Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people’s detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.

The home is a rich context for design research to study things and the Everyday. However, home is also a place of utmost privacy for most people. To better understand this context through an observational artifact without impacting privacy, we designed the Peekaboo cam that enables inhabitants to control their data release actively or passively. The Peekaboo cam is an observational research camera with a coverable lens. We validate in a field study in two homes for 14 days. The resulting photo streams provide qualitative insights on Everyday things in transition. We suggest four design guidelines for observational artifacts for home ecologies.

Digital devices and intelligent systems are becoming popular and ubiquitous all around us. However, they seldom provide sufficient feed-forwards and feedbacks to reassure users as to their current status and indicate what actions they are about to perform. In this study, we selected and analyzed nine concept videos on future IoT products/systems. Through systematic analysis of the interactions and communications of users with the machines and systems demonstrated in the films, we extracted 38 design vocabulary items and clustered them into 12 groups: Active, Request, Trigger functions, Approve, Reject, Notify, Recommend, Guide, Show problems, Express emotions, Exchange info, and Socialize. This framework can not only inspire designers to create self-explanatory intelligence, but also support developers to provide a language structure at different levels of the periphery of human attention. Through the enhancement of situated awareness, human IoT system interaction can become more seamless and graceful.

In the city, mobility is calling for new forms of smartness. To understand what is needed to design thoughtful forms of smart mobility, this paper combines thing ethnography and animistic research approaches to reveal the interwoven networks of personal and social relationships that develop around scooters in Taiwanese everyday life. To this end, a three-day study with six different types of scooterists was conducted in Taipei. Cameras and sensors were directly attached to the scooters themselves, to collect data from a 'thing' perspective. The data collected were then organized and offered to professional actors, who were invited to 'speak' on behalf of the scooters. Through the performance of the actors interpreting and empathizing with the scooter's everyday life, intents, expectations and relationships between scooters and scooterists were revealed and captured. We further discussed how the socio-material networks among scooters could provoke various creative and meaningful arrangements in everyday life.

The paper makes three observations: 1) that preference is a partial ordering in one-dimension; 2) that a product, or a service, may be high-dimensional; consequently, and 3) transitivity, or its lack, results from choice of the direction for projection from high to low dimensions. The mass customization of cellular telephone designs gives the paper a context. The dimensions of the product space are: function, size, styling, color, etc. The medium for presentation (i.e., screen or paper), however, is necessarily two-dimensional (2-D). A criterion for projection is to preserve the information (such as, the similarity between the products, manifested as clusters, and the market opportunities, manifested as voids) from the high dimension. Drawing upon an analogy from the deformation mechanics of materials, the equilibrium conditions are formulated as distance and curvature. Distances give the clustering information; curvature gives the shape information. Projection is then seen as the process that minimizes the deformation in the projected geometry. The findings then become apparent. There are infinitely many ways to project. In most projections, intransitivity of preference results.

We consider the computation of an optimal workpiece orientation allowing the maximal number of surfaces to be machined in a single setup on a three-, four-, or five-axis numerically controlled machine. Assuming the use of a ball-end cutter, we establish the conditions under which a surface is machinable by the cutter aligned in a certain direction, without the cutter's being obstructed by portions of the same surface. The set of such directions is represented on the sphere as a convex region, called the visibility map of the surface. By using the Gaussian maps and the visibility maps of the surfaces on a component, we can formulate the optimal workpiece orientation problems as geometric problems on the sphere. These and related geometric problems include finding a densest hemisphere that contains the largest subset of a given set of spherical polygons, determining a great circle that separates a given set of spherical polygons, computing a great circle that bisects a given set of spherical polygons, and finding a great circle that intersects the largest or the smallest subset of a set of spherical polygons. We show how all possible ways of intersecting a set of n spherical polygons with v total number of vertices by a great circle can be computed in O ( vn log n ) time and represented as a spherical partition. By making use of this representation, we present efficient algorithms for solving the five geometric problems on the sphere.