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Raspberry Pi driven flow-injection system for electrochemical continuous monitoring platforms 树莓派驱动的电化学连续监测平台流动注射系统
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325134
Bruno Donato, Francesca Stradolini, Abuduwaili Tuoheti, F. Angiolini, D. Demarchi, G. Micheli, S. Carrara
{"title":"Raspberry Pi driven flow-injection system for electrochemical continuous monitoring platforms","authors":"Bruno Donato, Francesca Stradolini, Abuduwaili Tuoheti, F. Angiolini, D. Demarchi, G. Micheli, S. Carrara","doi":"10.1109/BIOCAS.2017.8325134","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325134","url":null,"abstract":"The degree of interest in bio-sensing platforms brings to the forefront a corresponding need for effective testing of their capabilities. This necessity is even more crucial when examining the properties of a sensor for continuous monitoring of a concentration trend in time, before in vivo implementations. Moreover, in the framework of personalised medical practices, it is imperative to introduce a robust way to represent and parametrise the highly variable responses of human metabolism. The aim of this paper is to propose a novel solution for the design of an automatic flow-injection environment that can assess the performance of systems for continuous monitoring. The setup is also approved for successfully reproducing a paracetamol concentration trend in buffer solution.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133362219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Emotion recognition based on low-cost in-ear EEG 基于低成本耳内脑电图的情绪识别
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325198
Gang Li, Zhe Zhang, Guoxing Wang
{"title":"Emotion recognition based on low-cost in-ear EEG","authors":"Gang Li, Zhe Zhang, Guoxing Wang","doi":"10.1109/BIOCAS.2017.8325198","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325198","url":null,"abstract":"In this paper, we propose a low-cost in-ear EEG device which is implemented by refitting a commercial scalp EEG device, in order to recognize emotion in a manner that is simple, inexpensive, and popular in style. EEG signals of twelve subjects were recorded under three emotion conditions that were induced by music and video materials. By using wavelet packet transformation (WPT), two frequency features and a nonlinear feature are extracted to create a three-dimensional feature vector for each labeled EEG segment. These feature vectors are input into a support vector machine (SVM) classifier for automatic emotion recognition. The SVM classifier achieved a best 94.1% cross-validation accuracy for positive (high valence, HV) and negative (low valence, LV) two-class emotion recognition. However, the accuracy for excited (high valence and high arousal, HVHA), relaxed (high valence and low arousal, HVLA) and negative (LV) multi-class emotion classification was 58.8%. The experimental results show that the proposed low-cost in-ear EEG has outstanding accuracy for valence recognition, but poor accuracy for arousal recognition.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131951278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
EXPO-AGRI: Smart automatic greenhouse control EXPO-AGRI:智能自动温室控制
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325181
A. Castellini, A. Farinelli, G. Minuto, D. Quaglia, Iseo Secco, F. Tinivella
{"title":"EXPO-AGRI: Smart automatic greenhouse control","authors":"A. Castellini, A. Farinelli, G. Minuto, D. Quaglia, Iseo Secco, F. Tinivella","doi":"10.1109/BIOCAS.2017.8325181","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325181","url":null,"abstract":"Predicting and controlling plant behavior in controlled environments is a growing requirement in precision agriculture. In this context sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. In this paper we present a general architecture for automatic greenhouse control. In particular, we focus on a preliminary model for predicting the risk of new infections of downy mildew of basil (Peronospora belbahrii) on sweet basil. The architecture has three main elements of innovation: new kinds of sensors are used to extract information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122371167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An implant for wireless in situ measurement of lip pressure with 12 sensors 一种带12个传感器的唇压无线原位测量植入物
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325203
J. Becker, David Pellhammer, Patrick Preisner, Julia C. Gloggler, B. Lapatki, M. Ortmanns
