Gregory D. Abowd

Youngwook Do, Yuxi Wu, Gregory D. Abowd et al.

Sensor-based interactive systems -- e.g., "smart" speakers, webcams, and RFID tags -- allow us to embed computational functionality into physical environments. They also expose users to real and perceived privacy risks: users know that device manufacturers, app developers, and malicious third parties want to collect and monetize their personal data, which fuels their mistrust of these systems even in the presence of privacy and security controls. We propose a new design paradigm, physically-intuitive privacy and security (PIPS), which aims to improve user trust by designing privacy and security controls that provide users with simple, physics-based conceptual models of their operation. PIPS consists of three principles: (1) direct physical manipulation of sensor state; (2) perceptible assurance of sensor state; and, (3) intent-aligned sensor (de)activation. We illustrate these principles through three case studies -- Smart Webcam Cover, Powering for Privacy, and On-demand RFID -- each of which has been shown to improve trust relative to existing sensor-based systems.

Zhihan Zhang, Alexander Metzger, Yuxuan Mei et al. · uw

Interest in sustainability information has surged in recent years. However, the data required for a life cycle assessment (LCA) that maps the materials and processes from product manufacturing to disposal into environmental impacts (EI) are often unavailable. Here we reimagine conventional LCA by introducing multimodal AI agents that emulate interactions between LCA experts and stakeholders like product managers and engineers to calculate the cradle-to-gate (production) carbon emissions of electronic devices. The AI agents iteratively generate a detailed life-cycle inventory leveraging a custom data abstraction and software tools that extract information from online text and images from repair communities and government certifications. This approach reduces weeks or months of expert time to under one minute and closes data availability gaps while yielding carbon footprint estimates within 19% of expert LCAs with zero proprietary data. Additionally, we develop a method to directly estimate EI by comparing an input to a cluster of products with similar descriptions and known carbon footprints. This runs in 3 ms on a laptop with a MAPE of 12.28% on electronic products. Further, we develop a data-driven method to generate emission factors. We use the properties of an unknown material to represent it as a weighted sum of emission factors for similar materials. Compared to human experts picking the closest LCA database entry, this improves MAPE by 120.26%. We analyze the data and compute scaling of this approach and discuss its implications for future LCA workflows.

Nivedita Arora, Ali Mirzazadeh, Injoo Moon et al.

Augmenting everyday surfaces with interaction sensing capability that is maintenance-free, low-cost (about $1), and in an appropriate form factor is a challenge with current technologies. MARS (Multi-channel Ambiently-powered Realtime Sensing) enables battery-free sensing and wireless communication of touch, swipe, and speech interactions by combining a nanowatt programmable oscillator with frequency-shifted analog backscatter communication. A zero-threshold voltage field-effect transistor (FET) is used to create an oscillator with a low startup voltage (about 500 mV) and current (< 2uA), whose frequency can be affected through changes in inductance or capacitance from the user interactions. Multiple MARS systems can operate in the same environment by tuning each oscillator circuit to a different frequency range. The nanowatt power budget allows the system to be powered directly through ambient energy sources like photodiodes or thermoelectric generators. We differentiate MARS from previous systems based on power requirements, cost, and part count and explore different interaction and activity sensing scenarios suitable for indoor environments.

Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram et al.

The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone. To address this problem, we introduce IMUTube, an automated processing pipeline that integrates existing computer vision and signal processing techniques to convert videos of human activity into virtual streams of IMU data. These virtual IMU streams represent accelerometry at a wide variety of locations on the human body. We show how the virtually-generated IMU data improves the performance of a variety of models on known HAR datasets. Our initial results are very promising, but the greater promise of this work lies in a collective approach by the computer vision, signal processing, and activity recognition communities to extend this work in ways that we outline. This should lead to on-body, sensor-based HAR becoming yet another success story in large-dataset breakthroughs in recognition.

Weiren Wang, Miseon Park, Yuanzhe Fan et al.

Wearable devices such as Google Glass can provide an efficient way to get around users information. We present Face Card, a system builds on Google Glass to provide information-sharing service with around people. With a look at Google Glass, users can quickly get information of nearby and coming users. Utilizing Bluetooth Low Energy (BLE) and proper user interface, Face Card demonstrates the potential of being an efficient information sharing system framework.