Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962758
Yudai Sasaki, Fumio Eura, Kento Kobayashi, Ryosuke Kondo, Kyohei Tomita, Yu Nishiyama, H. Tsukihara, Naoki Matsumoto, N. Koizumi
In recent years, research has been very active on the development of artificial intelligence, robot technology, and support for proper image acquisition in ultrasound diagnosis. A conventional problem using robot technology is that robots themselves are large and complicated mechanisms. If a robot is large, there is a restriction where it can be used; that is, a certain amount of space is necessary. In consideration of these constraints, in this research, we developed a compact medical robot holding an ultrasound probe that can easily perform at-home diagnosis that compensates for organ movement. When the robot automatically diagnoses organs, it is necessary to scan organs with the ultrasound probe over the same cross-section always aligned with the center of the organ. Based on the present research, in order to compensate for the movement of the phantom with movement of the ultrasound probe in the ultrasound images, the movement of the phantom in the ultrasound images is analyzed. As a method, Kalman filter model with linear Gaussian noise is applied to position observations obtained by template matching, and system noise and observation noise are estimated in object state estimation. We also constructed a steady-state Kalman filter with asymptotic stability using solutions from the Riccati equation. Furthermore, verification experiments were carried out with the model on dataset acquired in previous research, and the position and velocity of the phantom were analyzed.
{"title":"Automatic Diagnosis by Compact Portable Ultrasound Robot: State Estimation of Internal Organs with Steady-State Kalman Filter","authors":"Yudai Sasaki, Fumio Eura, Kento Kobayashi, Ryosuke Kondo, Kyohei Tomita, Yu Nishiyama, H. Tsukihara, Naoki Matsumoto, N. Koizumi","doi":"10.1109/HI-POCT45284.2019.8962758","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962758","url":null,"abstract":"In recent years, research has been very active on the development of artificial intelligence, robot technology, and support for proper image acquisition in ultrasound diagnosis. A conventional problem using robot technology is that robots themselves are large and complicated mechanisms. If a robot is large, there is a restriction where it can be used; that is, a certain amount of space is necessary. In consideration of these constraints, in this research, we developed a compact medical robot holding an ultrasound probe that can easily perform at-home diagnosis that compensates for organ movement. When the robot automatically diagnoses organs, it is necessary to scan organs with the ultrasound probe over the same cross-section always aligned with the center of the organ. Based on the present research, in order to compensate for the movement of the phantom with movement of the ultrasound probe in the ultrasound images, the movement of the phantom in the ultrasound images is analyzed. As a method, Kalman filter model with linear Gaussian noise is applied to position observations obtained by template matching, and system noise and observation noise are estimated in object state estimation. We also constructed a steady-state Kalman filter with asymptotic stability using solutions from the Riccati equation. Furthermore, verification experiments were carried out with the model on dataset acquired in previous research, and the position and velocity of the phantom were analyzed.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839674","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962883
Sakura Sikander, Pradipta Biswas, Pankaj Kulkarni, Bradley Atwood, Sang-Eun Song
We designed a novel tactile display apparatus in order to facilitate medical palpation and early diagnosis of a possibly cancerous tumor for the advancement of early diagnostic procedure and to overcome the limitations of existing bulky systems. This paper introduces our first 3D printed soft prototype nodule. It encloses granular particles to physically simulate a lump under varying stiffness control. To support the design, we performed initial feasibility tests. A force versus displacement graph was plotted to understand the behavior of the nodule. It is observed that, under vacuum pressure, the particles are jammed together resulting in much higher stiffness of the nodule compared to the normal condition where particles inside are not jammed together leading to lesser stiffness.
