Co‐Design of Stretchable Fabric Sensors and Tiny Machine Learning for Human Interface Device‐Based Edge‐Intelligent Wearable Gloves

Stretchable fabric sensors are a promising approach for smart wearable devices owing to their simple fabrication and low cost. However, current practical applications are limited by a lack of seamless integration among the sensor, the embedded platform, and the intelligent processing algorithm. To address this issue, this study proposes a co‐design approach that integrates a stretchable fabric sensor, a resource‐constrained embedded platform, and a lightweight machine‐learning (tinyML) model. The sensor is fabricated from a graphite‐based conductive ink‐embedded stretch fabric, demonstrating stable performance with a sensitivity coefficient GF ≈ 218 and mechanical durability of up to 3000 working cycles, while maintaining a simple, low‐cost fabrication process. Signals from multiple sensor channels are processed directly on the embedded device using a tinyML model with a compact memory footprint, making it suitable for systems with extremely limited resources (<1 MB of flash memory). When implemented in a wireless glove controller for mobile phone‐based human interface device protocol, the ultra‐lightweight convolutional neural network/random forest (tinyCNN/RF) model achieved up to 99.7/97.4% accuracy in on‐device action classification. Experimental results show that the proposed co‐design method optimizes system performance, reduces the impact of component‐level noise, and broadens the application potential of edge AI‐integrated smart wearable devices.