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Co-Design of Stretchable Fabric Sensors and Tiny Machine Learning for Human Interface Device-Based Edge-Intelligent Wearable Gloves
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DOI：10.1002/aisy.70463.png)
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Abstract
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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.
Keywords:
edge aI inference
embedded machine learning
human interface devices (HID)
strain fabric sensors
wearable system
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