Pub Date : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827694
Viengnam Douangphachanh, H. Oneyama
It is increasingly common to find many useful sensors on today's smartphones. Beside the use in the smartphones' user interface and features, many researchers and developers have also adopted the sensors for use in numerous applications in several fields and purposes. In this study, a simple model has been formulated to estimate road surface roughness condition from Android smartphone sensor data. The goal is to explore the use of smartphones, as a low cost and easy to implement approach, in the field of road maintenance management and continuous monitoring. The formulation of the model is based on an experiment and frequency domain analysis, in which it has been found that the sensor data, such as 3 axis acceleration and speed, has a linear relationship with road surface roughness condition. In our preliminary simulations on example road network with various settings, we have found that the performance and results of the model are very encouraging.
{"title":"Formulation of a simple model to estimate road surface roughness condition from Android smartphone sensors","authors":"Viengnam Douangphachanh, H. Oneyama","doi":"10.1109/ISSNIP.2014.6827694","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827694","url":null,"abstract":"It is increasingly common to find many useful sensors on today's smartphones. Beside the use in the smartphones' user interface and features, many researchers and developers have also adopted the sensors for use in numerous applications in several fields and purposes. In this study, a simple model has been formulated to estimate road surface roughness condition from Android smartphone sensor data. The goal is to explore the use of smartphones, as a low cost and easy to implement approach, in the field of road maintenance management and continuous monitoring. The formulation of the model is based on an experiment and frequency domain analysis, in which it has been found that the sensor data, such as 3 axis acceleration and speed, has a linear relationship with road surface roughness condition. In our preliminary simulations on example road network with various settings, we have found that the performance and results of the model are very encouraging.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127521446","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827649
V. Sachnev, Hyoung-Joong Kim
In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.
{"title":"Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine","authors":"V. Sachnev, Hyoung-Joong Kim","doi":"10.1109/ISSNIP.2014.6827649","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827649","url":null,"abstract":"In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115206923","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827688
Wu-Ja Lin, Yi-Xiang Huang
In this manuscript, we propose a system which can automatically recognize the oyster racks in an aerial image. As the oyster racks are recognized, the volume of the oysters could thus be estimated and the price can be predicted in advance. Besides that, when a disaster such as a typhoon strikes on the offshore, the loss could also be estimated when the numbers of oyster racks before and after the disaster are available for analysis. These information are useful for the local government in governing the aquaculture affairs. The advantage of the proposed system is to provide a useful tool in analyzing aquaculture information with less man power and time.
{"title":"Automatic recognition of oyster racks in the aerial image","authors":"Wu-Ja Lin, Yi-Xiang Huang","doi":"10.1109/ISSNIP.2014.6827688","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827688","url":null,"abstract":"In this manuscript, we propose a system which can automatically recognize the oyster racks in an aerial image. As the oyster racks are recognized, the volume of the oysters could thus be estimated and the price can be predicted in advance. Besides that, when a disaster such as a typhoon strikes on the offshore, the loss could also be estimated when the numbers of oyster racks before and after the disaster are available for analysis. These information are useful for the local government in governing the aquaculture affairs. The advantage of the proposed system is to provide a useful tool in analyzing aquaculture information with less man power and time.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130722114","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827699
C. Wann, Jia-Yu Shiu
In this paper, a cooperative mobile target estimation approach based on interacting multiple model (IMM) algorithm is presented. We propose a dual-IMM estimator structure to improve the accuracy and robustness of mobile target localization and tracking in wireless sensor networks. Suppose that two sensor systems are affected by different levels of noises, the measured data can be first processed at each individual IMM-based estimator. Each IMM-based estimator then exchanges the local estimates, local model probabilities and model transition probabilities with the other estimator for data sharing and data integration. By updating the associated model probabilities in each of the IMM estimators, the dual structure performs state estimation and attains the objective of data fusion for target tracking. Simulation results show that the overall performance of the dual-IMM estimator is improved. The proposed dual-IMM estimator structure can also be extended to multiple-IMM cases for data fusion, cooperative localization and target tracking.
{"title":"Mobile target tracking and data fusion using dual-interacting multiple model system","authors":"C. Wann, Jia-Yu Shiu","doi":"10.1109/ISSNIP.2014.6827699","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827699","url":null,"abstract":"In this paper, a cooperative mobile target estimation approach based on interacting multiple model (IMM) algorithm is presented. We propose a dual-IMM estimator structure to improve the accuracy and robustness of mobile target localization and tracking in wireless sensor networks. Suppose that two sensor systems are affected by different levels of noises, the measured data can be first processed at each individual IMM-based estimator. Each IMM-based estimator then exchanges the local estimates, local model probabilities and model transition probabilities with the other estimator for data sharing and data integration. By updating the associated model probabilities in each of the IMM estimators, the dual structure performs state estimation and attains the objective of data fusion for target tracking. Simulation results show that the overall performance of the dual-IMM estimator is improved. The proposed dual-IMM estimator structure can also be extended to multiple-IMM cases for data fusion, cooperative localization and target tracking.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131018438","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827594
A. Gogolev, L. Marcenaro
In this article we investigate randomized binary majority consensus in networks with random topologies and noise. Using computer simulations, we show that asynchronous Simple Majority rule can reach ≃ 100% convergence rate being randomized by an update-biased random neighbor selection and a small fraction of errors. Next, we show that such gains are robust towards additive noise and topology randomization.
