Recent advances in fabric-based sensors have made it possible to densely instrument textile surfaces on smart toys without changing their look and feel. While such surfaces can be instrumented with traditional sensors, rigid elements change the nature of interaction and diminish the appeal of plush toys.

Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography (PSG) and show that it significantly outperforms two commercially-available sleep tracking wearables—Fitbit and Oura Ring.

Recent years have seen exciting developments in the use of RFID tags as sensors to enable a range of applications including home automation, health and wellness, and augmented reality. However, widespread use of RFIDs as sensors requires significant instrumentation to deploy tethered readers, which limits usability in mobile settings. Our solution is WearID, a low-power wrist-worn backscatter reader that bridges this gap and allows ubiquitous sensing of interaction with tagged objects. Our end-to-end design includes innovations in hardware architecture to reduce power consumption and deal with wrist attenuation and blockage, as well as signal processing architecture to reliably detect grasping, touching, and other hand-based interactions. We show via exhaustive characterization that WearID is roughly 6× more power efficient than state-of-art commercial readers, provides 3D coverage of 30 to 50 cm around the wrist despite body blockage, and can be used to reliably detect hand-based interactions. We also open source the design of WearID with the hope that this can enable a range of new and unexplored applications of wearables.

Smart apparel with embedded sensors have the potential to revolutionize human behavior sensing by leveraging everyday clothing as the sensing substrate. However, existing textile-based sensing techniques rely on tight-fitting garments to obtain sufficient signal to noise, making it uncomfortable to wear and limiting the technology to niche applications like athletic performance monitoring.

Duty-cycling has emerged as the predominant method for optimizing power consumption of low-power radios, particularly for sensors that transmit sporadically in small bursts. But duty-cycling is a poor fit for applications involving high-rate sensor data from wearable sensors such as IMUs, microphones, and imagers that need to stream data to the cloud to execute sophisticated machine learning models.