Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA

被引:1
|
作者
Frutuoso, Maria Ines [1 ]
Neto, Horacio C. [1 ]
Vestias, Mario P. [2 ]
Duarte, Rui Policarpo [2 ]
机构
[1] Univ Lisbon, INESC ID, Inst Super Tecn, P-1049001 Lisbon, Portugal
[2] Inst Politecn Lisboa, Inst Super Engn Lisboa, INESC ID, P-1959007 Lisbon, Portugal
关键词
electroencephalography; hardware; software co-design; photoplethysmography; SoC FPGA; wearable monitoring devices;
D O I
10.3390/a16030141
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Wearable devices used for personal monitoring applications have been improved over the last decades. However, these devices are limited in terms of size, processing capability and power consumption. This paper proposes an efficient hardware/software embedded system for monitoring bio-signals in real time, including a heart rate calculator using PPG and an emotion classifier from EEG. The system is suitable for outpatient clinic applications requiring data transfers to external medical staff. The proposed solution contributes with an effective alternative to the traditional approach of processing bio-signals offline by proposing a SoC-FPGA based system that is able to fully process the signals locally at the node. Two sub-systems were developed targeting a Zynq 7010 device and integrating custom hardware IP cores that accelerate the processing of the most complex tasks. The PPG sub-system implements an autocorrelation peak detection algorithm to calculate heart rate values. The EEG sub-system consists of a KNN emotion classifier of preprocessed EEG features. This work overcomes the processing limitations of microcontrollers and general-purpose units, presenting a scalable and autonomous wearable solution with high processing capability and real-time response.
引用
收藏
页数:16
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