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Journal ArticleDOI

A CMOS Wearable Infrared Light Intensity Digital Converter for Monitoring Unplanned Self-Extubation of Patients

Cheng-Ta Chiang, +1 more
- 01 Aug 2019 - 
- Vol. 19, Iss: 15, pp 6430-6436
TLDR
In this paper, a complementary metal oxide semiconductor wearable infrared (IR) light intensity digital converter was proposed for monitoring the unplanned self-extubation of patients. But the proposed converter could process the IR light intensity without limitations of the field of view and is immune to ambient optical noise.
Abstract
This paper proposes a complementary metal oxide semiconductor wearable infrared (IR) light intensity digital converter for monitoring the unplanned self-extubation of patients. The proposed converter could process the IR light intensity without limitations of the field-of-view and is immune to ambient optical noise. Furthermore, the output of the proposed converter can be easily transmitted to Internet of things devices. The sensing area of a monolithic photodiode was $120 \times 120\,\,\mu \text{m}^{2}$ , and the complete size of the chip was $1.21 \times 2.04\,\,\text {mm}^{2}$ . The measured distance between an IR light source and the proposed converter was 15–29 cm, and the measured signal-to-noise-distortion ratio was corresponding to 80.9–74.9 dB. Finally, an experiment was conducted for monitoring the unplanned self-extubation of patients. The results indicate that the proposed chip is suitable for usage in medical institutions.

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Citations
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Journal ArticleDOI

Recognition and Detection of Vehicle Noise and Vibration Signals Relying on Variable Step Size LMS Algorithm

TL;DR: In this paper , a variable step size LMS algorithm is applied to vehicle noise and vibration signal recognition, and the obtained optimal separability feature is used as the characteristic parameter of the vibration signal.
Journal ArticleDOI

Multimedia Digital Signal Processing of Infrared Chemical Remote Sensing Based on Piecewise Linear Discriminant Algorithm

Meitao Gong
TL;DR: In this paper, the characteristics of unilateral piecewise linear classifier applied to the infrared spectrum identification of chemical agents are studied in the field of infrared remote sensing monitoring, where the relaxation factors are used to replace the constrained conditions that cannot be optimized into constrained separate line segment calculation conditions.
References
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Book

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TL;DR: This chapter discusses the design and simulation of delta-sigma modulator systems, and some of the considerations for implementation considerations for [Delta][Sigma] ADCs.
Journal ArticleDOI

MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

TL;DR: A recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.
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Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification

TL;DR: This work presents a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which contains surface electromyography data for 6 DOF force activations as well as for 40 discrete hand movements, and proposes the movement error rate as an alternative to the standard window-based accuracy.
Journal ArticleDOI

Micropower CMOS Integrated Low-Noise Amplification, Filtering, and Digitization of Multimodal Neuropotentials

TL;DR: A 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, showing spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.
Journal ArticleDOI

sEMG-Based Identification of Hand Motion Commands Using Wavelet Neural Network Combined With Discrete Wavelet Transform

TL;DR: DWT and wavelet neural network algorithms are employed to improve the pattern recognition effects of sEMG signals and the maximum identification accuracy rate is 100%, and an average classification accuracy rate of the proposed WNN is 94.67%, which is substantially better than the artificial neural network (ANN) algorithm.
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