{"title":"An implant for wireless in situ measurement of lip pressure with 12 sensors","authors":"J. Becker, David Pellhammer, Patrick Preisner, Julia C. Gloggler, B. Lapatki, M. Ortmanns","doi":"10.1109/BIOCAS.2017.8325203","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325203","url":null,"abstract":"This paper reports on the wireless measurement of lip pressure against the teeth. A prior version with a maximum of 4 wired sensors is significantly improved. A flexible printed circuit board allows for the use of two sensors per tooth and up to 12 sensors matching to the individual patient's dental arch. Wireless power supply and data transmission abandons any disturbing influences to the natural posture of the mandible. The needed high resolution is accomplished by the use of modified barometric pressure sensors, which are able to measure forces with direct contact between the lips and the piezo-resistive membrane. The implant can operate for hours on a coin-battery and transmit data to any tablet-or personal-computer in the range up to 10m from the closed mouth.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Internet of Things as a means to improve agricultural sustainability 物联网作为提高农业可持续性的手段
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325182
G. Colucci, Mattia Poletti, R. Stefanelli, D. Trinchero
{"title":"Internet of Things as a means to improve agricultural sustainability","authors":"G. Colucci, Mattia Poletti, R. Stefanelli, D. Trinchero","doi":"10.1109/BIOCAS.2017.8325182","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325182","url":null,"abstract":"The paper presents a technological platform that favours the introduction of sustainable processes in agriculture. The platform is based on an extensive use of Wireless Sensor Networks, where low power nodes are combined with multimedia devices to integrate meteorological measurements with high definition pictures. Two different solutions are discussed: a short range, multi hop architecture and a long range, single hop variation. Experimental results demonstrate the performance of the proposed solutions, which appears particularly suitable to continuously and pervasively monitor vineyards, favouring a measurable reduction in the use of pesticides.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127910067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
An implementation of motion artifacts elimination for PPG signal processing based on recursive least squares adaptive filter 基于递归最小二乘自适应滤波器的运动伪影消除PPG信号处理实现
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325141
Chih-Chin Wu, I-Wei Chen, W. Fang
{"title":"An implementation of motion artifacts elimination for PPG signal processing based on recursive least squares adaptive filter","authors":"Chih-Chin Wu, I-Wei Chen, W. Fang","doi":"10.1109/BIOCAS.2017.8325141","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325141","url":null,"abstract":"In Photoplethysmographic (PPG) signals analysis, the accuracy and stability are highly affected by Motion Artifacts (MAs) disturbances. In this paper, we adopt an adaptive and efficient approach based on the developed DC Remover method and Recursive Least Squares (RLS) adaptive filter for reducing MAs from PPG signals in real time. The experimental results of this work show a high correlation coefficient between Electrocardiography (ECG)-derived heart rate and PPG-derived heart rate, which is higher than 0.8504 of the R value, a high agreement by Bland-Altman analysis in the limits of agreement represent the 95% confidence interval and the standard deviation is 3.81 BPM (Beats Per Minutes). An overall PPG signal with higher signal quality is obtained. Further, the precision of heart rate calculated by PPG is improved.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116098184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Impedance spectroscopy systems: Review and an all-digital adaptive IIR filtering approach 阻抗谱系统:综述和全数字自适应IIR滤波方法
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325148
N. Ivanisevic, S. Rodriguez, A. Rusu
{"title":"Impedance spectroscopy systems: Review and an all-digital adaptive IIR filtering approach","authors":"N. Ivanisevic, S. Rodriguez, A. Rusu","doi":"10.1109/BIOCAS.2017.8325148","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325148","url":null,"abstract":"Impedance spectroscopy is a low-cost sensing technique that is generating considerable interest in wearable and implantable biomedical applications since it can be efficiently integrated on a single microchip. In this paper, the fundamental characteristics of the most well-known system architectures are presented, and a more robust and hardware-efficient solution is proposed. An all-digital implementation based on adaptive filtering is used for identifying the impedance parameters of a sample-under-test. The coefficients of an infinite-impulse-response (IIR) filter are tuned by an adaptive algorithm based on pseudo-linear regression and output-error formulation. A three-level pseudorandom noise generator with a concave power spectral density is employed without deteriorating the nominal performance. Proof-of-concept has been verified with behavioral simulations.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116714788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Neural spikes digital detector/sorting on FPGA 基于FPGA的神经尖峰数字检测器/排序
