Chasing Lions: Co-Designing Human-Drone Interaction in Sub-Saharan Africa

Drones are an exciting technology that is quickly being adopted in the global consumer market. Africa has become a center of deployment with the first drone airport established in Rwanda and drones currently being used for applications such as medical deliveries, agriculture, and wildlife monitoring. Despite this increasing presence of drones, there is a lack of research on stakeholders' perspectives from this region. We ran a human-drone interaction user study (N=15) with experts from several sub-Saharan countries using a co-design methodology. Participants described novel applications and identified important design aspects for the integration of drones in this context. Our results highlight the potential of drones to address real world problems, the need for them to be culturally situated, and the importance of considering the social aspects of their interaction with humans. This research highlights the need for diverse perspectives in the human-drone interaction design process.Comment: To be published in the ACM conference on Designing Interactive Systems (DIS '20

The data hungry home

It's said that the pleasure is in the giving, not the receiving. This belief is validated by how humans interact with their family, friends and society as well as their gardens, homes, and pets. Yet for ubiquitous devices, this dynamic is reversed with devices as the donors and owners as the recipients. This paper explores an alternative paradigm where these devices are elevated, becoming members of Data Hungry Homes, allowing us to build relationships with them using the principles that we apply to family, pets or houseplants. These devices are developed to fit into a new concept of the home, can symbiotically interact with us and possess needs and traits that yield unexpected positive or negative outcomes from interacting with them. Such relationships could enrich our lives through our endeavours to “feed” our Data Hungry Homes, possibly leading us to explore new avenues and interactions outside and inside the home

Generative Adversarial Networks and Data Clustering for Likable Drone Design

Novel applications for human-drone interaction demand new design approaches, such as social drones that need to be perceived as likable by users. However, given the complexity of the likability perception process, gathering such design information from the interaction context is intricate. This work leverages deep learning-based techniques to generate novel likable drone images. We collected a drone image database (N=360) applicable for design research and assessed the drone’s likability ratings in a user study (N=379). We employed two clustering methodologies: 1. likability-based, which resulted in non-likable, neutral, and likable drone clusters; and 2. feature-based (VGG, PCA), which resulted in drone clusters characterized by visual similarity; both clustered using the K-means algorithm. A characterization process identified three drone features: colorfulness, animal-like representation, and emotional expressions through facial features, which affect drone likability, going beyond prior research. We used the likable drone cluster (N=122) for generating new images using StyleGAN2-ADA and addressed the dataset size limitation using specific configurations and transfer learning. Our results were mitigated due to the dataset size; thus, we illustrate the feasibility of our approach by generating new images using the original database. Our findings demonstrate the effectiveness of Generative Adversarial Networks (GANs) exploitation for drone design, and to the best of our knowledge, this work is the first to suggest GANs for such application

Generating Alerts from Breathing Pattern Outliers

Analysing human physiological data allows access to the health state and the state of mind of the subject individual. Whenever a person is sick, having a panic attack, happy or scared, physiological signals will be different. In terms of physiological signals, we focus, in this manuscript, on monitoring breathing patterns. The scope can be extended to also address heart rate and other variables. We describe an analysis of breathing rate patterns during activities including resting, walking, running and watching a movie. We model normal breathing behaviours by statistically analysing signals, processed to represent quantities of interest. We consider moving maximum/minimum, the amplitude and the Fourier transform of the respiration signal, working with different window sizes. We then learn a statistical model for the basal behaviour, per individual, and detect outliers. When outliers are detected, a system that incorporates our approach would send a visible signal through a smart garment or through other means. We describe alert generation performance in two datasets—one literature dataset and one collected as a field study for this work. In particular, when learning personal rest distributions for the breathing signals of 14 subjects, we see alerts generated more often when the same individual is running than when they are tested in rest conditions

Designing mobile projectors to support interactivity

Current commercial pico-projector systems are mainly designed as a principal or secondary output for which very few systems have interaction capabilities. Recent research, however, has created pico-projection prototypes with user interfaces tailored to device or application uses. This paper explores different design possibilities for mobile and embedded pico-projectors and identifies how those designs influence the choice of interaction techniques

First Step toward Gestural Recognition in Harsh Environments

We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people’s lives. As these robots become increasingly autonomous, researchers are seeking ways to enable natural communication strategies between robots and first responders, such as using gestural interaction. First response work often takes place in harsh environments, which hold unique challenges for gesture sensing and recognition, including in low-visibility environments, making the gestural interaction non-trivial. As such, an adequate choice of sensors and algorithms needs to be made to support gestural recognition in harsh environments. In this work, we compare the performances of three common types of remote sensors, namely RGB, depth, and thermal cameras, using various algorithms, in simulated harsh environments. Our results show 90 to 96% recognition accuracy (respectively with or without smoke) with the use of protective equipment. This work provides future researchers with clear data points to support them in their choice of sensors and algorithms for gestural interaction with robots in harsh environments

Offsetting Displays on Mobile Projector Phones

Emerging phone handsets include embedded projectors that will provide a widespread new form of display for mobile users. However, it is not clear how the handheld projector will be used alongside the existing phone screen. The current approach of many manufacturers is to place the projector in the top of the phone. This typically prevents users from simultaneously interacting with the phone and looking at the projection. The result is that using the phone’s screen to supplement or interact with the projected information is difficult. This paper illustrates a technique to dynamically offset the throw angle of a mobile phone projector from the handset’s screen to support different tasks. We describe our design and use it to explore three application scenarios that we have implemented. 1