Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584843
Guangyin Feng, Ji-Jon Sit
This paper presents a transmitter architecture for wireless power transfer with automatic resonance frequency tracking to maintain high efficiency over wide variations of antenna distance. By injection-locking the source oscillator to the output resonance in a positive feedback loop, the closed-loop transmitter functions as a power oscillator with the oscillation frequency determined by the most dominant resonance in the coupled antennas. We show that this frequency tracking minimizes the change in input impedance presented to the power amplifier (PA), and hence mitigates mismatch that can cause a sharp non-linear drop in PA efficiency. The power oscillator was tested well above and below critical coupling, and maintained PA efficiency above 60 % even when highly over-coupled at 25 % spacing (10mm/40mm) below critical coupling. Compared to an open-loop system, the charging range with efficiency over 50 % is doubled.
{"title":"Injection-Locked Power Oscillator for Resonance Frequency Tracking in Wireless Power Transfer","authors":"Guangyin Feng, Ji-Jon Sit","doi":"10.1109/BIOCAS.2018.8584843","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584843","url":null,"abstract":"This paper presents a transmitter architecture for wireless power transfer with automatic resonance frequency tracking to maintain high efficiency over wide variations of antenna distance. By injection-locking the source oscillator to the output resonance in a positive feedback loop, the closed-loop transmitter functions as a power oscillator with the oscillation frequency determined by the most dominant resonance in the coupled antennas. We show that this frequency tracking minimizes the change in input impedance presented to the power amplifier (PA), and hence mitigates mismatch that can cause a sharp non-linear drop in PA efficiency. The power oscillator was tested well above and below critical coupling, and maintained PA efficiency above 60 % even when highly over-coupled at 25 % spacing (10mm/40mm) below critical coupling. Compared to an open-loop system, the charging range with efficiency over 50 % is doubled.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126259530","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584828
F. Khan, Usman Ashraf, Muhammad Awais Bin Altaf, Wala Saadeh
An electroencephalograph (EEG) based classification processor for the depth of Anesthesia (DoA) during the intraoperative procedure is presented. To enable a DoA to monitor the correct estimation across a range of patients, a novel feature extraction along with machine learning processor is utilized. The decisions are solely based on seven features extracted from EEG along with the EMG signal for motion artifacts rejection. To extract the features efficiently on hardware, a 128-point FFT is proposed that achieves an area reduction and energy/FFT-operation by 39% and 58%, respectively, compared to the conventional. A simple decision tree is used to perform a multiclass DoA classification. The system is synthesized using a 65nm process and experimental verification is done using FPGA based on the subset of patients from the University of Queensland Vital Signs. The proposed patient-specific DoA classification processor achieves a classification accuracy of 79%.
{"title":"A Patient-Specific Machine Learning based EEG Processor for Accurate Estimation of Depth of Anesthesia","authors":"F. Khan, Usman Ashraf, Muhammad Awais Bin Altaf, Wala Saadeh","doi":"10.1109/BIOCAS.2018.8584828","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584828","url":null,"abstract":"An electroencephalograph (EEG) based classification processor for the depth of Anesthesia (DoA) during the intraoperative procedure is presented. To enable a DoA to monitor the correct estimation across a range of patients, a novel feature extraction along with machine learning processor is utilized. The decisions are solely based on seven features extracted from EEG along with the EMG signal for motion artifacts rejection. To extract the features efficiently on hardware, a 128-point FFT is proposed that achieves an area reduction and energy/FFT-operation by 39% and 58%, respectively, compared to the conventional. A simple decision tree is used to perform a multiclass DoA classification. The system is synthesized using a 65nm process and experimental verification is done using FPGA based on the subset of patients from the University of Queensland Vital Signs. The proposed patient-specific DoA classification processor achieves a classification accuracy of 79%.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378249","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584783
Bassem Ibrahim, R. Jafari
Continuous blood pressure (BP) monitoring is essential for diagnosis and management of cardiovascular disorders. Currently, BP is measured using cuff-based methods, which are obtrusive and not suitable for continuous monitoring. Estimation of BP using pulse transit time (PTT) is a prominent method that eliminates the need for a cuff. In this paper, we present a new method to estimate BP based on PTT measurements from an array of 2×2 bio-impedance sensors placed on the wrist, which can be integrated into a small wearable device such as a smart watch for continuous BP monitoring. Diastolic and systolic BP were estimated using AdaBoost regression model based on PTT features extracted from the wrist bio-impedance signals. Data was collected from three participants using our custom bio-impedance sensors. Our method can estimate BP accurately with correlation coefficient, mean absolute error (MAE) and standard deviation (STD) of 0.92, 1.71 and 2.46 mmHg for the diastolic BP and 0.94, 2.57 and 4.35 mmHg for the systolic BP.
