PhD defense Mikhail Manokhin: Towards flexible and low-power wireless smart sensors: converter for healthcare applications

Complete title: Towards flexible and low-power wireless smart sensors: reconfigurable analog-to-feature converter for healthcare applications

Jury

• Hervé BARTHELEMY, Full Professor, Université de Toulon (Reviewer)
• Philippe BENABES, Full Professor, CentraleSupélec (Reviewer)
• Rachid BOUCHAKOUR, Full Professor, Aix-Marseille Université (Examiner)
• Caroline LELANDAIS-PERRAULT, Associate Professor, CentraleSupélec (Examiner)
• Michael PELISSIER, Research Engineer, CEA-Leti (Examiner)
• Paul CHOLLET, Associate Professor, Télécom Paris (Thesis co-supervisor)
• Patricia DESGREYS, Full Professor, Télécom Paris (Thesis supervisor)

Abstract

Current human population growth and aging inevitably raise the rate of chronic diseases, the leading global cause of death. Wireless Body Area Networks (WBANs) composed of smart wearable or implantable sensors are the primary solution for proactive healthcare systems to reduce the burden of these diseases. However, such networks are severely restricted regarding energy usage and data throughput, especially in the case of biopotential and inertial sensors requiring continuous signal acquisition. Reducing the amount of collected and sent data, thus improving sensors’ autonomy, is possible in classification applications. For this purpose, this thesis aims to design a reconfigurable Analog-to-Feature (A2F) converter that extracts only relevant features for a given task in the analog domain within the sensor node and classifies further at the sensor or aggregator level. Based on Non-Uniform Wavelet Sampling (NUWS), our converter leverages a generic architecture to suit different low-frequency signals and enable WBANs with multimodal sensors. To prove the converter’s universality, we address two applications: anomaly detection in electrocardiogram (ECG) signals and human activity recognition (HAR) in inertial signals. After training the neural network classifiers for each application, we defined the relevant features and hardware specifications required for the complete circuit design. Thanks to the circuit level simulation of the converter, we can show that the estimated energy consumption is divided by 20 for ECG and 5 for HAR compared to the Nyquist approach. This fact highlights the potential of A2F conversion with NUWS in achieving flexible, reliable, and low-power sensor systems for healthcare and beyond.