A wearable, low-power, health-monitoring instrumentation based on a programmable system-on-chip^TM

Abstract

Improvement in quality and efficiency of health and medicine, at home and in hospital, has become of paramount importance. The solution of this problem would require the continuous monitoring of several key patient parameters, including the assessment of autonomic nervous system (ANS) activity using non-invasive sensors, providing information for emotional, sensorial, cognitive and physiological analysis of the patient. Recent advances in embedded systems, microelectronics, sensors and wireless networking enable the design of wearable systems capable of such advanced health monitoring. The subject of this article is an ambulatory system comprising a small wrist device connected to several sensors for the detection of the autonomic nervous system activity. It affords monitoring of skin resistance, skin temperature and heart activity. It is also capable of recording the data on a removable media or sending it to computer via a wireless communication. The wrist device is based on a Programmable System-on-Chip (PSoC) from Cypress: PSoCs are mixed-signal arrays, with dynamic, configurable digital and analogical blocks and an 8-bit Microcontroller unit (MCU) core on a single chip. In this paper we present first of all the hardware and software architecture of the device, and then results obtained from initial experiments.

FPGA based reconfigurable body area network using Nios II and uClinux

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“…By enabling unobtrusive monitoring and providing increased mobility, it broadens the scope of possible heart condition monitoring applications [Yazicioglu et al 2009], including epileptic seizure detection (ESD) [Van Elmpt et al 2006]. Most of the current ECG-monitors focus either on increasing the number of functionalities [Kyriacou et al 2007] or on reducing the power consumption and miniaturizing the form factor, leaving the signal processing to a computer or a handheld unit [Massot et al 2009;Chulsung et al 2006]. Often, the embedded beat detection algorithm that is the ground layer for every heart rate-based analysis, is not well-characterized and therefore may not be a reliable basis for the analysis as in [Kyriacou et al 2007] and[Chulsung et al 2006].…”