This paper introduces brainsourcing: utilizing brain responses of a group of human contributors each performing a recognition task to determine classes of stimuli. We investigate to what extent it is possible to infer reliable class labels using data collected utilizing electroencephalography (EEG) from participants given a set of common stimuli. An experiment (N=30) measuring EEG responses to visual features of faces (gender, hair color, age, smile) revealed an improved F1 score of 0.94 for a crowd of twelve participants compared to an F1 score of 0.67 derived from individual participants and a random chance of 0.50. Our results demonstrate the methodological and pragmatic feasibility of brainsourcing in labeling tasks and opens avenues for more general applications using brain-computer interfacing in a crowdsourced setting.
Acoustic activity recognition has emerged as a foundational element for imbuing devices with context-driven capabilities, enabling richer, more assistive, and more accommodating computational experiences. Traditional approaches rely either on custom models trained in situ, or general models pre-trained on preexisting data, with each approach having accuracy and user burden implications. We present Listen Learner, a technique for activity recognition that gradually learns events specific to a deployed environment while minimizing user burden. Specifically, we built an end-to-end system for self-supervised learning of events labelled through one-shot interaction. We describe and quantify system performance 1) on preexisting audio datasets, 2) on real-world datasets we collected, and 3) through user studies which uncovered system behaviors suitable for this new type of interaction. Our results show that our system can accurately and automatically learn acoustic events across environments (e.g., 97% precision, 87% recall), while adhering to users' preferences for non-intrusive interactive behavior.
In this work we introduce peripheral awareness as a neurological state for real-time human-computer integration, where the human is assisted by a computer to interact with the world. Changes to the field of view in peripheral awareness have been linked with quality of human performance. This instinctive narrowing of vision that occurs as a threat is perceived has implications in activities that benefit from the user having a wide field of view, such as cycling to navigate the environment. We present "Ena", a novel EEG-eBike system that draws from the user's neural activity to determine when the user is in a state of peripheral awareness to regulate engine support. A study with 20 participants revealed various themes and tactics suggesting that peripheral awareness as a neurological state is viable to align human-machine integration with internal bodily processes. Ena suggests that our work facilitates a safe and enjoyable human-computer integration experience.
We present a method for enabling arbitrary textiles to sense pressure and deformation: In-situ polymerization supports integration of piezoresistive properties at the material level, preserving a textile's haptic and mechanical characteristics. We demonstrate how to enhance a wide set of fabrics and yarns using only readily available tools. To further support customisation by the designer, we present methods for patterning, as needed to create circuits and sensors, and demonstrate how to combine areas of different conductance in one material. Technical evaluation results demonstrate the performance of sensors created using our method is comparable to off-the-shelf piezoresistive textiles. As application examples, we demonstrate rapid manufacturing of on-body interfaces, tie-dyed motion-capture clothing, and zippers that act as potentiometers.
We present ShArc, a precision, geometric measurement technique for building multi-bend/shape sensors. ShArc sensors are made from flexible strips that can be dynamically formed into complex curves in a plane. They measure local curvature by noting the relative shift between the inner and outer layers of the sensor at many points and model shape as a series of connected arcs. Unlike jointed systems where angular errors sum with each joint measured, ShArc sensors do not accumulate angular error as more measurement points are added. This allows for inexpensive, robust sensors that can accurately model curves with multiple bends. To demonstrate the efficacy of this technique, we developed a capacitive ShArc sensor and evaluated its performance. We conclude with examples of how ShArc sensors can be employed in applications like gesture input devices, user interface controllers, human motion tracking and angular measurement of free-form objects.