Three-Dimensional Integrated Ultra-Low-Volume Passive Microfluidics with Ion-Sensitive Field-Effect Transistors for Multiparameter Wearable Sweat Analyzers

Wearable systems could offer noninvasive and real-time solutions for monitoring of biomarkers in human sweat as an alternative to blood testing. Recent studies have demonstrated that the concentration of certain biomarkers in sweat can be directly correlated to their concentrations in blood, making sweat a trusted biofluid candidate for non-invasive diagnostics. We introduce a fully on-chip integrated wearable sweat sensing system to track biochemical information at the surface of the skin in real time. This system heterogeneously integrates, on a single silicon chip, state-of-the-art ultra-thin body (UTB) fully-depleted silicon-on-insulator (FD-SOI) ISFET sensors with a biocompatible microfluidic interface, to deliver a “Lab-on-skinTM” sensing platform. A full process for the fabrication of this system is proposed in this work and demonstrated by standard semiconductor fabrication procedures. The system is capable of collecting small volumes of sweat from the skin of a human, and posteriorly passively driving the biofluid, by capillary action, to a set of functionalized ISFETs for analysis of pH level and Na+ and K+ concentrations. Drop-casted Ion Sensing Membranes (ISM) on the different sets of sensors on the same substrate enables multi-parameter analysis on the same chip, with small and controlled cross-sensitivities, while a miniaturized Quasi-Reference Electrode (QRE) sets a stable analyte potential, avoiding the use of a cumbersome external Reference Electrode (RE). The progresses on Lab-on-SkinTM technology reported here can lead to autonomous wearable systems enabling real-time continuous monitoring of the sweat composition, with applications ranging from medicine to lifestyle behavioral engineering and sports.

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Benoît Mariani

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions.

EPFL2012

Sweat monitoring with CMOS compatible technology: ISFETS and beyond

Francesco Bellando

In recent years, sweat has gained increasing attention from the scientific community as a new analyte for health monitoring. The main advantage with respect to the "Gold Standard" for laboratory analysis, i.e. blood, is of course the possibility of performing non-invasive assays, granting the maximum comfort for the user. Sweat contains a wide collection of relevant biomarkers, such as ions (Na+, K+, Mg+, Ca++...) and biomolecules (lactate, cortisol, cholesterol...). The most widespread technology for wearable sweat sensors, as of now, consists in the employ of big electrodes for potentiometric detection of target markers. This strategy, on top of not being CMOS-compatible and therefore harder to integrate in a complex wearable system (e. g. a smartwatch), requires a huge amount of sweat, which is produced only under intense heath or physical activity. Ion-Sensitive Field-Effect Transistors (ISFETs), on the other hand, can be made extremely small without reducing their sensing performances, allowing monitoring of sweat composition at very low sweating rates, compatible with at-rest values. In this work, such advantage is pursued via the integration of a low-volume capillary microfluidics, with a capacity of just a few hundreds of nanoliters, on a chip containing a set of Fully-Depleted Silicon-On-Insulator FETs (FD SOI FETs) and miniaturized Ag/AgCl Reference Electrodes (mREs). This system, able to host up to four different functionalizations, each for monitoring a specific sweat biomarker, is capable of collecting sweat from the skin of an user at rest, passively driving it in contact with the sensors, and readily transducing the chemical parameters measured by the functionalized gates in an electric signal, which is then analyzed by the read-out system, providing data on the composition of the analyte and, therefore, on the health condition of the wearer. The fabricated devices showed excellent performances in terms of both electrical characterization and biomarker sensitivity: the FETs characterized with a metal gate have shown an ION/IOFF ratio of 106, a nearly-ideal Subthreshold Swing (SS) of just 65 mV/dec, a hysteresis free characteristics and fully ohmic contacts on ultra-thin silicon. The threshold voltage (Vth) shifts linked to a tenfold variation in the biomarkers concentrations also reached the nearly-ideal values of 55 to 60 mV/dec, and extremely linear dependence over a wide range of dilutions. One of the strengths of the ISFET technology is its ability to exploit technological progresses of MOSFETs for computing. For this reason, in the final part of this thesis we have investigated the application to sensors of one of the latest strategies for the improvement of MOSFET performances: the Negative Capacitance (NC). Addition of a ferroelectric capacitor (Fe-Cap) to the gate stack of our sensors reduced their SS by 21%, therefore improving their current sensitivity by 26%. The measured Current Efficiency also improved. Further experiments employing different sets of Fe-Caps and pulsed mode measurements, moreover, demonstrated a SS six times lower than the one of the baseline ISFETs and three times lower than the thermionic limit, paving the way to a new class of sensors with ideality factor larger than 1.

EPFL2020

Trajectory determination and analysis in sports by satellite and inertial navigation

Adrian Wägli

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching.