Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers, to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems as proponents for driving biomedical circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables

A wireless, low-power, smart sensor of cardiac activity for clinical remote monitoring

International audienceThis paper presents the development of a wireless wearable sensor for the continuous, long-term monitoring of cardiac activity. Heart rate assessment, as well as heart rate variability parameters are computed in real time directly on the sensor, thus only a few parameters are sent via wireless communication for power saving. Hardware and software methods for heart beat detection and variability calculation are described and preliminary tests for the evaluation of the sensor are presented. With an autonomy of 48 hours of active measurement and a Bluetooth Low Energy radio technology, this sensor will form a part of a wireless body network for the remote mobile monitoring of vital signals in clinical applications requiring automated collection of health data from multiple patients

Training-to-Beat bioreactor for investigating Engineered Cardiac Tissues: design, development & validation

In last the decades, advances relevant to the generation of 3D Engineered Cardiac Tissues (ECTs) have been made. In reason of this, ECTs are now considered a great promise for in vitro studies of cardiac development, disease and, eventually, for strategies for the repair of the structure and function of the injured myocardium. Among the several physical stimuli which have been exploited to improve the functionality and maturation of ECTs, a preeminent role has been ascribed to mechanical stimulation. Appropriate mechanical stimulation can be recreated and maintained within bioreactors, which are devices/platforms devoted to mimic the physiological milieu in a monitored/controlled culture environment, where the engineered constructs can be properly stimulated. One main limitation of the bioreactor-based strategy for cardiac tissue engineering applications is that the devices which are currently used are meant to passively apply to ECTs a stimulus predefined by the user, regardless of their level of maturation along the duration of the in vitro culture. In this scenario, and trying to overcome current limitations, a novel bioreactor design has been conceived for the investigation of 3D ECTs with a biomimetic approach. Technically, the here proposed bioreactor is capable (1) to apply native-like or pathologic mechanical stimuli (cyclic strain) by means of a reliable linear actuator operating in a wide range of strains and frequencies, and (2) to monitor in real-time both chemo-physical parameters (e.g. oxygen tension, pH) of the milieu and the mechanical stiffness of ECTs by means of dedicated sensors, eventually adapting the stimulation to the actual stage of maturation of the constructs. As a proof of concept, a first experimental campaign has been carried out with a double aim: (1) to verify the bioreactor feasibility in delivering mechanical cyclical stimulation to 3D fibrin-based, ring shaped Engineered Cardiac Tissues (ECTs); (2) to assess the effect of cyclic strain on tissue maturation, contractility and modification on its mechanical properties. In detail, the bioreactor platform has been preliminarily tested to verify protocols for hold on, sterilization, and control of the delivered mechanical stimuli. Firstly, the suitability of the bioreactor platform in culturing ad-hoc designed constructs, in terms of ease of use and capability in setting the stimulation parameters, has been tested. Then, the observed maturation of ring shaped ECTs subject to sinusoidal cyclic strain within the bioreactor has confirmed the potency of the proposed approach and the instrumental role of mechanical stimulation in ECTs maturation and in the development of an adult-like cardiac phenotype responsive to electrical excitation. Even if further validation steps are required before the implementation of culture strategy fully adaptive in terms of mechanical stimuli applied to the engineered cardiac constructs, the developed bioreactor represents a valuable proof of concept for, in its most advanced operational mode, biomimetic culturing of engineered cardiac constructs

A modular multi electrode array system for electrogenic cell characterisation and cardiotoxicity applications

Multi electrode array (MEA) systems have evolved from custom-made experimental tools, exploited for neural research, into commercially available systems that are used throughout non-invasive electrophysiological study. MEA systems are used in conjunction with cells and tissues from a number of differing organisms (e.g. mice, monkeys, chickens, plants). The development of MEA systems has been incremental over the past 30 years due to constantly changing specific bioscientific requirements in research. As the application of MEA systems continues to diversify contemporary commercial systems are requiring increased levels of sophistication and greater throughput capabilities. [Continues.

Development of experimental setups for the characterization of the mechanoelectrical coupling of cells in vitro

The field of mechanobiology emerged from the many evidences that mechanical forces acting on cells have a central role in their development and physiology. Cells, in fact, convert such forces into biochemical activities and gene expression in a process referred as mechanotransduction. In vitro models that mimic cell environment also from the mechanical point of view represent therefore a key tool for modelling cell behaviour and would find many applications, e.g. in drug development and tissue engineering. In this work I introduce novel tools for the study of mechanotransduction. In particular, I present a system for the evaluation of the complex response of electrically active cells, such as neurons and cardiomyocytes. This system integrates atomic force microscopy, extracellular electrophysiological recording, and optical microscopy in order to investigate cell activity in response to mechanical stimuli. I also present cell scaffolds for the in vitro study of cancer. Obtained results, although preliminary, show the potential of the proposed systems and methods to develop accurate in vitro models for mechanobiology studies

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals

Funding and Strategic Alignment Guidance for Infusing Small Business Innovation Research Technology Into NASA Programs Associated With the Human Exploration and Operations Mission Directorate

This report is intended to help NASA program and project managers incorporate Small Business Innovation Research/Small Business Technology Transfer (SBIR/STTR) technologies that have gone through Phase II of the SBIR program into NASA Human Exploration and Operations Mission Directorate (HEOMD) programs. Other Government and commercial project managers can also find this information useful