The exponential growth of wearable healthcare devices market is fostered by the internetof-
things (IoT) era. Connected smart biosensors enable a decentralized healthcare that
does not constrain the user to be in a medical facility to get a real-time insight on his
health status and a medical diagnosis from a doctor. Moreover, remote physiological
monitoring is appealing in sport applications where athletes need real-time feedback on
their level of dehydration and muscle fatigue in order to optimize their performances.
Electrochemical sensors play a crucial role in physiology and healthcare monitoring since
they provide information at molecular level, where the biosensor is in direct contact
with bodily fluids such as sweat. A comprehensive healthcare diagnosis is achieved by
continuously monitoring several types of biomarkers because of correlations between
biological compounds. Namely, endogenous metabolites such as lactate, or potassium and
ammonium ions, enable the quantification of muscle fatigue, hence, preventing muscle
cramping. In therapeutic drug monitoring, exogenous compounds are continuously tracked
so that the drug is maintained in its therapeutic range, in order to always be effective
and not toxic for the patient. Besides multi-sensing and general-purpose capabilities,
electrochemical platforms need to be correlated to the health and physiological status
of the user, where the large amount of measured biological data must be accurately
processed and interpreted by smart data analytic tools.

This thesis covers the design, implementation, characterization, and validation of hardware
and software interfaces for multi-panel electrochemical sensing platforms.
A multi-mode hardware front-end enabling voltammetric and potentiometric measurements
is designed to provide a continuous and concurrent monitoring of endogenous
metabolites, drugs, and electrolytes. This versatile and multi-sensing platform offers a
portable solution for remote and comprehensive healthcare monitoring.
Moreover, a multi-ion-sensing front-end is designed for accurate physiology in sweatsensing
applications. The hardware is proposed as a solution for multiple electrolyte
detection in artificial sweat samples. In such complex media, multi-ion-sensors are subject
to interference from background electrolytes that considerably distorts sensor response.
Therefore, a compact and analytical model of ion-sensing transduction mechanism is
proposed to understand both qualitatively and quantitatively the non-linearity induced
by these artifacts. The ion-sensor model is implemented at the core of an emulator of
synthetic datasets that is built to simulate ion-sensor responses in artificial sweat samples.
The emulator addresses the expensive time and chemical resources needed to acquire large
database for training multivariate calibration models. Thus, the emulated data is used for
the training and optimization of a multi-output support vector regressor that is proposed
as an accurate, unbiased, robust, compact, low-complexity, and low-latency estimator
for the multivariate calibration of multi-ion-sensors. Then, the multi-ion-sensing array,
the analog front-end interface, and the chemometric model deployed on a Raspberry
Pi, are seamlessly co-integrated for the monitoring of sodium, potassium, ammonium,
and calcium ions in artificial sweat, within an IoT framework for real-time and accurate
physiology.