{"title":"Continuous Blood Pressure Monitoring using Wrist-worn Bio-impedance Sensors with Wet Electrodes","authors":"Bassem Ibrahim, R. Jafari","doi":"10.1109/BIOCAS.2018.8584783","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584783","url":null,"abstract":"Continuous blood pressure (BP) monitoring is essential for diagnosis and management of cardiovascular disorders. Currently, BP is measured using cuff-based methods, which are obtrusive and not suitable for continuous monitoring. Estimation of BP using pulse transit time (PTT) is a prominent method that eliminates the need for a cuff. In this paper, we present a new method to estimate BP based on PTT measurements from an array of 2×2 bio-impedance sensors placed on the wrist, which can be integrated into a small wearable device such as a smart watch for continuous BP monitoring. Diastolic and systolic BP were estimated using AdaBoost regression model based on PTT features extracted from the wrist bio-impedance signals. Data was collected from three participants using our custom bio-impedance sensors. Our method can estimate BP accurately with correlation coefficient, mean absolute error (MAE) and standard deviation (STD) of 0.92, 1.71 and 2.46 mmHg for the diastolic BP and 0.94, 2.57 and 4.35 mmHg for the systolic BP.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"80 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890058","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584833
M. Douthwaite, P. Georgiou
This work presents a circuit for asynchronous, automatic biasing of a CMOS ISFET array for wearable electrochemical measurement systems. The circuit is integrated into a temperature compensated pH-to-frequency converter utilising the floating gates of an ISFET array. The work represents the first effort to address the issue of variable bias points in integrated electrochemical sensors for wearable applications, and is a low power, low transistor solution without an output voltage ripple. Designed in a 0.35flm CMOS technology, the system achieves a low power consumption of 29.72µW with a typical settling time of 0.7ms.
{"title":"An Asynchronous Auto-biasing Circuit for Wearable Electrochemical Sensors","authors":"M. Douthwaite, P. Georgiou","doi":"10.1109/BIOCAS.2018.8584833","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584833","url":null,"abstract":"This work presents a circuit for asynchronous, automatic biasing of a CMOS ISFET array for wearable electrochemical measurement systems. The circuit is integrated into a temperature compensated pH-to-frequency converter utilising the floating gates of an ISFET array. The work represents the first effort to address the issue of variable bias points in integrated electrochemical sensors for wearable applications, and is a low power, low transistor solution without an output voltage ripple. Designed in a 0.35flm CMOS technology, the system achieves a low power consumption of 29.72µW with a typical settling time of 0.7ms.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633202","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584761
Federico Mazza, Yan Liu, N. Donaldson, T. Constandinou
Recent developments in the design of active implantable devices have achieved significant advances, for example, an increased number of recording channels, but too often practical clinical applications are restricted by device longevity. It is important however to complement efforts for increased functionality with translational work to develop implant technologies that are safe and reliable to be hosted inside the human body over long periods of time. This paper first examines techniques currently used to evaluate micro-package hermeticity and key challenges, highlighting the need for new, in situ instrumentation that can monitor the encapsulation status over time. Two novel circuits are then proposed to tackle the specific issue of moisture penetration inside a sub-mm, silicon-based package. They both share the use of metal tracks on the different layers of the CMOS stack to measure changes in impedance caused by moisture present in leak cracks or diffused into the oxide layers.
