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Measuring the activity of oral-bacteria in plaque, the sticky biofilm on enamel, can provide the presently lacking oral feedback to patients. We have developed O-pH, optical pH monitor that measures pH in the range of 4-7.5 and tested in vivo on 25 pediatric subjects (10-18 years) with high caries risk, 18 of these subjects had a dental cleaning within last two months and 7 didn’t have a cleaning in over 2 months. The average pH after a sugar rinse from regions of biofilm associated with caries was 5.8 and 0.5 units lower than the biofilm of sound enamel.

Sugar-rich diets and poor dental hygiene promote the formation of a biofilm (plaque) that strongly adheres to the dental enamel surface and fosters the evolution of aciduric bacteria. The acid contributes to demineralization of the exterior tooth enamel, which accelerates after the pH drops below a critical value (∼5.5) for extended time periods resulting in the need for restorative procedures. Preventative techniques to alert the dentist and caries-susceptible patients regarding vulnerability to dental decay require a clinical measure of plaque activity. Therefore, there is a need to evaluate the acid production capability of plaque deposits in the pits and fissures of occlusal and interproximal regions. A ratiometric fluorescence pH-sensing device has been developed using an FDA-approved dye and LED excitation. Fluorescein spectral profiles were collected using a spectrometer and analyzed with a spectral unmixing algorithm for calibration over the pH range of 4.5 to 7. An in vivo pilot study on human subjects was performed using a sucrose rinse to accelerate bacterial metabolism and to measure the time-dependent drop in pH. The optical system is relatively immune to confounding factors such as photobleaching, dye concentration, and variation in excitation intensity associated with earlier dye-based pH measurement techniques.

For long, we have studied tiny energy harvesters to liberate sensors from batteries. With remarkable progress in embedded deep learning, we are now re-imagining these sensors as intelligent compute nodes. Naturally, we are approaching a crossroad where sensor intelligence is meeting energy autonomy enabling maintenance-free swarm intelligence and unleashing a plethora of applications ranging from precision agriculture to ubiquitous asset tracking to infrastructure monitoring. One of the critical challenges, however, is to adapt intelligence fidelity in response to available energy to maximise the overall system availability. To this end, we present the design and implementation of ePerceptive: a novel framework for best-effort embedded intelligence, i.e., inference fidelity varies in proportion to the instantaneous energy supplied. ePerceptive operates on two core principles. First, it enables training a single deep neural network (DNN) to operate on multiple input resolutions without compromising accuracy or incurring memory overhead. Second, it modifies a DNN architecture by injecting multiple exits to guarantee valid, albeit lower-fidelity inferences in the event of energy interruption. The combination of these techniques offers a smooth adaptation between inference latency and recognition accuracy while matching the computational load to the available power budget. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and off-the-shelf RF and solar energy harvesters. Our evaluation of these batteryless sensors with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%.

Tooth decay is one of the most common chronic infectious diseases worldwide. Bacteria from the oral biofilm create a local acidic environment that demineralizes the enamel in the caries disease process. By optically imaging plaque pH in pits and fissures and contacting surfaces of teeth, then medicinal therapies can be accurately applied to prevent or monitor the reversal of caries. To achieve this goal, the fluorescence emission from an aqueous solution of sodium fluorescein was measured using a multimodal scanning fiber endoscope (mmSFE). The 1.6-millimeter diameter mmSFE scans 424nm laser light and collects wide-field reflectance for navigational purposes in grayscale at 30 Hz. Two fluorescence channels centered at 520 and 549 nm are acquired and ratiometric analysis produces a pseudo-color overlay of pH. In vitro measurements calibrate the pH heat maps in the range 4.7 to 7.2 pH (0.2 standard deviation). In vivo measurements of a single case study provides informative images of interproximal biofilm before and after a sugar rinse. Post processing a time series of images provides a method that calculates the average pH changes of oral biofilm, replicating the Stephan Curve. These spatio-temporal records of oral biofilm pH can provide a new method of assessing the risk of tooth decay, guide the application of preventative therapies, and provide a quantitative monitor of overall oral health. The non-contact in vivo optical imaging of pH may be extended to measurements of wound healing, tumor environment, and other food processing surfaces since it relies on low power laser light and a US FDA approved dye.

