Francesca Dalia Faraci

Switch-Matrix-Based High-Density Microelectrode Array in CMOS Technology

We report on a CMOS-based microelectrode array (MEA) featuring 11, 011 metal electrodes and 126 channels, each of which comprises recording and stimulation electronics, for extracellular bidirectional communication with electrogenic cells, such as neurons or cardiomyocytes. The important features include: (i) high spatial resolution at (sub)cellular level with 3150 electrodes per mm2 (electrode diameter 7 ¿m, electrode pitch 18 ¿m); (ii) a reconflgurable routing of the recording sites to the 126 channels; and (iii) low noise levels.

Depth recording capabilities of planar high-density microelectrode arrays

We use a planar, CMOS-based microelectrode array (MEA) featuring 3,150 metal electrodes per mm2 and 126 recording channels to record spatially highly resolved extracellular action potentials (EAPs) from Purkinje cells (PCs) in acute cerebellar slices. An Independent-Component-Analysis-based (ICA) spike sorter is used to reveal EAPs of single cells at subcellular resolution. Those EAPs are then used to set up a compartment model of a PC. The model is used to make and finetune estimations of the distance between MEA surface and PC soma. This distance is estimated using the amplitude-independent part of the shape of the EAPs obtained from recordings. The estimation shows that, in our preparations, we can record from PCs with the center of their soma at approximately 35 µm and 90 µm vertical distance to the chip surface.

Using barometric pressure data to recognize vertical displacement activities on smartphones

A method for detecting major vertical displacements in human activities is proposed.Prediction is based on barometers available on smartphones and inference models.Decision trees are a good choice for their high accuracy and low energy consumption.Barometers offer high accuracy, energy efficiency and independence from position. We introduce a novel, efficient methodology for the automatic recognition of major vertical displacements in human activities. It is based exclusively on barometric pressure measured by sensors commonly available on smartphones and tablets. We evaluate various algorithms to distinguish dynamic activities, identifying four different categories: standing/walking on the same floor, climbing stairs, riding an elevator and riding a cable-car. Activities are classified using standard deviation and slope of barometric pressure. We leverage three different inference models to predict the action performed by a user, namely: Bayesian networks, decision trees, and recurrent neural networks. We find that the best results are achieved with a recurrent neural network (reaching an overall error rate of less than 1%). We also show that decision tree classifiers can achieve good accuracy and offer a better trade-off between computational overhead and energy consumption; therefore, they are good candidates for smartphone implementations. As a proof of concept, we integrate the decision tree classifier in an App that infers user activity and measures elevation differences. Test results with various users show an average recognition accuracy rate of about 95%. We further show the power consumption of running barometric pressure measurements and analyse the correlation of pressure with environmental factors. Finally, we compare our approach to other standard methodologies for activity detection based on accelerometer and/or on GPS data. Our results show that our technique achieves similar accuracy while offering superior energy efficiency, independence from the sensor location, and immunity to environmental factors (e.g., weather conditions, air handlers).

Interaction of emerging mobile telecommunication systems with the human body

Emerging telecommunication systems, with new devices and new transmission techniques modify the exposure of the population to electromagnetic fields [UMTS Forum, October 2000] [UMTS Forum, January 2002]. Although the risk for human health is currently thought to be low, there is a need to clarify these changes. This work involves analyzing typical scenarios and worst cases and developing new techniques and standards for the compliance testing of personal communication devices. The approach can be divided into two interconnected parallel strands. Numerical models have been developed, tested and refined to reproduce and predict non-ionizing radiation (NIR) exposure of the human body using finite difference time domain technique (FDTD). Experimental validation has been performed, using physical phantoms and human volunteers.