Abstract
Throat sensing has received increasing demands in recent years, especially for oropharyngeal treatment applications. The conventional videofluoroscopy (VFS) approach is limited by either exposing the patient to radiation or incurring expensive costs on sophisticated equipment as well as well-trained speech-language pathologists. Here, we propose a smart and non-invasive throat sensor that can be fabricated using an ionic polymer–metal composite (IPMC) material. Through the cation’s movement inside the IPMC material, the sensor can detect muscle movement at the throat using a self-generated signal. We have further improved the output responses of the sensor by coating it with a corrosive-resistant gold material. A support vector machine algorithm is used to train the sensor in recognizing the pattern of the throat movements, with a high accuracy of 95%. Our proposed throat sensor has revealed its potential to be used as a promising solution for smart healthcare devices, which can benefit many practical applications such as human–machine interactions, sports training, and rehabilitation.
Highlights
Academic Editors: Lidiia G.Kolzunova and Elena V
We designed our ionic polymer–metal composite (IPMC) sensor with dimensions of 1 cm × 4 cm, and a thickness of 0.15 mm
The cross-sectional view of the IPMC sensor shown in Figure 1b(ii) demonstrates that the gold nanoparticles successfully covered the entire surface of the IPMC sensor, including the pores
Summary
The pressure applied onto the IPMC material induces the mobile cations and water molecules to move from a high-stress region to a lower stress region This ionic movement causes an imbalanced charge distribution across the membrane and forms a dielectric potential layer. An AI-assisted, self-powered, and flexible throat sensor is proposed which has been designed and fabricated using the IPMC material for monitoring laryngeal movement. IPMC sensors can be applied for sitting posture monitoring [31], where good or bad sitting postures are identified based on the threshold limit This straightforward recognition method may fail in a dynamic environment, where multiple muscle movements might crosstalk with the measured signal.
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