VLSI PROJECTS ABSTRACT 2016-2017
LOW-POWER SYSTEM FOR DETECTION OF SYMPTOMATIC PATTERNS IN AUDIO BIOLOGICAL SIGNALS
In the past decade, rapid advancements in the development of low-power design methodologies have resulted in feasible designs for various wearable and implantable medical systems. Numerous wearable health monitoring systems have been proposed in order to deliver early warning of an impending health condition. These systems monitor various internal as well as external parameters related to the human health, such as temperature, heart rate, and so on. Apart from these parameters, it is well known that acoustic symptoms, such as cough, sneeze, belching, and so on, are early markers of serious health issues, such as influenza, diarrhea, and whooping cough, especially among children. If repetitive occurrence of these symptoms is detected in advance, it is possible for the patient or the healthcare personnel to commence remedial action prior to aggravation of the problem. In the literature, most of the developed systems detect a single acoustic symptom (cough or sneeze). The Kids Health Monitoring System (KiMS) proposed in uses wearable sensors and acoustic signal processing in order to provide health monitoring in children. Using the neural network-based processing, the KiMS classifies various symptoms and activities and, subsequently, transmits the record to a parent or doctor for further analysis.
We describe the proposed algorithm and the methodology used to modify the various computational tools in order to make it implementable into low-power hardware. In Section II, we had described the basics and justified the basis for selecting specific computational techniques used in developing this algorithm. The application of these computations is dependent on the characteristic property of the symptom to be detected. The algorithm methodology is shown in Fig. 1. We also describe the details along with the mapping of algorithm to specific signals as follows.
• efficient low-power health monitoring system
• High power for monitoring system.
• Xilinx ISE