SmoothMoves is an interaction technique for augmented reality (AR) based on smooth pursuits head movements. It works by computing correlations between the movements of on-screen targets and the user's head while tracking those targets. The paper presents three studies. The first suggests that head based input can act as an easier and more affordable surrogate for eye-based input in many smooth pursuits interface designs. A follow-up study grounds the technique in the domain of augmented reality, and captures the error rates and acquisition times on different types of AR devices: head-mounted (2.6%, 1965ms) and hand-held (4.9%, 2089ms). Finally, the paper presents an interactive lighting system prototype that demonstrates the benefits of using smooth pursuits head movements in interaction with AR interfaces. A final qualitative study reports on positive feedback regarding the technique's suitability for this scenario. Together, these results show SmoothMoves is viable, efficient and immediately available for a wide range of wearable devices that feature embedded motion sensing.

As the COVID-19 pandemic has forced many to work remotely from home, collaborating solely through digital technologies, a growing population of remote home workers are faced with profound wellbeing challenges. Passive sensing devices and ambient feedback have great potential to support the wellbeing of the remote workers, but there is a lack of background and understanding of the domestic workplace in terms of physical and affective dimensions and challenges to wellbeing. There are profound research gaps on wellbeing in the domestic workplace, with the current push for remote home and hybrid working making this topic timely. To address these gaps and shape a starting point for an “ambient workspaces” agenda, we conducted an exploratory study to map physical and affective aspects of working from home. The study involved both qualitative and quantitative measures of occupant experience, including sensor wristbands, and a custom web application for self-reporting mood and aspects of the environment. It included 13 participants for a period of 4 weeks, during a period of exclusive home working. Based on quantitative and qualitative analysis, our study addresses wellbeing challenges of the domestic workplace, establishes correlations between mood and physical aspects, and discusses the impact of feedback mechanisms in the domestic workplace on the behavior of remote workers. Insights from these observations are then used to inform a future design agenda for ambient technologies that supports the wellbeing of remote workers; addressing the design opportunities for ambient interventions in domestic workspaces. This work offers three contributions: 1) qualitatively and quantitatively informed understandings of the experiences of home-workers; 2) a future design agenda for “ambient home workspaces”; and 3) we propose three design concepts for ambient feedback and human–AI interactions in the built environment, to illustrate the utility of the design agenda.

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Figure 1: A digital render of one of the exemplar physicalizations (phys1, right), colors depict identified clusters by the participant. Various reconfiguration strategies were observed, including the increase of cohesion within clusters (left) and the increase of separation between clusters (middle).

The rapid growth in the range of data measures from wearable and stationary sensing devices has led to the adoption of multiple devices in health research. Such multi-device setups present challenges in sustaining patient engagement to capture continuous and high-quality datasets. One approach is to present health data to patients throughout the study but often occurs upon study completion. We report on preliminary insights from a feasibility study (IDEA-FAST) where multiple devices were used by 141 patients in their free-living environments. Interviews with a subset of patients and clinicians highlight challenges and opportunities around participation, data use and interpretation, including understanding compliance and data explainability with patients. We propose that summarising metadata from device usage could foster engagement and scale across a range of technologies regardless of the specific measures or post-processing algorithms provided by devices. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing.