Neural Touch for Enhanced Wearable Haptics with Recurrent Neural Network and IoT-Enabled Tactile Experiences

Improving wearable haptic experiences with the help of Recurrent Neural Networks (RNNs) and the Internet of Things (IoT) is the next frontier of this study. Leveraging innovative neural network topologies and seamless communication, the proposed technology, called "Neural Touch," intends to transform tactile interactions in wearable. A more realistic and responsive touch experience is made possible by the RNN’s critical role in modeling and predicting subtle tactile input. Wearable devices may communicate with a central system in real-time due to the IoT, which allows for dynamic modifications and individualized tactile input depending on user preferences and contextual information. Combining RNNs with the IoT can improve the accuracy of haptic feedback and pave the way for more realistic and situational relevant tactile experiences. It explores the Neural Touch system’s architecture, training procedures, and IoT connectivity integration in its design and execution. The method works by conducting user research and experiments that show how well it provides improved and adaptable haptic input in different contexts. These findings highlight how Neural Touch can revolutionize wearable haptics and lead to smarter, more engaging interactions in the ever-changing world of connected devices.