{Reference Type}: Journal Article
{Title}: Combinatorial Bionic Hierarchical Flexible Strain Sensor for Sign Language Recognition with Machine Learning.
{Author}: Zong X;Zhang N;Wang J;Zong H;Zhang C;Xu G;
{Journal}: ACS Appl Mater Interfaces
{Volume}: 16
{Issue}: 29
{Year}: 2024 Jul 24
{Factor}: 10.383
{DOI}: 10.1021/acsami.4c07868
{Abstract}: Flexible strain sensors have been widely researched in fields such as smart wearables, human health monitoring, and biomedical applications. However, achieving a wide sensing range and high sensitivity of flexible strain sensors simultaneously remains a challenge, limiting their further applications. To address these issues, a cross-scale combinatorial bionic hierarchical design featuring microscale morphology combined with a macroscale base to balance the sensing range and sensitivity is presented. Inspired by the combination of serpentine and butterfly wing structures, this study employs three-dimensional printing, prestretching, and mold transfer processes to construct a combinatorial bionic hierarchical flexible strain sensor (CBH-sensor) with serpentine-shaped inverted-V-groove/wrinkling-cracking structures. The CBH-sensor has a high wide sensing range of 150% and high sensitivity with a gauge factor of up to 2416.67. In addition, it demonstrates the application of the CBH-sensor array in sign language gesture recognition, successfully identifying nine different sign language gestures with an impressive accuracy of 100% with the assistance of machine learning. The CBH-sensor exhibits considerable promise for use in enabling unobstructed communication between individuals who use sign language and those who do not. Furthermore, it has wide-ranging possibilities for use in the field of gesture-driven interactions in human-computer interfaces.