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Design and development of an Internet-of-Things enabled wearable ExG measuring system with a novel signal processing algorithm for electrocardiogram

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IET CIRCUITS DEVICES & SYSTEMS 2019年第6期13卷 903-907页

作者： Das, Devarshi Mrinal Vidwans, Amogh Srivastava, Abhishek Ahmad, Meraj Vaishnav, Saujal Dewan, Sourya Baghini, Maryam Shojaei Indian Inst Technol IIT Ropar Dept Elect Engn Rupnagar Punjab India Indian Inst Technol IIT Bombay Dept Elect Engn Mumbai Maharashtra India

In this article, the design and development aspects of a compact bio-potential measuring system, named ExGSense, is presented. Two versions of the prototype have been developed;first one can measure 3 + 1 V leads in time-multiplexed fashion, while the other can measure 3 + 1 V leads simultaneously. This article also presents an efficient algorithm for filtering electrocardiogram signals which is required to attenuate the effect of motion artefacts which are inevitable in wearable systems. Further, a user-friendly interface for PC and smartphone has also been developed. By the virtue of an ultra-low noise instrumentation amplifier and the programmability of gain and bandwidth of the bio-signal measuring system, a number of other bio-potential signals like EMG, EOG and EEG have been successfully recorded using disposable, off-the-shelf wet Ag/AgCl electrodes.

关键词： electromyography medical signal processing biomedical electrodes electrocardiography electroencephalography electro-oculography instrumentation amplifiers filtering theory Internet of Things human computer interaction Internet-of-Things wearable ExG measuring system signal processing algorithm development aspects compact bio-potential measuring system time-multiplexed fashion motion artefacts user-friendly interface bio-signal measuring system bio-potential signals ultralow noise instrumentation amplifier ExGSense electrocardiogram signal filtering smartphone EMG EOG EEG disposable off-the-shelf wet Ag-AgCl electrodes Ag-AgCl

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Study and Implementation of a signal processing algorithm for Doppler Ultrasound Fetal Heart signals 14

Study and Implementation of a Signal Processing Algorithm fo...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Chuqi Ji, Chenpeng Lin, Xiaoqiang Yao, Jian Huang, Hai Zhejiang Univ Coll Biomed Engn & Instrument Sci Hangzhou Peoples R China Medzone Inst Biomed Mat & Devices Hangzhou Peoples R China

ISBN: (纸本)9781538694909

The Doppler ultrasound fetal heart signals are work and complex, while the background noise is strong. It results in many problems for household integrated Doppler fetal heart monitors. such as low sensitivity, small measuring area, low accuracy of fetal heart rates, and self-excited howling. A fetal heart signal processing algorithm is proposed in this paper, which includes the adaptive demising method based on spectral subtraction and the non-linear self-excitation suppression processing algorithm. The measuring results in a Doppler ultrasound fetal heart simulator and pregnant women show that the good denoising effect can been achieved and the signal-to-noise of the signals can been greatly enhanced by the proposed algorithm. Also, the sensitivity of fetal heart measurement is improved and the self-excited howling is eliminated effectively.

关键词： Doppler ultrasound signal processing algorithm adaptive denoising self-excited suppression

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An Innovative Rail Pressure Sensor signal processing algorithm to Determine the Start of Injection and End of Injection of Diesel Engines With Common Rail Injection Systems

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IEEE ACCESS 2018年 6卷 64674-64687页

作者： Kang, Qinmiao Li, Yong Xie, Zhifeng Liu, Yongquan Zhou, Ming Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China Chinese Aeronaut Estab Inst 606 Shenyang 110015 Liaoning Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

This paper proposes an innovative rail pressure sensor signal processing algorithm to determine the start of injection and end of injection for single or multiple injections of diesel engines with a common rail injection system (CRIS). The importance of fuel injection timing (FIT) and fuel injection volume (FIV) in the CRIS is discussed, as well as the current progress of research in this field. The working principle of a solenoid valve injector is also described, pressure wave propagation is analyzed, and the characteristics of pressure waves are studied by simulation and experiments. The proposed algorithm has high reliability and flexibility and can be adjusted in real time according to engine operating status. The algorithm can effectively avoid the defects and shortcomings of traditional engine matching calibration, enhance the control precision of the FIT and FIV, improve the consistency and stability of fuel injection, and is expected to promote the performance of engine on fuel economy and pollutant emissions. The hardware implementation of this algorithm in practical ECU is also presented.