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325077
E. Vallicelli, M. Matteis, A. Baschirotto, Michael Rescati, Marco Reato, M. Maschietto, S. Vassanelli, D. Guarrera, G. Collazuol, R. Zeiter
{"title":"Neural spikes digital detector/sorting on FPGA","authors":"E. Vallicelli, M. Matteis, A. Baschirotto, Michael Rescati, Marco Reato, M. Maschietto, S. Vassanelli, D. Guarrera, G. Collazuol, R. Zeiter","doi":"10.1109/BIOCAS.2017.8325077","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325077","url":null,"abstract":"This paper presents the results of a multidisciplinary experiment where the electrical activity of a rat hippocampus cultured neurons population has been detected and mapped by an advanced FPGA spike-sorting algorithm. Neurons are growth over a silicon chip that is thus capacitively coupled with neuronal cells. Due to noise power coming from bio-silicon interface and analog electronics signal processing, the Action Potentials detection intrinsically needs advanced noise rejection algorithms which are often software/off-line implemented. This approach disables instantaneous detection of neural spikes and cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA system able to separate relevant neuronal cells potentials from noise. The FPGA output signals provide real time spatial mapping of biosensor electrical activity, noise and synchronous neural network activity.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115477667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A sub-GHz UWB data transmitter with enhanced output amplitude for implantable bioelectronics 一种用于植入式生物电子学的具有增强输出振幅的sub-GHz超宽带数据发射器
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325113
X. Tong, Jie Li
{"title":"A sub-GHz UWB data transmitter with enhanced output amplitude for implantable bioelectronics","authors":"X. Tong, Jie Li","doi":"10.1109/BIOCAS.2017.8325113","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325113","url":null,"abstract":"An all-digital ultra-wideband (UWB) transmitter is proposed for wireless data transmission in bioelectronics. Edge combination method is utilized to generate a Gaussian-shape output pulse. Thanks to the proposed pulse-boosting technique that used during pulse generation, the peak-to-peak output amplitude of this transmitter can be up to 160 mV, under 1 V power supply. The output power spectral density (PSD) is mainly concentrated in the frequency range of 0–960 MHz, which can satisfy the Federal Communications Commission (FCC) mask requirements. Manchester encoder and OOK modulation circuit are closely cooperated to reduce the bit error rate during data transmission. Designed with 0.18 μm CMOS, this proposed UWB transmitter has an active area of 110.9 μm × 73.6 μm. The energy consumption of this transmitter is 0.65 pJ/bit, with the maximum pulse repetition frequency of 200 MHz. The miniaturized size and low energy consumption make this transmitter very competitive, compared with other state-of-the-art works.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A 1 V 10 bit 25 kS/s VCO-based ADC for implantable neural recording 用于植入式神经记录的1 V 10位25 kS/s vco型ADC
2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2017-10-01 DOI: 10.1109/BIOCAS.2017.8325226
X. Tong, Jie Wang
{"title":"A 1 V 10 bit 25 kS/s VCO-based ADC for implantable neural recording","authors":"X. Tong, Jie Wang","doi":"10.1109/BIOCAS.2017.8325226","DOIUrl":"https://doi.org/10.1109/BIOCAS.2017.8325226","url":null,"abstract":"A differential-input A/D converter (ADC) based on voltage-controlled oscillator (VCO) is proposed for implantable bioelectronics. Two single-ended VCO-based ADCs are matched and combined to support differential input, hence suppressing the common-mode interference and even harmonics distortion. A 10-bit binary subtraction circuit, which is utilized to process the digital output of two matched ADCs, generates the final digital output. The subtraction circuit is realized with Domino logic gates for further reduction in power consumption and chip area. Designed with 0.18 μm CMOS process, the 10 bit 25 kS/s VCO-based ADC can operate under 1 V power supply. The active area of this ADC is 270 μm × 100 μm, much smaller than a successive-approximation-register (SAR) ADC with similar performance.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126020553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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