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Page 28 of 44 Jung et al. Soft Sci 2024;4:15 https://dx.doi.org/10.20517/ss.2024.02

[265]
levels . With an advance of electronic systems and wearable technology, the same group developed
wearable microneedle-based sensor arrays for real-time monitoring of various biomarkers, such as lactate
and glucose, or alcohol and glucose in ISF biofluid [Figure 9L]. Reusability was enhanced using disposable
MNA, electronics and optimized system integration. Through the comparison of gold-standard
measurements in blood or breath, dual-analyte measurements using this system showed a good correlation
[266]
with a commercial technology . For more sophisticated metabolic profiling and monitoring, a universal
wearable biosensing strategy using molecularly imprinted polymer (MIP)-based artificial antibodies was
presented by Wang et al. [Figure 9M]. Autonomous and continuous molecular analysis was realized by
incorporating iontophoresis-based sweat induction and microfluidic-based sweat sampling. Several human
trials demonstrated the possibility of personalized monitoring of five essential or conditionally essential
[267]
amino acids for central fatigue, standard dietary intake, and COVID-19 severity .

Despite these promising developments, effectively using wearable DM monitoring systems still requires
more optimization and validation to thoroughly assess their reliability, versatility, and accuracy. These
detailed studies will determine how well non-invasive methods can monitor biomarkers in biofluids and
will compare the non-invasive data with traditional blood glucose measurements. Other limitations with
wearable approaches include inconsistent collection of biofluids, contamination on the electrode surface,
and the performance changes due to various physiological factors (such as temperature and pH) on the
accuracy of the readings.

To enhance DM management, multiplexed analysis for the diabetes-related biomarkers, such as
electrophysiological factors, insulin, ketone, alcohol, etc., can achieve a more comprehensive assessment of
patient health than singular glucose measurement. Nevertheless, the implementation of multi-analyte
sensing platforms entails several significant challenges, including the integration of different surface
modifications and sensing modalities into a single wearable platform, the mitigation of analytical
interference or cross-talk among different analytes, the necessity for receptor regeneration in bioaffinity-
based assay methods, and the maximization of sensitivity for analytes in biofluids due to the lower
concentration than blood. Consequently, future research should proceed in the direction of integrating
glucose sensing with other novel analytes and skills for clinical use while overcoming these hurdles.

Machine learning-based multiplexed analysis for diabetes mellitus and its complications
In the evolving landscape of DM management, ML algorithms have emerged as a key technological
advancement. Central to these algorithms is using extensive data sets to build predictive models, particularly
vital in glucose monitoring applications. Wearable devices and CGM systems play a crucial role by
providing the necessary input and output data. The process involves dividing this data into training and
evaluation sets, which helps refine the model parameters and assess its performance. By harnessing
sophisticated computational methodologies, there exists the prospect of substantially enhancing the
management of DM, rendering it more individualized and attuned to the specific requirements of each
patient. This entails the anticipation of prevailing glucose levels, prognostication of future trends, and the
provision of informed recommendations for insulin administration. This section presents several ML
techniques in glucose monitoring. The associated studies are summarized in [Table 7].

Artificial neural network
Artificial neural networks (ANN) have become a cornerstone in advancing DM monitoring, primarily due
to their ability to model complex relationships within data. The structure of an ANN is inspired by the
human brain, comprising layers of interconnected nodes or neurons. These include input layers, which
receive data such as blood glucose levels, dietary information and physical activity, hidden layers where the