High performance wearable body odor sensor arrays for disease detection and monitoring

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Project Summary Many diseases, both internal and cutaneous, have distinct odors associated with them, and their identification can provide unique diagnostic clues, guide laboratory evaluation, and facilitate and expedite treatment. Current body odor analysis relies on benchtop instruments, but they are too bulky for use at point-of-care, home or workplace. E-nose technologies provide a simple, light, and low cost alternative for body odor analysis, but they are highly susceptible to environmental changes (e.g., temperature and humidity). Additionally, e-nose suffers from strong cross-talk among the sensing elements when it is exposed to ~100 skin-emitted vapor analytes simultaneously. These drawbacks make e-nose pattern recognition difficult and inaccurate. To overcome this, we propose to develop a wearable micro-gas chromatography (GC) device integrated with graphene based nano-electronic e-nose and vital sign sensors, and use it to analyze body odors related to >20 diseases/conditions. In this wearable device, skin-emitted vapors will be pre-separated by micro-GC and then detected by the graphene e-nose embedded at the end of the GC column to generate time-series patterns. Because vapor analytes will be eluted out one or a few at a time, pattern recognition by e-nose will be much simpler and more accurate. The temperature/moisture issues will also be greatly reduced since the vapor sensors are insensitive to temperature changes. Additionally, the pre-concentrator in the GC is hydrophobic and does not trap water, and the remaining water will be separated out from other vapors through GC. Finally, the vapor concentration inside the GC column is >50X higher than near the skin surface due to the pre-concentration effect. Because of these advantages, the pattern recognition and disease detection capability will be significantly enhanced. Our multidisciplinary team has the needed expertise in biomedical/electrical engineering, data science, and a variety of clinical realms including dermatology, emergency medicine, pulmonology, and pediatrics. We will achieve the following specific aims. Aim 1. Develop and fabricate wearable devices and disposables. We will build 20 autonomous wearable GC devices integrated with graphene e-nose. The wearable device will be small, lightweight (~200 g), battery-powered. We will also fabricate 2,000 customized disposable plastic vapor sampling chambers using injection molding with vital sign sensors incorporated. Aim 2. Develop and implement algorithms to analyze time-series patterns. We will develop the algorithm based on deep learning to analyze time-series patterns and the vital sign data. We will train an autoencoder neural network model and apply it to the features from participants. A regularized classification model will be trained to identify the positive patients. Shapely values will be used to provide explanations for the prediction that the model makes. Aim 3. Analyze >20 diseases/conditions. We will recruit patients from the University of Michigan Health System and then use the wearable devices and algorithms developed in Aims 1 and 2 to analyze >20 diseases/conditions in four different specialties: dermatology, acute care, pulmonary medicine, and pediatrics.