{"title":"Prototyping and Initial Feasibility Study of Palpation Display Apparatus Using Granular Jamming","authors":"Sakura Sikander, Pradipta Biswas, Pankaj Kulkarni, Bradley Atwood, Sang-Eun Song","doi":"10.1109/HI-POCT45284.2019.8962883","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962883","url":null,"abstract":"We designed a novel tactile display apparatus in order to facilitate medical palpation and early diagnosis of a possibly cancerous tumor for the advancement of early diagnostic procedure and to overcome the limitations of existing bulky systems. This paper introduces our first 3D printed soft prototype nodule. It encloses granular particles to physically simulate a lump under varying stiffness control. To support the design, we performed initial feasibility tests. A force versus displacement graph was plotted to understand the behavior of the nodule. It is observed that, under vacuum pressure, the particles are jammed together resulting in much higher stiffness of the nodule compared to the normal condition where particles inside are not jammed together leading to lesser stiffness.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126059356","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962844
B. Askarian, F. Tabei, Grace Anne Tipton, J. Chong
In this paper, we propose a novel periodontal disease detection method using smartphones, image processing, and machine learning techniques. Periodontal disease is an inflammatory disease known to be the main cause of tooth loss. Here, a CIELAB color space is adopted for feature extraction and the support vector machine (SVM) is applied for distinguishing healthy gum from diseased gum. A gadget is designed to block ambient light and eliminate refraction effect as well. We recruited 30 subjects consisting of 15 gum-diseased and 15 healthy subjects. Experimental results show that our proposed method detects periodontal infection with 94.3% accuracy, 92.6% sensitivity, and 93% specificity, respectively.
{"title":"Smartphone-Based Method for Detecting Periodontal Disease","authors":"B. Askarian, F. Tabei, Grace Anne Tipton, J. Chong","doi":"10.1109/HI-POCT45284.2019.8962844","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962844","url":null,"abstract":"In this paper, we propose a novel periodontal disease detection method using smartphones, image processing, and machine learning techniques. Periodontal disease is an inflammatory disease known to be the main cause of tooth loss. Here, a CIELAB color space is adopted for feature extraction and the support vector machine (SVM) is applied for distinguishing healthy gum from diseased gum. A gadget is designed to block ambient light and eliminate refraction effect as well. We recruited 30 subjects consisting of 15 gum-diseased and 15 healthy subjects. Experimental results show that our proposed method detects periodontal infection with 94.3% accuracy, 92.6% sensitivity, and 93% specificity, respectively.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126912631","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962885
J. LaRocco, Soohong Min, D. Paeng
Medical ultrasound requires the coupling of skin with the transducer. Removing this requirement could make ultrasound stimulation and imaging more accessible and convenient. Synthetic setae pads are a biomimetic material that mimics the structure of a gecko’s foot, allowing it to hold substantial weight. By combining synthetic setae with an ultrasound transducer, the attenuation of existing commercial versions was measured using a 10 MHz ultrasound transducer. It was found that ShearGrip® attached to a smooth plastic surface (70.0% power loss) had notably less attenuation than to skin (86.5% power loss). The material was also examined by mounting it on the side of plastic tank, using a 5 MHz ultrasound transducer and hydrophone. In the case of the tank tests, the presence of moisture on the synthetic setae specimen was found to reduce attenuation by 0.29 dB. Otherwise, the attenuation could be 0.35 dB. However, this large attenuation was due to sample porosity and thickness, causing scattering and energy loss. Currently, work is underway to fully characterize the material. Potential improvements to the material’s acoustic conductance include uniform fiber alignment, reduced porosity, optimized thickness, and the use of lower frequency ultrasound stimulation.