IntroductionTuberculosis (TB) transmission is difficult to measure, and its drivers are not well understood. The effectiveness of infection control measures at healthcare clinics and the most appropriate intervention strategies to interrupt transmission are unclear. We propose a novel approach using clinical, environmental and position-tracking data to study the risk of TB transmission at primary care clinics in TB and HIV high burden settings in sub-Saharan Africa.Methods and analysisWe describe a novel and rapid study design to assess risk factors for airborne TB transmission at primary care clinics in high-burden settings. The study protocol combines a range of different measurements. We will collect anonymous data on the number of patients, waiting times and patient movements using video sensors. Also, we will collect acoustic sound recordings to determine the frequency and intensity of coughing. Environmental data will include indoor carbon dioxide levels (CO2 in parts per million) and relative humidity. We will also extract routinely collected clinical data from the clinic records. The number of Mycobacterium tuberculosis particles in the air will be ascertained from dried filter units using highly sensitive digital droplet PCR. We will calculate rebreathed air volume based on people density and CO2 levels and develop a mathematical model to estimate the risk of TB transmission. The mathematical model can then be used to estimate the effect of possible interventions such as separating patient flows or improving ventilation in reducing transmission. The feasibility of our approach was recently demonstrated in a pilot study in a primary care clinic in Cape Town, South Africa.Ethics and disseminationThe study was approved by the University of Cape Town (HREC/REF no. 228/2019), the City of Cape Town (ID-8139) and the Ethics Committee of the Canton Bern (2019-02131), Switzerland. The results will be disseminated in international peer-reviewed journals.

Electrocardiogram (ECG or EKG) was introduced since 1893 by Einthoven, and it has been used for decades in clinical settings for vital sign monitoring as well as cardiac assessment. The ECG signal with its unique characteristic waves of P waves, QRS complexes, and T waves holds important information about the functionalities of the heart. In recent years, advances in electronics and telecommunications have paved the way for out-of-clinic ECG acquisition and monitoring. The rise of advanced data science techniques, such as machine learning, has further opened doors for distanced, home-based, and automated diagnoses. In parallel, micro- and nanotechnology has enabled significant strides in biological investigations using small animal models, such as zebrafish and mouse, uncovering underlying mechanisms of numerous biological processes. In this chapter, we first introduce the basics of electrocardiogram and the methods for acquisition; and then systems used with zebrafish and humans are discussed. Artificial intelligence, specifically machine learning, is brought into the discussion with an emphasis on the use of convolutional neuron networks for classifying ECG patterns of arrhythmic zebrafish mutants. Finally, the chapter recapitulates with the necessity of translating findings from animal research for use with humans as well as a body sensor network with multimodal sensors which may reveal unprecedented connections among physiological parameters.

We have developed a new tool to measure the acid production by plaque oral bacteria. Many species of oral bacteria metabolize sugars in food and produce organic acids that demineralize the dental enamel leading to the formation of cavities. Measuring the acidity level before and after a sugar rinse can indicate the susceptibility of an individual to tooth decay and location of active caries. In a case study on two subjects, a non-contact optics-based pH device was able to track pH before and after a sugar rinse. The fiber optic probe measures acidity level in difficult to access dental locations such as occlusal pits and fissures based on changes in the spectral fluorescence profile of fluorescein (FL) dye. Fiber coupled 420 nm LED excites 200uM aqueous FL solution in the mouth. The fluorescence spectrum in 450-650 nm range is obtained using an adjacent fiber optic cable coupled to a spectrometer. Chemometric analysis of endmember dianion and anion species using least-square fitting is performed to determine the pH of the FL absorbed into the extracellular region of the oral biofilm. Other unwanted noise, like background light and auto-fluorescence in the range of 450-650 nm is removed before calculating biofilm pH. Using this device in a darkened room on two subjects, we were able to measure resting pH (before a sugar rinse) and track time dependent change in pH (after a sugar rinse) in the range of pH 4-7 paving the way for first clinical optical pH measurement in the mouth.

We present CASPER, a charging solution to enable a future of wearable devices that are much more distributed on the body. Instead of having to charge every device we want to adorn our bodies with, may it be distributed health sensors or digital jewelry, we can instead augment everyday objects such as beds, seats, and frequently worn clothing to provide convenient charging base stations that will charge devices on our body serendipitously as we go about our day. Our system works by treating the human body as a conductor and capacitively charging devices worn on the body whenever a well coupled electrical path is created during natural use of everyday objects. In this paper, we performed an extensive parameter characterization for through-body power transfer and based on our empirical findings, we present a design trade-off visualization to aid designers looking to integrate our system. Furthermore, we demonstrate how we utilized this design process in the development of our own smart bandage device and a LED adorned temporary tattoo that charges at hundreds of micro-watts using our system.