{"title":"Integrated Devices for Micro-Package Integrity Monitoring in mm-Scale Neural Implants","authors":"Federico Mazza, Yan Liu, N. Donaldson, T. Constandinou","doi":"10.1109/BIOCAS.2018.8584761","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584761","url":null,"abstract":"Recent developments in the design of active implantable devices have achieved significant advances, for example, an increased number of recording channels, but too often practical clinical applications are restricted by device longevity. It is important however to complement efforts for increased functionality with translational work to develop implant technologies that are safe and reliable to be hosted inside the human body over long periods of time. This paper first examines techniques currently used to evaluate micro-package hermeticity and key challenges, highlighting the need for new, in situ instrumentation that can monitor the encapsulation status over time. Two novel circuits are then proposed to tackle the specific issue of moisture penetration inside a sub-mm, silicon-based package. They both share the use of metal tracks on the different layers of the CMOS stack to measure changes in impedance caused by moisture present in leak cracks or diffused into the oxide layers.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129900019","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584779
Y. Arimi, Y. Kimura, Toshiki Wakamori, Hiroo Yamamoto, S. Mizuno, T. Iwata, Kazuhiro Takahashi, K. Sawada
In order to observe the pH distribution of biological cells, we propose a pH image sensor with a new structure enabling high density and high sensitivity imaging. In the new proposed pH image sensor we add the charge accumulation circuit to the conventional 2-Tr pixel structure. In the charge accumulation operation, the circuit amplifies a signal by repeatedly transferring the signal charge from the pixel to the capacitor and thus it is expected that the pH resolution can be improved from the viewpoint of Signal-Noise Ratio (SNR). Since the charge accumulation circuit is designed with three transistors and one capacitor and is arranged outside the pixel array, a new pH image sensor is designed without large increase in area. Image sensors with charge accumulation circuits were designed and fabricated. We evaluate the fabricated pH image sensors, observe sensor gain increased by the charge accumulation circuit and realized the performance that enabled pH imaging. From the measurement result, the input-referred sensor noise is reduced by 82% compared with the conventional 2- Tr pixel structure sensor. Therefore, it is possible to realize pH resolution less than 0.02pH.
{"title":"High pH Resolution Extended Gate Type pH Image Sensors with the Charge Accumulation Circuit","authors":"Y. Arimi, Y. Kimura, Toshiki Wakamori, Hiroo Yamamoto, S. Mizuno, T. Iwata, Kazuhiro Takahashi, K. Sawada","doi":"10.1109/BIOCAS.2018.8584779","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584779","url":null,"abstract":"In order to observe the pH distribution of biological cells, we propose a pH image sensor with a new structure enabling high density and high sensitivity imaging. In the new proposed pH image sensor we add the charge accumulation circuit to the conventional 2-Tr pixel structure. In the charge accumulation operation, the circuit amplifies a signal by repeatedly transferring the signal charge from the pixel to the capacitor and thus it is expected that the pH resolution can be improved from the viewpoint of Signal-Noise Ratio (SNR). Since the charge accumulation circuit is designed with three transistors and one capacitor and is arranged outside the pixel array, a new pH image sensor is designed without large increase in area. Image sensors with charge accumulation circuits were designed and fabricated. We evaluate the fabricated pH image sensors, observe sensor gain increased by the charge accumulation circuit and realized the performance that enabled pH imaging. From the measurement result, the input-referred sensor noise is reduced by 82% compared with the conventional 2- Tr pixel structure sensor. Therefore, it is possible to realize pH resolution less than 0.02pH.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122277885","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584838
N. Gurel, Donald Ward, Frank L. Hammond, O. Inan
This live demonstration presents a wearable system for neuro-vascular health assessment. The system is comprised of three key subcomponents: 1) a soft, fluid-driven thermal modulation pad, 2) a portable case containing electrical and mechanical hardware, and 3) off-board biosignal processing and power units. The soft thermal modulation pad (the only component that interfaces with the user) contains fluid channels, embedded temperature sensors, and a flexible protoboard, encased in graphite-based silicone. The pad induces fluid-based heating or cooling of the hand, and is connected to the portable case through inlets and outlets. The portable case contains electrical and mechanical actuation (temperature modulation, fluid flow), sensing, and control circuitry, none of which is in contact with the user. Components include Peltier tiles, temperature and flow rate sensors, fluid pump, reservoir, control circuitry, heat sinks, cooling fans, and a microcontroller. All components in the case are enclosed with a laser-cut acrylic sheet to shield them from outside world, except for the fluid reservoir (to be filled with water before use) and toggle switches. Separate from the portable case and the pad are a data acquisition system, photoplethysmography (PPG) sensor to be worn on the hand, a laptop for visualization of the data, and a power supply. To enable closed-loop temperature control, the current and voltage flowing through the Peltier tiles are controlled with a custom algorithm implemented on the microcontroller. Biosignals involved with thermoregulation are collected. PPG measures the changes in blood volume pulse at the collected location; the amplitude of the PPG signal reflects the change in dilation or constriction of the vasculature [1].
