Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring

Many devices and solutions for remote electrocardiogram (ECG) monitoring have been proposed in the literature. These solutions typically have a large marginal cost per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-technical users in need of long-term health monitoring in residential environments and is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the state of the art, considering both the prototypes with integrated front end and prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture providing low marginal cost per added sensor/user; and 3) the possibility of seamless integration with other smart home systems through a single IoT infrastructure

Building Blocks for Adaptive Modular Sensing Systems

This thesis contributes towards the development of systems and strategies by which sensor and actuator components can be combined to produce flexible and robust sensor systems for a given application. A set of intelligent modular blocks (building blocks) have been created from which composite sensors (made up of multiple sensor and actuator components) can be rapidly reconfigured for the construction of Adaptive Modular Sensing Systems. The composite systems are expected to prove useful in several application domains including industrial control, inspection systems, mobile robotics, monitoring and data acquisition. The intelligent building blocks, referred to as transducer interface modules, contain embedded knowledge about their capabilities and how they can interact with other modules. These modules encapsulate a general purpose modular hardware architecture that provides an interface between the sensors, the actuators, and the communication medium. The geometry of each transducer interface module is a cube. A connector mechanism implemented on each face of the module enables physical connection of the modules. Each module provides a core functionality and can be connected to other modules to form more capable composite sensors. Once the modules are combined, the capabilities (e.g., range, resolution, sample rate, etc.) and functionality (e.g., temperature measurement) of the composite sensor is determined and communicated to other sensors in the enviornment. For maximum flexibility, a distributed software architecture is executed on the blocks to enable automatic acquisition of configuration-specific algorithms. This logical algorithm imparts a collective identity to the composite group, and processes data based on the capabilities and functionalities of the transducers present in the system. A knowledge representation scheme allows each module in the composite group to store and communicate its functionality and capabilities to other connected modules in the system

Flexible data input layer architecture (FDILA) for quick-response decision making tools in volatile manufacturing systems

This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments’ Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant’s Key Performance Factors

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa

Hardware Architectures for Low-power In-Situ Monitoring of Wireless Embedded Systems

As wireless embedded systems transition from lab-scale research prototypes to large-scale commercial deployments, providing reliable and dependable system operation becomes absolutely crucial to ensure successful adoption. However, the untethered nature of wireless embedded systems severely limits the ability to access, debug, and control device operation after deployment—post-deployment or in-situ visibility. It is intuitive that the more information we have about a system’s operation after deployment, the better/faster we can respond upon the detection of anomalous behavior. Therefore, post-deployment visibility is a foundation upon which other runtime reliability techniques can be built. However, visibility into system operation diminishes significantly once the devices are remotely deployed, and we refer to this problem as a lack of post-deployment visibility