关键词： signal processing algorithm rail pressure sensor start of injection end of injection diesel engine CRIS

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Practical signal processing algorithm for wide-area surveillance-GMTI mode

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IET RADAR SONAR AND NAVIGATION 2015年第8期9卷 991-998页

作者： Yan, He Zhu, Daiyin Wang, Robert Mao, Xinhua Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Elect Space Microwave Remote Sensing Syst Dept Beijing Peoples R China

On the basis of the real data acquired with an X-band airborne wide-area surveillance (WAS)-GMTI radar, a practical signal processing algorithm for WAS-GMTI mode is proposed, and it is simple and effective for real-time processing. The key feature of the new algorithm is that the spatial steering vector and fixed channel error are estimated according to the result of the Doppler centroid estimation. This is due to the fact that the angle corresponding to the Doppler centroid is just the antenna look direction. In this study, the detailed description of the algorithm is presented with four steps: pre-processing, clutter suppression, constant false alarm rate detection and parameter estimation. Besides, to observe the effect of the proposed algorithm, the real data processing results are shown after each step. Finally, moving target positioning results of five successive scan periods is presented to demonstrate the effectiveness of the new algorithm.

关键词： search radar radar signal processing channel estimation radar antennas parameter estimation radar clutter interference suppression radar detection signal processing algorithm X-band wide-area surveillance-GMTI radar mode WAS spatial steering vector fixed channel error estimation Doppler centroid estimation antenna look direction clutter suppression constant false alarm rate detection parameter estimation real data processing moving target positioning successive scan periods ground moving target detection mode

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A Guide to signal processing algorithms for Nanopore Sensors

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ACS SENSORS 2021年第10期6卷 3536-3555页

作者： Wen, Chenyu Dematties, Dario Zhang, Shi-Li Uppsala Univ Dept Elect Engn Div Solid State Elect SE-75103 Uppsala Sweden Mendoza Technol Sci Ctr Inst Ciencias Humanas Sociales & Ambientales CONICET RA-5500 Mendoza M Argentina

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise;hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

关键词： nanopore sensing signal processing algorithm pulse-like signal spike recognition feature extraction analyte identification machine learning neural network

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Flexible Strain Sensor Based on Conductive Hydrogel/KC@PDMS for Neck Motion Control Wheelchair Using EMD-LSTM algorithm

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IEEE SENSORS JOURNAL 2024年第3期24卷 4079-4090页

作者： Wang, Xiaoming Xiong, Longbo Dong, Wentao Guo, Zhilong Huang, Yifeng Yao, Daojin East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China

Many disabled people are not suitable for the control of the current mainstream manual joystick-controlled wheelchair, so the study of new control methods can provide convenience for these people, including bioelectrical signal control, voice control, and visual control, but these control methods have peripheral equipment complex or susceptible to environmental noise interference, in contrast. These problems can be solved effectively by using a wearable flexible sensor to recognize human movement signal. To collect and identify the strain signal characteristics of the moving neck muscles, based on the principle of flexible piezoresistive sensor, we designed and produced a K-carrageenan at polydimethylsiloxane (PDMS) flexible sensor, designed a series of experiments to explore the conductive properties, mechanical properties, and air permeability of the sensor, and also designed the peripheral acquisition circuit and signal processing algorithm. Finally, the empirical mode decomposition (EMD)-long short-term memory (LSTM) algorithm is used to classify the signal and explore the feasibility of fitting the signal to the human neck to control the wheelchair. The results show that the maximum tensile rate of the sensor designed by us is as high as 180%, and the air permeability is also improved slightly. After 1000 cycles of stretching, the sensor still has good performance and can effectively collect human neck action signals. Through the field test, the accuracy of this system is up to 95%, which provides a new idea for wheelchair control.

关键词： Sensors Wheelchairs Sensor phenomena and characterization Neck Hydrogels Classification algorithms Capacitive sensors Flexible sensor neck movement recognition signal processing algorithm wheelchair control

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Safety in e-Scooters: a Machine-Learning Approach for Online Second Passenger Detection 17

Safety in e-Scooters: a Machine-Learning Approach for Online...

