Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management.

Patients with respiratory diseases require frequent and accurate blood oxygen level monitoring. Existing techniques, however, either need a dedicated hardware or fail to predict low saturation levels. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO 2 , using camera and flashlight functions that are readily available on today’s off-the-shelf smartphones. Since the phone’s camera and flashlight were not made for this purpose, utilizing them for oxygen level estimation poses many difficulties. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A near-field-based pressure detection and feedback mechanism are also proposed to mitigate the negative impacts of user’s behavior during the measurement. We also derive a non-linear referencing model with an outlier removal technique that allows PhO 2 to accurately estimate the oxygen level from color intensity ratios produced by the smartphone’s camera. An evaluation on COTS smartphone with six subjects shows that PhO 2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. In addition, our evaluation in hospitals presents high correlation with ground-truth qualified by the 0.83/1.0 Kendall τ coefficient.

Frequent blood pressure (BP) assessment is key to the diagnosis and treatment of many severe diseases, such as heart failure, kidney failure, hypertension, and hemodialysis. Current "gold-standard'' BP measurement techniques require the complete blockage of blood flow, which causes discomfort and disruption to normal activity when the assessment is done repetitively and frequently. Unfortunately, patients with hypertension or hemodialysis often have to get their BP measured every 15 minutes for a duration of 4-5 hours or more. The discomfort of wearing a cumbersome and limited mobility device affects their normal activities. In this work, we propose a device called eBP to measure BP from inside the user's ear aiming to minimize the measurement's impact on users' normal activities while maximizing its comfort level. eBP has 3 key components: (1) a light-based pulse sensor attached on an inflatable pipe that goes inside the ear, (2) a digital air pump with a fine controller, and (3) a BP estimation algorithm. In contrast to existing devices, eBP introduces a novel technique that eliminates the need to block the blood flow inside the ear, which alleviates the user's discomfort. We prototyped eBP custom hardware and software and evaluated the system through a comparative study on 35 subjects. The study shows that eBP obtains the average error of 1.8 mmHg and -3.1 mmHg and a standard deviation error of 7.2 mmHg and 7.9 mmHg for systolic (high-pressure value) and diastolic (low-pressure value), respectively. These errors are around the acceptable margins regulated by the FDA's AAMI protocol, which allows mean errors of up to 5 mmHg and a standard deviation of up to 8 mmHg.

Cognitive state monitoring is crucial for neurological disorders such as epilepsy, narcolepsy, insomnia, and many other human health concerns. The capability to continuously monitor an individual wearing the device and accurately provide early warnings of seizures or narcolepsy sleep attacks would be game-changing for these disorders. Beyond human health, complete hand-free/voice-free human-computer interaction is desirable for privacy-sensitive use cases or people with disabilities. To achieve this goal, we propose Earable, an ear-worn biosensing platform for cognitive state quantification and human-computer interaction. Earable can capture biosignal including brain waves activities, eyes movements, and facial muscle contractions from the back of the ears. Its form factor is convenient to use in everyday life. In this demo, we show two use cases for our Earable platform. First, as an example of cognitive state monitoring, our system plays relaxing music and dims the light when the user is trying to relax or sleep by detecting alpha and beta waves generated by the brain. Second, as an example of human-computer interaction, our system controls a drone with eye movements and facial muscle activity.

This paper explores a new wearable system, called TYTH, that enables a novel form of human computer interaction based on the relative location and interaction between the user's tongue and teeth. TYTH allows its user to interact with a computing system by tapping on their teeth. This form of interaction is analogous to using a finger to type on a keypad except that the tongue substitutes for the finger and the teeth for the keyboard. We study the neurological and anatomical structures of the tongue to design TYTH so that the obtrusiveness and social awkwardness caused by the wearable is minimized while maximizing its accuracy and sensing sensitivity. From behind the user's ears, TYTH senses the brain signals and muscle signals that control tongue movement sent from the brain and captures the miniature skin surface deformation caused by tongue movement. We model the relationship between tongue movement and the signals recorded, from which a tongue localization technique and tongue-teeth tapping detection technique are derived. Through a prototyping implementation and an evaluation with 15 subjects, we show that TYTH can be used as a form of hands-free human computer interaction with 88.61% detection rate and promising adoption rate by users.

We present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband. The key challenges in creating such a system are (1) to sense useful hand gestures at ultra-low power so that the device can be powered by the limited energy harvestable from the surrounding environment and (2) to make the system work reliably without requiring training every time a user puts on the wristband. We present successive capacitance sensing, an ultra-low power sensing technique, to capture small skin deformations due to muscle and tendon movements on the user's wrist, which corresponds to specific groups of wrist muscles representing the gestures being performed. We build a wrist muscles-to-gesture model, based on which we develop a hand gesture classification method using both motion and static features. To eliminate the need for per-usage training, we propose a kernel-based on-wrist localization technique to detect the CapBand's position on the user's wrist. We prototype CapBand with a custom-designed capacitance sensor array on two flexible circuits driven by a custom-built electronic board, a heterogeneous material-made, deformable silicone band, and a custom-built energy harvesting and management module. Evaluations on 20 subjects show 95.0% accuracy of gesture recognition when recognizing 15 different hand gestures and 95.3% accuracy of on-wrist localization.

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary privacy breach. However, the majority of normal users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose DroidNet, an Android permission control framework based on crowdsourcing. At its core, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise ranking algorithm using a transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our simulation and real user experimental results demonstrate that DroidNet provides accurate recommendations and cover the majority of app requests given a small coverage from a small set of initial experts.

Using touchscreens has largely limited user inputs to small form-factor devices. To address this constraint, we explore a novel input mechanism, dubbed PaperKey, that enables users to interact with mobile devices by performing multi-finger typing gestures on a surface where the device is placed. Using acceleration signals on the device, PaperKey infers the user's type events and then leverages a vision based technique for detecting the exact typing locations on a paper keyboard layout. Compared to single audio, image, or vibration sensing, this work accurately localizes keystrokes with faster processing speed. Additionally, this mechanism keeps the mobility of devices by working without external sensors.

Drones are increasingly flying in sensitive airspace where their presence may cause harm, such as near airports, forest fires, large crowded events, secure buildings, and even jails. This problem is likely to expand given the rapid proliferation of drones for commerce, monitoring, recreation, and other applications. A cost-effective detection system is needed to warn of the presence of drones in such cases. In this paper, we explore the feasibility of inexpensive RF-based detection of the presence of drones. We examine whether physical characteristics of the drone, such as body vibration and body shifting, can be detected in the wireless signal transmitted by drones during communication. We consider whether the received drone signals are uniquely differentiated from other mobile wireless phenomena such as cars equipped with Wi- Fi or humans carrying a mobile phone. The sensitivity of detection at distances of hundreds of meters as well as the accuracy of the overall detection system are evaluated using software defined radio (SDR) implementation.

Accurately measuring and monitoring patient's blood oxygen level plays a critical role in today's clinical diagnosis and healthcare practices. Existing techniques however either require a dedicated hardware or produce inaccurate measurements. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO2, using camera and flashlight functions that are readily available on today's off-the-shelf smart phones. Since phone's camera and flashlight are not made for this purpose, utilizing them for oxygen level estimation poses many challenges. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A light-based pressure detection algorithm and feedback mechanism are also proposed to mitigate the negative impacts of user's behavior during the measurement. We also derive a non-linear referencing model that allows PhO2 to estimate the oxygen level from color intensity ratios produced by smartphone's camera.

This paper introduces LIBS, a light-weight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-the-shelf foam earplug. A supervised non-negative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost self-care, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBS's outputs. We constructed a hardware prototype from off-the-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.