SBIR Phase I: Wearable Technology to Prevent Decompression Sickness Underwater by Continuously Monitoring Bubble Presence in the Bloodstream and Tissues

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable the development of a unique wearable scuba-diving device that will eliminate the risk of Decompression Sickness (DCS), by monitoring in real time the formation of nitrogen bubbles in the body of the user while in the dive. The risk to develop DCS while conducting underwater activities occurs during the ascension phase, when the changing pressure may yield the formation of nitrogen bubbles in tissues and body. The presence of bubbles triggers a variety of serious injuries with long term consequences and even death. Any professional and licensed recreational scuba training makes trainees aware about the risks associated to DCS. Divers are instructed to follow ascent rates and safety stops according with criteria established from statistical considerations of empirical data. Nevertheless, DCS is suffered by rule-abiding divers during 3% of the immersions, requiring costly and distressing evacuation and treatment with hyperbaric chambers. The wearable device will alert the scuba-diver before the sickness develops, reducing the risk to suffer DCS, making underwater activities simpler and safer. The proposed project plans to demonstrate an innovative ultrasonic resonant concept adapted from the acoustic chamber notion. Acoustic chambers are structures which are belted by piezoelectric arrangements which are set to deform expanding and compressing when subjected to oscillating electric voltages. The frequency of oscillations can be set to match the modes of vibration of the media entering in resonance. Under such conditions the setup is extremely sensitive to minor changes in the elastic properties of the system, such as those induced by the presence of compressible bubbles. This project will execute a plan to produce a wearable design of the piezoelectric array. Artificial intelligence as well as conventional methods will be employed to analyze the electrical disturbances, and relate them to the bubbles sizes in real time. The computing, data acquisition and power requirements will be determined. Ultimately, these results will determine the feasibility to integrate all the required components in an autonomous wearable DCS risk detection device by a scuba diver.