Soft Robotic Face Mask for Respiratory Support – UROP Symposium

Soft Robotic Face Mask for Respiratory Support

Justin Mason

Research Mentor(s): Xiaonan Huang
Program: Engin
Authors: Justin Mason, Xiaonan Huang


Current standard-of-care face masks require constant force application and healthcare provider attention to ensure proper positioning over the nose and mouth to minimize large oxygen leaks. This reliance on dedicated personnel poses challenges, particularly in emergencies where healthcare resources may be limited. Our research project introduces the development of a self-sealing soft robotic face mask designed to provide respiratory support to patients experiencing breathing difficulties. This project aims to address the aforementioned challenges by proposing a novel face mask that eliminates the need for constant attention from multiple healthcare providers. The proposed soft robotic face mask utilizes particle jamming and a dual-channel reservoir system to create a secure seal against the patient’s face without requiring continuous adjustments or repositioning. The particle jamming component increases the adaptation of the mask to the patient’s face topology. Concurrently, the reservoir holds an internal air chamber, introducing negative pressure to the system and providing the holding force necessary to create the seal on the patient’s face.

Bag-valve-mask (BVM) ventilation is a standard respiratory support method commonly performed with two healthcare providers, one holding the mask and the other holding the connected bag, which is used to pump oxygen into the patient’s lungs. Our innovative self-sealing mask enables healthcare providers conducting BVM ventilation, for example, to safely and securely seal the mask to the patient’s face, thus allowing the task to be completed efficiently with only one healthcare provider instead of two.

We prioritized the optimization of the face mask’s material properties, incorporating flexibility, comfort, and adaptability to various face shapes. Furthermore, rigorous testing protocols will evaluate the mask’s performance, primarily leakage prevention and fit. The anticipated outcomes of this research project include a functional prototype of the proposed face mask, demonstrating its ability to seal and maintain a secure fit on the patient’s face. The implications of this research are significant, as our self-sealing soft robotic face mask has the potential to reform respiratory support for patients with breathing difficulties by reducing the dependency on dedicated personnel for mask positioning, as well as enabling healthcare providers the ability to spend their time more efficiently by freeing their hands during respiratory support.

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