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.

Computer-assisted in vitro reconstitution of purine degradation pathway to lower the purine content in food.

BACKGROUND: With the increasing prevalence of gout and its etiological hyperuricemia, dietary control of gout based on low-purine food according to patients' eating habits is becoming a better choice compared to the existing drug treatment such as allopurinol with notorious side effects. Reconstructing the purine metabolic pathway in vitro to degrade purine substances in food into natural functional allantoin appears to be an innovative method for preparing nutritious and healthy food of low purine content. The present study reports a computer-assisted in vitro reconstruction of four purinolytic enzymes metabolizing adenosine into allantoin to reduce purine content in food for personalized dietary control of hyperuricemia and gout. RESULTS: Under the optimum reaction conditions of 40 °C and pH 7, 0.1 U of enzymes and 0.5 mmol L-1 adenosine determined by an orthogonal test design, 16 different enzyme complexes were experimentally tested. The tested enzyme composition and allantoin production values were used as input and output to build a three-layer back propagation artificial neural network (BP-ANN) model, which was further optimized by a genetic algorithm (GA). The optimum enzyme complex predicted by the GA-BP-ANN model produced 248.08±7.832 µmol L-1 allantoin, which was 19.9% higher than equimolar mixture of enzymes, and also more efficiently lowered purine contents in beer, as well as beef and yeast extracts. CONCLUSION: This is the first in vitro reconstitution of complete purine metabolic pathway by combining ANN and GA technologies, with successful application with respect to lowering the purine content in food, indicating a promising application of computer-assisted in vitro reconstitution of purinolytic pathway in low-purine food to prevent hyperuricemia and gout. © 2022 Society of Chemical Industry.