Erratum to “A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device” [Aug 20 889-902]

Ng, Kian Ann; Rusly, Astrid; Gammad, Gil Gerald Lasam; Le, Nguyen; Liu, Shih-Chiang; Leong, Kenneth; Zhang, Miaolin; Ho, John S.; Yoo, Jerald; Yen, Shih-Cheng

doi:10.1109/tbcas.2020.3028226

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“…Incorporation of smart detection algorithm on wearable device, i.e. Artificial Intelligence-of-Things (AIoT), would provide prompt or earlier warning [2]- [6]. To facilitate such approach, resource and energy-efficient implementation for the detection algorithm with decent accuracy and sensitivity would be critical [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Resource and Energy Efficient Implementation of ECG Classifier Using Binarized CNN for Edge AI Devices

Wong

John

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Wearable Artificial Intelligence-of-Things (AIoT) devices demand smart gadgets that are both resource and energy-efficient. In this paper, we explore efficient implementation of binary convolutional neural network employing function merging and block reuse techniques. The hardware implemented in field programmable gate array (FPGA) platform can classify ventricular beat in electrocardiogram achieving accuracy of 97.5%, sensitivity of 85.7%, specificity of 99.0%, precision of 92.3%, and F1-score of 88.9% while consuming only 10.5-µW of dynamic power dissipation.

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