{"title":"Live Demonstration: A Soft Thermal Modulation System with Embedded Fluid Channels for Neuro-Vascular Assessment","authors":"N. Gurel, Donald Ward, Frank L. Hammond, O. Inan","doi":"10.1109/BIOCAS.2018.8584838","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584838","url":null,"abstract":"This live demonstration presents a wearable system for neuro-vascular health assessment. The system is comprised of three key subcomponents: 1) a soft, fluid-driven thermal modulation pad, 2) a portable case containing electrical and mechanical hardware, and 3) off-board biosignal processing and power units. The soft thermal modulation pad (the only component that interfaces with the user) contains fluid channels, embedded temperature sensors, and a flexible protoboard, encased in graphite-based silicone. The pad induces fluid-based heating or cooling of the hand, and is connected to the portable case through inlets and outlets. The portable case contains electrical and mechanical actuation (temperature modulation, fluid flow), sensing, and control circuitry, none of which is in contact with the user. Components include Peltier tiles, temperature and flow rate sensors, fluid pump, reservoir, control circuitry, heat sinks, cooling fans, and a microcontroller. All components in the case are enclosed with a laser-cut acrylic sheet to shield them from outside world, except for the fluid reservoir (to be filled with water before use) and toggle switches. Separate from the portable case and the pad are a data acquisition system, photoplethysmography (PPG) sensor to be worn on the hand, a laptop for visualization of the data, and a power supply. To enable closed-loop temperature control, the current and voltage flowing through the Peltier tiles are controlled with a custom algorithm implemented on the microcontroller. Biosignals involved with thermoregulation are collected. PPG measures the changes in blood volume pulse at the collected location; the amplitude of the PPG signal reflects the change in dilation or constriction of the vasculature [1].","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"85 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987832","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584757
Cynthia R. Steinhardt, Joseph L. Betthauser, Christopher L. Hunt, N. Thakor
Non-invasive recording of EMG signals from the arm of a typical subject or amputee has been popularized in control of a variety of devices, including upper limb prostheses. One of the most difficult challenges of using external recording devices, such as the Myo Armband, is the need to retrain a movement classifier due to the shift in positions and electrode location around the arm. Electrode shift causes distortion of the features to be extracted for classification and makes previous training unusable. For amputees, this means retraining movement classifiers several times per day. In this experiment, the Myo Armband is used to test the ability to predict the degree of electrode shift from the electrode sites used to originally train a classifier in order to correct by the detected shift and continue to use the same classifer, instead of training a new one. The Myo Armband was rotated around the arm of subjects with intact limbs as they performed six commonly used movements. The mean absolute value of each electrode was used to characterize the response at each electrode site. Shifts in orientation between one position and a new position were identified by minimizing the mean-squared error of their characteristic movement profiles. The correct shift was identified across subjects using only 0.25 s of data with over 90% accuracy using the “open” or “wrist supinate” grips. New movements at a shifted location were classified using the feature vectors of a previously collected training set and accounting for the shift; classification error averaged 95.7 ± 0.4%, indicating a possibility for real-time correction of electrode shift error.