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17th IFAC Symposium on Control of Transportation Systems (CTS)

作者： Leoni, Jessica Tanelli, Mara Strada, Silvia Carla Savaresi, Sergio Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB Milan Italy

Electric scooters are pivotal for urban mobility, yet safety concerns still prevent widespread adoption. Research identifies rider negligence, such as carrying a second passenger, as a major risk. To address this issue, we propose an autonomous system for real-time detection of second passengers. By analyzing vehicle dynamics through minimal sensors and employing an interpretable machine learning approach, our solution ensures accuracy and interpretability. Rigorous testing with diverse users validates its effectiveness, showcasing adaptability to user characteristics and road conditions, proving the potential of this approach to foster safer electric scooter usage. Copyright (c) 2024 The Authors.

关键词： Electric scooter risk detection machine-learning signal processing algorithm smart mobility

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Research of AI driven light health and therapy 48

Research of AI driven light health and therapy

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Wang, Xiaofei Zhao, Yi Yu, Tao Ren, Jie Yan, Huafeng Song, Jian Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Tsinghua Changgung Hosp Photomed Lab Beijing Peoples R China Tongji Univ Dept Architecture Shanghai Peoples R China Medpower Technol Co Ltd Suzhou Peoples R China

ISBN: (纸本)9798350376975;9798350376968

This article reports an experimental system of light health and therapy driven by Artificial intelligence (AI) technology. Light health and therapy, also known as phototherapy, is a therapy used to treat a variety of depression and neurodegenerative diseases, such as Seasonal Affective disorder (SAD) and Alzheimer's Disease (AD). Light therapy affect activity, functional connectivity and plasticity of multiple brain regions by adjusting the frequency and color temperature of illuminating system. The retina converts photon into electrical signals and transmits these stimulus into central nervous system to make resonances and oscillations to boost human mood and improve cognitive function. In life science, non-imaging vision system refers to light signal projected to the higher brain region through intrinsically photosensitive retinal ganglion cell (ipRGC cell) to participate in the regulation of circadian rhythm, emotion, cognition and other functions. The mechanism of this physiological phenomenon is not fully understood. This article introduce AI technology to make a closed loop experimental system to study this phenomenon. The goal of this paper is to use AI technology to analyze the input light stimulus and human physiological electrical signals, and try to find out the corresponding nonlinear relationship to guide the stimulation of light therapy.

关键词： Artificial intelligence (AI) light health therapy signal processing algorithm

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Estimation of Water Surface Velocity Using FMCW Radar with On-Chip Antenna

Estimation of Water Surface Velocity Using FMCW Radar with O...

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2024 Asia-Pacific Microwave Conference

作者： Chen, Jintao Hung, Wing-Hung Wu, Ke-Li Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9798350363555;9798350363548

This paper introduces a methodology for estimating surface water velocity utilizing Frequency Modulated Continuous Wave (FMCW) millimeter-wave (mmWave) radar technology operating at 60 GHz. The study leverages the IWR6843AOP radar sensor to obtain high-resolution data on water surfaces. By employing 1TX 1RX configuration, this method achieves an extensive beam width without compromising accuracy due to the specially designed algorithm for precise velocity estimation. This non-contact technique serves as an efficient and reliable alternative to conventional methods, offering enhanced safety and accessibility. Experimental results substantiate the efficacy of our approach, demonstrating significant promise for widespread application in environmental monitoring and water management systems.

关键词： Water flow velocity FMCW radar Millimeter-wave radar Surface velocity estimation non-contact measurement signal processing algorithm Environmental monitoring Water resource management

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Safety in e-Scooters: a Machine-Learning Approach for Online Second Passenger Detection

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IFAC-PapersOnLine 2024年第10期58卷 14-21页

作者： Jessica Leoni Mara Tanelli Silvia Carla Strada Sergio Savaresi Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria (DEIB) Milan Italy

Electric scooters are pivotal for urban mobility, yet safety concerns still prevent widespread adoption. Research identifes rider negligence, such as carrying a second passenger, as a major risk. To address this issue, we propose an autonomous system for real-time detection of second passengers. By analyzing vehicle dynamics through minimal sensors and employing an interpretable machine learning approach, our solution ensures accuracy and interpretability. Rigorous testing with diverse users validates its effectiveness, showcasing adaptability to user characteristics and road conditions, proving the potential of this approach to foster safer electric scooter usage.

关键词： Electric scooter risk detection machine-learning signal processing algorithm smart mobility

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