Interactions and connectivity is increasingly expanding to shared objects and environments, such as furniture, vehicles, lighting, and entertainment systems. For transparent personalization in such contexts, we see an opportunity for embedded recognition, to complement traditional, explicit authentication. We introduce Zensei, an implicit sensing system that leverages bio-sensing, signal processing and machine learning to classify uninstrumented users by their body's electrical properties. Zensei could allow many objects to recognize users. E.g., phones that unlock when held, cars that automatically adjust mirrors and seats, or power tools that restore user settings. We introduce wide-spectrum bioimpedance hardware that measures both amplitude and phase. It extends previous approaches through multi-electrode sensing and high-speed wireless data collection for embedded devices. We implement the sensing in devices and furniture, where unique electrode configurations generate characteristic profiles based on user's unique electrical properties. Finally, we discuss results from a comprehensive longitudinal 22-day data collection experiment with 46 subjects. Our analysis shows promising classification accuracy and low false acceptance rate.

Sharing digital content with others is now an important part of human social activities. Despite the increasing need to share, most sharing operations are not simple. Many applications are not interoperable with others, require an Internet connection, or require cumbersome configuration and coordination efforts. Our idea is to simplify digital content sharing on mobile devices by providing support for self-organizing heterogeneous networks and pervasive storage. That is, mobile devices can spontaneously connect to each other over a mixture of different available networks (e.g., 3G/4G, WiFi, Bluetooth, etc.) without requiring an explicit user action of network selection or mandatory Internet access. Moreover, indirect communication can be further augmented by pervasive storage. Mobile devices can store shared content on it, which can later be automatically downloaded by other devices in proximity, thus allowing location-based sharing with minimal coordination even when devices are not in the same location at the same time. This paper shows how these technologies can be incorporated into mobile content-sharing middleware to simplify sharing operations among mobile devices without any modification to commercially available devices or applications. In particular, (i) we provide an implementation of our approach as extension modules for existing content-sharing middleware, (ii) we present two example applications built on top of it, and (iii) we demonstrate our approach through experiments in representative situations.

Surface and object recognition is of significant importance in ubiquitous and wearable computing. While various techniques exist to infer context from material properties and appearance, they are typically neither designed for real-time applications nor for optically complex surfaces that may be specular, textureless, and even transparent. These materials are, however, becoming increasingly relevant in HCI for transparent displays, interactive surfaces, and ubiquitous computing. We present SpecTrans, a new sensing technology for surface classification of exotic materials, such as glass, transparent plastic, and metal. The proposed technique extracts optical features by employing laser and multi-directional, multi-spectral LED illumination that leverages the material's optical properties. The sensor hardware is small in size, and the proposed classification method requires significantly lower computational cost than conventional image-based methods, which use texture features or reflectance analysis, thereby providing real-time performance for ubiquitous computing. Our evaluation of the sensing technique for nine different transparent materials, including air, shows a promising recognition rate of 99.0%. We demonstrate a variety of possible applications using SpecTrans' capabilities.

Touché proposes a novel Swept Frequency Capacitive Sensing technique that can not only detect a touch event, but also recognize complex configurations of the human hands and body. Such contextual information significantly enhances touch interaction in a broad range of applications, from conventional touchscreens to unique contexts and materials. For example, in our explorations we add touch and gesture sensitivity to the human body and liquids. We demonstrate the rich capabilities of Touché with five example setups from different application domains and conduct experimental studies that show gesture classification accuracies of 99% are achievable with our technology.

Touché proposes a novel Swept Frequency Capacitive Sensing technique that can not only detect a touch event, but also recognize complex configurations of the human hands and body. Such contextual information significantly enhances touch interaction in a broad range of applications, from conventional touchscreens to unique contexts and materials. For example, in our explorations we add touch and gesture sensitivity to the human body and liquids. We demonstrate the rich capabilities of Touché with five example setups from different application domains and conduct experimental studies that show gesture classification accuracies of 99% are achievable with our technology.

At present, touchscreens can differentiate multiple points of contact, but not who is touching the device. In this work, we consider how the electrical properties of humans and their attire can be used to support user differentiation on touchscreens. We propose a novel sensing approach based on Swept Frequency Capacitive Sensing, which measures the impedance of a user to the environment (i.e., ground) across a range of AC frequencies. Different people have different bone densities and muscle mass, wear different footwear, and so on. This, in turn, yields different impedance profiles, which allows for touch events, including multitouch gestures, to be attributed to a particular user. This has many interesting implications for interactive design. We describe and evaluate our sensing approach, demonstrating that the technique has considerable promise. We also discuss limitations, how these might be overcome, and next steps.