{"title":"Evaluation of synthetic setae pads for dry attachment of an ultrasound transducer","authors":"J. LaRocco, Soohong Min, D. Paeng","doi":"10.1109/HI-POCT45284.2019.8962885","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962885","url":null,"abstract":"Medical ultrasound requires the coupling of skin with the transducer. Removing this requirement could make ultrasound stimulation and imaging more accessible and convenient. Synthetic setae pads are a biomimetic material that mimics the structure of a gecko’s foot, allowing it to hold substantial weight. By combining synthetic setae with an ultrasound transducer, the attenuation of existing commercial versions was measured using a 10 MHz ultrasound transducer. It was found that ShearGrip® attached to a smooth plastic surface (70.0% power loss) had notably less attenuation than to skin (86.5% power loss). The material was also examined by mounting it on the side of plastic tank, using a 5 MHz ultrasound transducer and hydrophone. In the case of the tank tests, the presence of moisture on the synthetic setae specimen was found to reduce attenuation by 0.29 dB. Otherwise, the attenuation could be 0.35 dB. However, this large attenuation was due to sample porosity and thickness, causing scattering and energy loss. Currently, work is underway to fully characterize the material. Potential improvements to the material’s acoustic conductance include uniform fiber alignment, reduced porosity, optimized thickness, and the use of lower frequency ultrasound stimulation.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123942290","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962666
R. Flores, Sahra Afshari, J. Christen
We previously reported a fluorescence-based Point-of-care (PoC) diagnostic for human papillomavirus (HPV). In this work, we present our progress in modifying the system for colorimetric testing. This decreases the number of steps required to complete the assay, simplifies calibration, and decreases the cost of the system. We were able to confirm the system was successfully modified for colorimetric detection using a newly designed 3D printed cartridge and calibration slides.
{"title":"Colorimetric Point-of-Care Human Papillomavirus Diagnostic Reader","authors":"R. Flores, Sahra Afshari, J. Christen","doi":"10.1109/HI-POCT45284.2019.8962666","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962666","url":null,"abstract":"We previously reported a fluorescence-based Point-of-care (PoC) diagnostic for human papillomavirus (HPV). In this work, we present our progress in modifying the system for colorimetric testing. This decreases the number of steps required to complete the assay, simplifies calibration, and decreases the cost of the system. We were able to confirm the system was successfully modified for colorimetric detection using a newly designed 3D printed cartridge and calibration slides.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133707488","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962648
B. Askarian, F. Tabei, Grace Anne Tipton, J. Chong
Keratoconus is a progressive corneal disease which may cause blindness if it is not detected in the early stage. In this paper, we propose a portable, low-cost, and robust keratoconus detection method which is based on smartphone camera images. A gadget has been designed and manufactured using 3-D printing to supplement keratoconus detection. A smartphone camera with the gadget provides more accurate and robust keratoconus detection performance. We adopted the Prewitt operator for edge detection and the support vector machine (SVM) to classify keratoconus eyes from healthy eyes. Experimental results show that the proposed method can detect mild, moderate, advanced, and severe stages of keratoconus with 89% accuracy on average.
{"title":"Novel Keratoconus Detection Method Using Smartphone","authors":"B. Askarian, F. Tabei, Grace Anne Tipton, J. Chong","doi":"10.1109/HI-POCT45284.2019.8962648","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962648","url":null,"abstract":"Keratoconus is a progressive corneal disease which may cause blindness if it is not detected in the early stage. In this paper, we propose a portable, low-cost, and robust keratoconus detection method which is based on smartphone camera images. A gadget has been designed and manufactured using 3-D printing to supplement keratoconus detection. A smartphone camera with the gadget provides more accurate and robust keratoconus detection performance. We adopted the Prewitt operator for edge detection and the support vector machine (SVM) to classify keratoconus eyes from healthy eyes. Experimental results show that the proposed method can detect mild, moderate, advanced, and severe stages of keratoconus with 89% accuracy on average.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131917200","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962686
Md. Khayrul Bashar
Malarial is a mosquito born deadly disease that quickly grows from person to person because of the infectious mosquito bite. Knowing accurately the developing stages of a parasite is critical for accurate drag selection for early recovery. However, limited study were found that dealt with the automated classification of malaria parasite stages. In this study, a supervised method for classifying malaria parasite stages from microscopy images has been proposed. To achieve the target, this method combines color and texture features with the support vector machine (SVM) classifier. Three texture features, namely histogram of oriented pattern (HOG), local binary pattern (LBP), Grey-level Co-occurrence Matrix (GLCM), and four color features, namely local color moments (StatMom) and color histograms (HSV, LAB, and YCrCb), have been considered. An experimental analysis with an unbalanced dataset of 46,978 single-cell thin blood smear images showed promising performances of the color features compared to the texture features. Using SVM classifier, the proposed color-texture feature (YCrCb_HOG) showed the highest classification accuracy (96.9%) on average, which exceeds the performance of a recently published method using HOG_LBP feature with the SVM (87.1%).