{"title":"Registration of EMG Electrodes to Reduce Classification Errors due to Electrode Shift","authors":"Cynthia R. Steinhardt, Joseph L. Betthauser, Christopher L. Hunt, N. Thakor","doi":"10.1109/BIOCAS.2018.8584757","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584757","url":null,"abstract":"Non-invasive recording of EMG signals from the arm of a typical subject or amputee has been popularized in control of a variety of devices, including upper limb prostheses. One of the most difficult challenges of using external recording devices, such as the Myo Armband, is the need to retrain a movement classifier due to the shift in positions and electrode location around the arm. Electrode shift causes distortion of the features to be extracted for classification and makes previous training unusable. For amputees, this means retraining movement classifiers several times per day. In this experiment, the Myo Armband is used to test the ability to predict the degree of electrode shift from the electrode sites used to originally train a classifier in order to correct by the detected shift and continue to use the same classifer, instead of training a new one. The Myo Armband was rotated around the arm of subjects with intact limbs as they performed six commonly used movements. The mean absolute value of each electrode was used to characterize the response at each electrode site. Shifts in orientation between one position and a new position were identified by minimizing the mean-squared error of their characteristic movement profiles. The correct shift was identified across subjects using only 0.25 s of data with over 90% accuracy using the “open” or “wrist supinate” grips. New movements at a shifted location were classified using the feature vectors of a previously collected training set and accounting for the shift; classification error averaged 95.7 ± 0.4%, indicating a possibility for real-time correction of electrode shift error.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785565","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584688
M. N. Sahadat, Nordine Sebkhi, Fanpeng Kong, Maysam Ghovanloo
People with motor disabilities affecting their four limbs (e.g. tetraplegia, ALS) can use their remaining abilities such as tongue, head, and eye motion to interact with devices, such as PC, smartphone, and wheelchair. Most of the existing assistive technologies (AT) rely on a single remaining ability, which is typically insufficient when performing complex computer tasks such as “drag and drop”, typing long sentences, or selecting multiple items. In this work, a multimodal AT is presented to leverage both tongue gestures and head motion, simultaneously, to interact with target devices at latency and accuracy of 10 ms and 95.9%, respectively, measured among 15 able-bodied participants. A wearable headset transmits commands wirelessly via Bluetooth Low Energy (BLE), utilizing a human interface device (HID) protocol for seamless interfacing with various applications running on PCs and smartphones without requiring a custom driver.
{"title":"Standalone Assistive System to Employ Multiple Remaining Abilities in People with Tetraplegia","authors":"M. N. Sahadat, Nordine Sebkhi, Fanpeng Kong, Maysam Ghovanloo","doi":"10.1109/BIOCAS.2018.8584688","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584688","url":null,"abstract":"People with motor disabilities affecting their four limbs (e.g. tetraplegia, ALS) can use their remaining abilities such as tongue, head, and eye motion to interact with devices, such as PC, smartphone, and wheelchair. Most of the existing assistive technologies (AT) rely on a single remaining ability, which is typically insufficient when performing complex computer tasks such as “drag and drop”, typing long sentences, or selecting multiple items. In this work, a multimodal AT is presented to leverage both tongue gestures and head motion, simultaneously, to interact with target devices at latency and accuracy of 10 ms and 95.9%, respectively, measured among 15 able-bodied participants. A wearable headset transmits commands wirelessly via Bluetooth Low Energy (BLE), utilizing a human interface device (HID) protocol for seamless interfacing with various applications running on PCs and smartphones without requiring a custom driver.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130325122","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}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584773
Parisa Forouzannezhad, Alireza Abbaspour, M. Cabrerizo, M. Adjouadi
Alzheimer‘s disease (AD) is a neurodegenerative disease which is progressive and can be described by amyloid deposition, and neuronal atrophy. In this study, a support vector machine (SVM) approach with radial basis function (RBF) has been proposed in order to detect the Alzheimer's disease in its early stage using multiple modalities, including positron emission tomography (PET), magnetic resonance imaging (MRI), and standard neuropsychological test scores. A total number of 896 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were considered in this study. The proposed approach is able to classify cognitively normal control (CN) group from early mild cognitive impairment (EMCI) with an accuracy of 81.1%. In addition, the accuracy of 91.9% for CN vs. late mild cognitive impairment and accuracy of 96.2% for CN vs. AD classifications have been achieved through the proposed model.
{"title":"Early Diagnosis of Mild Cognitive Impairment Using Random Forest Feature Selection","authors":"Parisa Forouzannezhad, Alireza Abbaspour, M. Cabrerizo, M. Adjouadi","doi":"10.1109/BIOCAS.2018.8584773","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584773","url":null,"abstract":"Alzheimer‘s disease (AD) is a neurodegenerative disease which is progressive and can be described by amyloid deposition, and neuronal atrophy. In this study, a support vector machine (SVM) approach with radial basis function (RBF) has been proposed in order to detect the Alzheimer's disease in its early stage using multiple modalities, including positron emission tomography (PET), magnetic resonance imaging (MRI), and standard neuropsychological test scores. A total number of 896 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were considered in this study. The proposed approach is able to classify cognitively normal control (CN) group from early mild cognitive impairment (EMCI) with an accuracy of 81.1%. In addition, the accuracy of 91.9% for CN vs. late mild cognitive impairment and accuracy of 96.2% for CN vs. AD classifications have been achieved through the proposed model.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116823832","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}
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