Undetected caries can lead to painful cavities and surgical restorations. Lack of proper detection tools makes caries prevention dependent on dentist's expertise and presents obstacles in oral health monitoring. To overcome this problem, we have developed a new approach to predict early stages of enamel demineralization caused by oral bacteria. These bacteria metabolize sugars in our food and produce organic acids that lead to cavities. Measuring the acidity level can help predict early stages of tooth decay. pH paper or pH electrodes can be used to monitor acidity, but neither are able to track pH levels in all dental locations. Our device, DpOW, is a noncontact optics-based pH device that uses changes in the spectral fluorescence of FDA allowed fluorescein dye to measure acidity levels in difficult to access dental locations such as occlusal fissures. A prototype has been tested over a wide pH range (7.12 to 3.89) and shown to track the change in pH with 0.94 correlation coefficient.

For adults, electrocardiogram (ECG) has been used for over a century as the method to diagnose the heart's performance as well as one of the key indicators in vital-sign monitoring. Nevertheless, there has been no such a device to monitor fetal ECG (fECG) in spite of high fetus mortality rate due to heart diseases. In this work, we present a novel patch using non-contact electrodes (NCEs) to acquire the abdominal ECG (aECG) of the mother which comprises both fECG and maternal ECG (mECG) and a reference patch on mother's chest to extract mECG. The device is based on a polyimide film with NCEs and circuits, Bluetooth Low Energy (BLE) communication and a flexible Li-polymer battery. A MATLAB algorithm was developed to precisely extract the fECG and mECG using Linear Mean Square (LMS) adaptive filter. The system was characterized and validated using recorded ECG signals and an online database with zero error between R-peaks of stabilized filter outputs and required signals. Therefore, our aECG and mECG patch hold promise to monitor the wellbeing states of both the fetus and mother in the home setting as well as provide data for early diagnoses of heart diseases even before the baby is born.

Due to the global hypertension epidemic, a convenient, miniaturized, and cuff-less blood pressure (BP) monitoring device is desirable. During the past several years, we have attested various studies to acquire BP indirectly via pulse transit time (PTT) obtained from electrocardiogram (ECG) and photoplethysmogram (PPG). Towards this end, some progress has been achieved but the prospect of designing a device that requires neither a pressure cuff nor cross-body contact of ECG electrodes has been uncharted. In this paper, we present two approaches to determine PTT which open the door to convenient, non-invasive, and cuff-less wearables for continuous BP monitoring at home settings. The first device was designed with contact-electrode ECG and PPG sensors, located on the bicep and the ear; while the second one, which is a wrist-worn device, consisted of a non-contact ECG circuit and a piezoelectric pulse sensor. Results indicated that our novel designs enable next-generation devices, providing essential continuous BP monitoring off-the-clinic for hypertension patients as well as healthy people.

Sugar-rich diets and poor dental hygiene promote the formation of a biofilm (plaque) that strongly adheres to the dental enamel surface and fosters the evolution of aciduric bacteria. The acid contributes to demineralization of the exterior tooth enamel, which accelerates after the pH drops below a critical value (∼5.5) for extended time periods resulting in the need for restorative procedures. Preventative techniques to alert the dentist and caries-susceptible patients regarding vulnerability to dental decay require a clinical measure of plaque activity. Therefore, there is a need to evaluate the acid production capability of plaque deposits in the pits and fissures of occlusal and interproximal regions. A ratiometric fluorescence pH-sensing device has been developed using an FDA-approved dye and LED excitation. Fluorescein spectral profiles were collected using a spectrometer and analyzed with a spectral unmixing algorithm for calibration over the pH range of 4.5 to 7. An in vivo pilot study on human subjects was performed using a sucrose rinse to accelerate bacterial metabolism and to measure the time-dependent drop in pH. The optical system is relatively immune to confounding factors such as photobleaching, dye concentration, and variation in excitation intensity associated with earlier dye-based pH measurement techniques.

Blood pressure (BP) is one of the most important monitoring parameters in clinical medicine. For years, the cuff-based sphygmomanometer and the arterial invasive line have been the gold standards for care professionals to assess BP. During the past few decades, the wide spread of the oscillometry-based BP arm or wrist cuffs have made home-based BP assessment more convenient and accessible. However, the discontinuous nature, the inability to interface with mobile applications, the relative inaccuracy with movement, and the need for calibration have rendered those BP oscillometry devices inadequate for next-generation healthcare infrastructure where integration and continuous data acquisition and communication are required. Recently, the indirect approach to obtain BP values has been intensively investigated, where BP is mathematically derived through the “Time Delay” in propagation of pressure waves in the vascular system. This holds promise for the realization of cuffless and continuous BP monitoring systems, for both patients and healthy populations in both inpatient and outpatient settings. This review highlights recent efforts in developing these next-generation blood pressure monitoring devices and compares various mathematical models. The unmet challenges and further developments that are crucial to develop “Time Delay”-based BP devices are also discussed.