Today’s electronics are manufactured to provide stable functionality and fixed physical forms optimized for reliable operation over long periods and repeated use. However, even when applications don’t call for such robustness, the permanency of these electronics comes with environmental consequences. In this paper, we describe an alternative approach that utilizes sustainable transient electronics whose method of destruction is also key to their functionality. We create these electronics through three different methods: 1) by inkjet printing conductive silver traces on poly(vinyl alcohol) (PVA) substrates to create water-soluble sensors; 2) by mixing a conductive beeswax material configured as a meltable sensor; and 3) by fabricating edible electronics with 3D printed chocolate and culinary gold leaf. To enable practical applications of these devices, we implement a fully transient and sustainable chipless RF detection system.

This work presents PITAS, a thin-sheet robotic material composed of a reversible phase transition actuating layer and a heating/sensing layer. The synthetic sheet material enables non-expert makers to create shape-changing devices that can locally or remotely convey physical information such as shape, color, texture and temperature changes. PITAS sheets can be manipulated into various 2D shapes or 3D geometries using subtractive fabrication methods such as laser, vinyl, or manual cutting or an optional additive 3D printing method for creating 3D objects. After describing the design of PITAS, this paper also describes a study conducted with thirteen makers to gauge the accessibility, design space, and limitations encountered when PITAS is used as a soft robotic material while designing physical information communication devices. Lastly, this work reports on the results of a mechanical and electrical evaluation of PITAS and presents application examples to demonstrate its utility.

Improving end-users’ awareness of cybersecurity warnings (e.g., phishing and malware alerts) remains a longstanding problem in usable security. Prior work suggests two key weaknesses with existing warnings: they are primarily communicated via saturated communication channels (e.g., visual, auditory, and vibrotactile); and, they are communicated rationally, not viscerally. We hypothesized that wrist-based affective haptics should address both of these weaknesses in a form-factor that is practically deployable: i.e., as a replaceable wristband compatible with modern smartwatches like the Apple Watch. To that end, we designed and implemented Spidey Sense, a wristband that produces customizable squeezing sensations to alert users to urgent cybersecurity warnings. To evaluate Spidey Sense, we applied a three-phased ‘Gen-Rank-Verify’ study methodology with 48 participants. We found evidence that, relative to vibrotactile alerts, Spidey Sense was considered more appropriate for the task of alerting people to cybersecurity warnings.

Airflow energy harvesting has attracted much attention in the community of energy harvesting, but the efforts to make it accessible for individual users have been limited. In this paper, we address this issue and demonstrate a comprehensive toolkit, Exergy, which can help and support novice users to simulate, design, and manufacture an airflow energy harvester for vehicles.

Many ubiquitous computing technologies have been developed, but we are not yet living in the world Mark Weiser envisioned---seamless integration of electronics into everyday objects. One of the reasons is that the systems being developed have various energy constraints. I propose one approach to solve this problem, a concept called Compute-proximal Energy Harvesting, and applied it to the automobiles. In carrying out this work, I realized various barriers associated with energy harvesting, and I propose a comprehensive energy harvesting tools to overcome them.

This paper presents a child activity recognition approach using a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. Child activities are classified into 11 daily activities which are wiggling, rolling, standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, and stopping. The overall accuracy of activity recognition was 98.43% using only a single- wearable triaxial accelerometer sensor and a barometric pressure sensor with a support vector machine.

In this paper, we present a health care system which can diagnose chronic diseases such as metabolic syndrome. In case of chronic invalids health information has to be check periodically, causing major inconvenience to the patient. In this paper, we propose an automatic diagnostic method for detecting metabolic syndrome, by utilizing a collaborative community computing approach in conjunction with a well-being index.