{"title":"Improved Classification of Malaria Parasite Stages with Support Vector Machine Using Combined Color and Texture Features","authors":"Md. Khayrul Bashar","doi":"10.1109/HI-POCT45284.2019.8962686","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962686","url":null,"abstract":"Malarial is a mosquito born deadly disease that quickly grows from person to person because of the infectious mosquito bite. Knowing accurately the developing stages of a parasite is critical for accurate drag selection for early recovery. However, limited study were found that dealt with the automated classification of malaria parasite stages. In this study, a supervised method for classifying malaria parasite stages from microscopy images has been proposed. To achieve the target, this method combines color and texture features with the support vector machine (SVM) classifier. Three texture features, namely histogram of oriented pattern (HOG), local binary pattern (LBP), Grey-level Co-occurrence Matrix (GLCM), and four color features, namely local color moments (StatMom) and color histograms (HSV, LAB, and YCrCb), have been considered. An experimental analysis with an unbalanced dataset of 46,978 single-cell thin blood smear images showed promising performances of the color features compared to the texture features. Using SVM classifier, the proposed color-texture feature (YCrCb_HOG) showed the highest classification accuracy (96.9%) on average, which exceeds the performance of a recently published method using HOG_LBP feature with the SVM (87.1%).","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128724720","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 : 2019-11-01DOI: 10.1109/hi-poct45284.2019.8962638
{"title":"HI-POCT 2019 Welcome Message","authors":"","doi":"10.1109/hi-poct45284.2019.8962638","DOIUrl":"https://doi.org/10.1109/hi-poct45284.2019.8962638","url":null,"abstract":"","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120879796","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 : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962886
A. S. Ravindran, Sho Nakagome, D. S. Wickramasuriya, J. Contreras-Vidal, R. Faghih
Recognizing human emotion from heartbeat information alone is a challenging but ongoing research area. Here, we utilize a point process model to characterize heartbeat dynamics and use it to extract instantaneous heart rate variability (HRV) features. These features are then fed into a convolutional neural network (CNN) to characterize different emotional states from small windows. On average, we achieved over 60% classification accuracy and as high as 77% in some subjects. This is comparable to other studies that use a combination of physiological signals as opposed to only HRV measures as done here. Informative features were identified for the different affective states. These findings enable the possibility of augmenting electrocardiogram or photoplethysmogram monitoring wearable devices with automated human emotion recognition capabilities for mental health applications. They also allow for the use of instantaneous estimation of HRV features to be used in combination with models that use other types of physiological signals for instantaneous emotion recognition.
{"title":"Emotion Recognition by Point Process Characterization of Heartbeat Dynamics","authors":"A. S. Ravindran, Sho Nakagome, D. S. Wickramasuriya, J. Contreras-Vidal, R. Faghih","doi":"10.1109/HI-POCT45284.2019.8962886","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962886","url":null,"abstract":"Recognizing human emotion from heartbeat information alone is a challenging but ongoing research area. Here, we utilize a point process model to characterize heartbeat dynamics and use it to extract instantaneous heart rate variability (HRV) features. These features are then fed into a convolutional neural network (CNN) to characterize different emotional states from small windows. On average, we achieved over 60% classification accuracy and as high as 77% in some subjects. This is comparable to other studies that use a combination of physiological signals as opposed to only HRV measures as done here. Informative features were identified for the different affective states. These findings enable the possibility of augmenting electrocardiogram or photoplethysmogram monitoring wearable devices with automated human emotion recognition capabilities for mental health applications. They also allow for the use of instantaneous estimation of HRV features to be used in combination with models that use other types of physiological signals for instantaneous emotion recognition.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121348885","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 : 2019-11-01DOI: 10.1109/hi-poct45284.2019.8962628
{"title":"HI-POCT 2019 TOC","authors":"","doi":"10.1109/hi-poct45284.2019.8962628","DOIUrl":"https://doi.org/10.1109/hi-poct45284.2019.8962628","url":null,"abstract":"","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293200","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：