Machine Learning Enables Reliable Colorimetric Detection of pH and Glucose in Wearable Sweat Sensors

Lijun Zhou, Sidharth S. Menon, Xinqi Li, Miqin Zhang, Mohammad H. Malakooti
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Abstract

In healthcare, blood pH and glucose levels are critical indicators, especially for chronic conditions like diabetes. Although taking blood samples is accurate, it is invasive and unaffordable for many. Wearable sensors offer non‐invasive and continuous detection methods, yet face major challenges, such as high cost, inaccuracies, and complex interpretation. Colorimetric wearable sensors integrated with machine learning (ML) are introduced for accurately detecting pH values and glucose concentrations in sweat. These battery‐free and cost‐effective biosensors, made of cotton textiles, are designed to work seamlessly with smartphones for data collection and automated analysis. A new pH indicator is synthesized with enhanced sensitivity and two types of glucose sensors are developed by depositing enzymatic solutions onto cotton substrates. The sensors' performance is assessed using standard solutions with known pH levels ranging from 4 to 10 and glucose concentrations between 0.03 to 1 mm. The photos captured from these sensors are then analyzed by image processing and three different ML algorithms, achieving an accuracy of 90% in pH and glucose detection. These findings provide effective synthesis methods for textile‐based sweat sensors and demonstrate the significance of employing different ML algorithms for their colorimetric analysis, thus eliminating the need for human intervention in the process.

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机器学习实现对可穿戴式汗液传感器中 pH 值和葡萄糖的可靠比色检测
在医疗保健领域,血液 pH 值和葡萄糖水平是至关重要的指标,尤其是对糖尿病等慢性疾病而言。虽然采集血样是准确的,但对许多人来说是侵入性的,而且负担不起。可穿戴传感器提供了非侵入性的连续检测方法,但也面临着高成本、不准确和复杂解释等重大挑战。本文介绍了与机器学习(ML)集成的比色可穿戴传感器,用于准确检测汗液中的 pH 值和葡萄糖浓度。这些免电池、高性价比的生物传感器由棉纺织品制成,可与智能手机无缝配合,用于数据收集和自动分析。通过在棉基质上沉积酶溶液,合成了一种灵敏度更高的新型 pH 指示剂,并开发了两种类型的葡萄糖传感器。使用已知 pH 值在 4 到 10 之间、葡萄糖浓度在 0.03 到 1 mm 之间的标准溶液,对传感器的性能进行了评估。然后通过图像处理和三种不同的 ML 算法对这些传感器拍摄的照片进行分析,pH 值和葡萄糖检测的准确率达到 90%。这些发现为基于纺织品的汗液传感器提供了有效的合成方法,并证明了采用不同的 ML 算法进行比色分析的意义,从而消除了在此过程中的人工干预需求。
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