Multi-characteristic tannic acid-reinforced polyacrylamide/sodium carboxymethyl cellulose ionic hydrogel strain sensor for human-machine interaction.

Big data and cloud computing are propelling research in human-computer interface within academia. However, the potential of wearable human-machine interaction (HMI) devices utilizing multiperformance ionic hydrogels remains largely unexplored. Here, we present a motion recognition-based HMI system that enhances movement training. We engineered dual-network PAM/CMC/TA (PCT) hydrogels by reinforcing polyacrylamide (PAM) and sodium carboxymethyl cellulose (CMC) polymers with tannic acid (TA). These hydrogels possess exceptional transparency, adhesion, and remodelling features. By combining an elastic PAM backbone with tunable amounts of CMC and TA, the PCT hydrogels achieve optimal electromechanical performance. As strain sensors, they demonstrate higher sensitivity (GF = 4.03), low detection limit (0.5 %), and good linearity (0.997). Furthermore, we developed a highly accurate (97.85 %) motion recognition system using machine learning and hydrogel-based wearable sensors. This system enables contactless real-time training monitoring and wireless control of trolley operations. Our research underscores the effectiveness of PCT hydrogels for real-time HMI, thus advancing next-generation HMI systems.

Gesture Recognition System Using Reduced Graphene Oxide-Enhanced Hydrogel Strain Sensors for Rehabilitation Training.

Gesture recognition systems epitomize a modern and intelligent approach to rehabilitative training, finding utility in assisted driving, sign language comprehension, and machine control. However, wearable devices that can monitor and motivate physically rehabilitated people in real time remain little studied. Here, we present an innovative gesture recognition system that integrates hydrogel strain sensors with machine learning to facilitate finger rehabilitation training. PSTG (PAM/SA/TG) hydrogels are constructed by thermal polymerization of acrylamide (AM), sodium alginate (SA), and tannic acid-reduced graphene oxide (TA-rGO, TG), with AM polymerizing into polyacrylamide (PAM). The surface of TG has abundant functional groups that can establish multiple hydrogen bonds with PAM and SA chains to endow the hydrogel with high stretchability and mechanical stability. Our strain sensor boasts impressive sensitivity (Gauge factor = 6.13), a fast response time (40.5 ms), and high linearity (R2 = 0.999), making it an effective tool for monitoring human joint movements and pronunciation. Leveraging machine learning techniques, our gesture recognition system accurately discerns nine distinct types of gestures with a recognition accuracy of 100%. Our research drives wearable advancements, elevating the landscape of patient rehabilitation and augmenting gesture recognition systems' healthcare applications.