rather than modality. A follow-up experiment showed that generative prototyping elicits robust perceptual contrasts across design variants: communicative signaling (lights, functional cues) increases acceptability and comfort, whereas erratic motion reduces them. The pipeline achieves over a 10× reduction in per-clip time compared to physical trials, enabling rapid, human-in-the-loop iteration on behaviors and UI cues. Together, these results demonstrate human-level perceptual fidelity and practical viability, positioning SHOWTIME as a cost-effective, scalable method for refining autonomous systems and their interfaces in real-world urban contexts.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

We present SNAKE, an innovative method that harnesses heat transferred from human touch interactions to estimate product quality. SNAKE offers an accessible and cost-effective solution that seamlessly integrates with existing retail practices; for example, it can be integrated with scales and cashiers already present in shops. Rigorous and systematic experiments demonstrate that SNAKE achieves a high level of accuracy (83%) and outperforms optical sensing and WiFi sensing baselines. We also provide evidence that SNAKE can capture touch interactions of different durations and maintain consistency across diverse user profiles and operating environments. To assess the potential for practical impact, we also carry out an additional user study (N = 100) which suggests that SNAKE has potential to improve consumer purchasing decisions by at least 25% and reduce food waste (or increase promotional opportunities) by 10%–15%. In summary, our contribution offers a novel solution for leveraging smart IoT solutions to support retailing and foster sustainable retail practices.

The inherent social characteristics of humans make them prone to adopting distributed and collaborative applications easily. Although fundamental methods and technologies have been defined and developed over the years to construct these applications, their adoption in practice is uncommon because end-users may be puzzled about how to use them without much hassle. Indeed, commonly, these applications require a certain level of technical expertise and awareness to use them correctly. Fortunately, AI-chatbot interventions are envisioned to assist and support various human tasks. In this paper, we contribute pervasive chatbots as a solution that fosters a more transparent and user-friendly interconnection of devices in distributed and collaborative environments. Through two rigorous user studies, firstly, we quantify the perception of users toward distributed and collaborative applications (N = 56 participants). Secondly, we analyze the benefits of adopting pervasive chatbots when compared with the chatbot reference model designed for assistance and recommendations (N = 24 participants). Our results suggest that pervasive chatbots can significantly enhance the practicability of distributed and collaborative applications, reducing the time and effort needed for collaboration with surrounding devices by 57%. With this information, we then provide design and development implications to integrate pervasive chatbot interventions in distributed and collaborative environments. Moreover, challenges and opportunities are also provided to highlight the remaining issues that need to be addressed to realize the full vision of pervasive chatbots for any multi-device application. Our work paves the way towards the proliferation of sophisticated and highly decentralized computing environments that are easily interconnected.

Hand-grip strength is a widely recognized indicator of muscle strength and overall health of individuals, particularly among older adults. Hand-grip strength measurements are typically obtained using dynamometers or specifically tailored devices, limiting the context in which measurements can be taken to health checks and clinical settings. In this demo, we showcase a new smart ring, namely HIPPO. The smart ring implements an innovative approach that offers a non-intrusive and opportunistic way to extract handgrip strength measurements from individuals. HIPPO re-purposes off-the-shelf light sensors available in existing wearable devices, e.g., smartwatches, and exploits the principle of light reflectivity, such that as an individual interacts with everyday objects, changes in their surfaces can be used to derive the hand-grip measurements.