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Herein, we developed a flexible, low-cost non-enzymatic sweat sensing chip for in situ acquisition of bioinformation in sweat of individuals under exercise conditions to advance personal health monitoring and medication management for patients with Parkinson's disease. This low-cost, flexible, wearable sweat sensor consists of a printed screen electrode modified with g-C3N4 material and an external MSME element. The doping strategy and surface activation strategy of the g-C3N4-based exhibited efficient glucose oxidase-like activity and electrochemical activity when testing l-dopa and glucose in sweat. The optimized signal was transmitted to a smartphone for processing 12 individuals with simulated dosing, enabling continuous monitoring of l-dopa metabolism in sweat and management of dosing. The generalization ability and robustness of models constructed by methods such as multiple linear regression, artificial neural networks, and convolutional neural networks were compared cross-sectionally. Deep learning models based on artificial neural networks help develop a user-personalized medication administration reminder system, which provides a promising paradigm for reliable medication supervision for Parkinson's patients in the Internet of Things era.
Yu et al. (Fri,) studied this question.