Embedded system design for wearable applications

New generation technologies and device miniaturization are boosting the development of wearable unobtrusive embedded systems, capable to acquire and process physiological signals, whereas making sense of the data retrieved with such devices requires real-time execution of advanced algorithms on resource-constrained platforms. Hence, they require a multi-modal approach to reach miniaturized form factors at extreme energy efficiency. In this course, we will give an overview of the HW-SW co-design of such systems, ranging from acquisition circuits to algorithms. We will describe embedded system design for wearable application in 2 classes of physiological signals: inertial data and biopotentials (EEG and EMG in particular). The course will describe basic concepts in signal acquisition from digital Inertial Measurement Units, analog/digital EMG and EEG. It will also explore advanced processing techniques and machine learning optimized for embedded platform to extract meaningful information close to the device and preserving energy efficiency.  Examples from rehabilitation, prosthetics and HMI domain will be presented