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.

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.