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Devices that can morph their functions on demand provide a rich yet unexplored paradigm for the next generation of electronic devices and sensors. For example, an antenna that can morph its shape can be used to adapt communication to different wireless standards or improve wireless signal reception. We utilize temperature-sensitive shape memory alloys (SMA) to realize a shape morphing antenna (ShMoA). In the designed architecture, multiple conjoined shape memory alloy sections form the antenna. The shape morphing of this antenna is achieved through temperature control. Different temperature threshold levels are used for programming the shape. Besides its conventional use for RF applications, ShMoA can serve as a multi-level temperature sensor, analogous to thermoreceptors in an insect antenna. ShMoA essentially combines the function of temperature sensing, embedded computing for detection of threshold crossings, and radio frequency readout, all in the single construct of a shape-morphing antenna (ShMoA) without the need for any battery or peripheral electronics. The ShMoA can be employed as bio-inspired wireless temperature sensing antennae on mobile robotic flies, insects, drones and other robots. It can also be deployed as programmable antennas for multi-standard wireless communication. 

Education researchers have proposed that qualitative and emerging computational machine learning (ML) approaches can be productively combined to advance analyses of student-generated artifacts for evidence of engagement in scientific practices. We applied such a combined approach to written arguments excerpted from university students’ biology laboratory reports. These texts are lengthy and contain multiple different features that could be attended to in analysis. We present two outcomes of this combined analysis that illustrate possible affordances of combined workflows: 1) Comparing ML and human-generated scores allowed us to identify and reanalyze mismatches, increasing our overall confidence in the coding; and 2) ML-identified word clusters allowed us to interpret the overlap in meaning between the original coding scheme and the ML predicted scores, providing insight into which features of students’ writing can be used to differentiate rote from more meaningful engagement in scientific argumentation. 

Wearables have garnered significant attention in recent years not only as consumer electronics for entertainment, communications, and commerce but also for real-time continuous health monitoring. This has been spurred by advances in flexible sensors, transistors, energy storage, and harvesting devices to replace the traditional, bulky, and rigid electronic devices. However, engineering smart wearables that can seamlessly integrate with the human body is a daunting task. Some of the key material attributes that are challenging to meet are skin conformability, breathability, and biocompatibility while providing tunability of its mechanical, electrical, and chemical properties. Electrospinning has emerged as a versatile platform that can potentially address these challenges by fabricating nanofibers with tunable properties from a polymer base. In this article, we review advances in wearable electronic devices and systems that are developed using electrospinning. We cover various applications in multiple fields including healthcare, biomedicine, and energy. We review the ability to tune the electrical, physiochemical, and mechanical properties of the nanofibers underlying these applications and illustrate strategies that enable integration of these nanofibers with human skin. 

Abstract Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.