{"title":"Efficient binary consensus in randomized and noisy environments","authors":"A. Gogolev, L. Marcenaro","doi":"10.1109/ISSNIP.2014.6827594","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827594","url":null,"abstract":"In this article we investigate randomized binary majority consensus in networks with random topologies and noise. Using computer simulations, we show that asynchronous Simple Majority rule can reach ≃ 100% convergence rate being randomized by an update-biased random neighbor selection and a small fraction of errors. Next, we show that such gains are robust towards additive noise and topology randomization.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125327098","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827599
N. Rajakaruna, C. Rathnayake, Kit Yan Chan, I. Murray
Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision.
{"title":"Image deblurring for navigation systems of vision impaired people using sensor fusion data","authors":"N. Rajakaruna, C. Rathnayake, Kit Yan Chan, I. Murray","doi":"10.1109/ISSNIP.2014.6827599","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827599","url":null,"abstract":"Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407072","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827654
Walid M. Ibrahim, N. Abuali, A. Taha, H. Hassanein
Localization plays a substantial role in the future Internet, especially within the context of the Internet of Things (IoT). Increased dependence on devices and sensed data presses for more efficient and accurate localization schemes. In the IoT environment the area covered is large making it impossible to localize all devices and Sensor Nodes (SNs) using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate devices' positions. Simulating localization techniques for wireless sensor networks is required in order to reduce cost and study the difference between localization techniques easily especially if the simulated environment is large. Thus a realistic model is required to simulate the localization process as accurately as possible. Many multi-hop localization techniques use Received Signal Strength Indicator (RSSI) to estimate the distance between SNs. Our interest in this work is to enhance the validation of these schemes prior to deployment. Specifically, we propose the use of a more realistic model for generating RSSI values. The model is based on practical measurements and is validated through extensive simulation.
{"title":"Improving the accuracy of simulation models for localization schemes","authors":"Walid M. Ibrahim, N. Abuali, A. Taha, H. Hassanein","doi":"10.1109/ISSNIP.2014.6827654","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827654","url":null,"abstract":"Localization plays a substantial role in the future Internet, especially within the context of the Internet of Things (IoT). Increased dependence on devices and sensed data presses for more efficient and accurate localization schemes. In the IoT environment the area covered is large making it impossible to localize all devices and Sensor Nodes (SNs) using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate devices' positions. Simulating localization techniques for wireless sensor networks is required in order to reduce cost and study the difference between localization techniques easily especially if the simulated environment is large. Thus a realistic model is required to simulate the localization process as accurately as possible. Many multi-hop localization techniques use Received Signal Strength Indicator (RSSI) to estimate the distance between SNs. Our interest in this work is to enhance the validation of these schemes prior to deployment. Specifically, we propose the use of a more realistic model for generating RSSI values. The model is based on practical measurements and is validated through extensive simulation.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121486225","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827613
Xiaoting Wang, S. Suvorova, T. Vaithianathan, C. Leckie
There is growing interest in using low-cost wearable sensors to model limb movement in applications such as stroke rehabilitation and physiotherapy. This paper presents an algorithm for the detection and classification of arm motion in time series collected by wearable inertial sensors. High level arm trajectory features are obtained from raw sensor data using a sensor orientation tracking algorithm and an arm model. The features are then used in a clustering-based classifier. In the classifier training stage, features are clustered using the k-means algorithm, and a histogram of “key poses” is generated from the clustering as a template for each class. In the recognition stage, new data are segmented and matched to the templates. Experiments on human subjects show that by using trajectory features in the proposed approach, we can achieve higher accuracy than a range of benchmark non-temporal classifiers.
{"title":"Using trajectory features for upper limb action recognition","authors":"Xiaoting Wang, S. Suvorova, T. Vaithianathan, C. Leckie","doi":"10.1109/ISSNIP.2014.6827613","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827613","url":null,"abstract":"There is growing interest in using low-cost wearable sensors to model limb movement in applications such as stroke rehabilitation and physiotherapy. This paper presents an algorithm for the detection and classification of arm motion in time series collected by wearable inertial sensors. High level arm trajectory features are obtained from raw sensor data using a sensor orientation tracking algorithm and an arm model. The features are then used in a clustering-based classifier. In the classifier training stage, features are clustered using the k-means algorithm, and a histogram of “key poses” is generated from the clustering as a template for each class. In the recognition stage, new data are segmented and matched to the templates. Experiments on human subjects show that by using trajectory features in the proposed approach, we can achieve higher accuracy than a range of benchmark non-temporal classifiers.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133022885","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827601
A. Henseleit, J. Stuermer, C. Pohl, Natalie Haustein, F. Sonntag, T. Bley, E. Boschke
Techniques for monitoring cell cultures and fermentation processes not only enable prompt feedback to variations in critical parameters (e.g., media composition and metabolites) but further improve our understanding of the processes themselves. In this context, surface plasmon resonance (SPR) spectroscopy is one of the methods of choice. This technique exploits angle shifting to follow molecular interactions in real-time. Therefore, it allows samples to be characterized without additional molecular labels and time-consuming sample preparation. The immobilization of receptors onto the chip surface is one of the most challenging requirements in SPR. Especially for measurements in crude samples, it is crucial to achieve a sufficient immobilization level and block the remaining sensitive area to prevent nonspecific binding. In this article, we present a SPR-based detection system for human serum albumin (HSA). As HSA is exclusively synthesized in the liver, it can be used to characterize the specific activity of in vitro cultivated human hepatocytes. These can be cultivated in so-called multi-organ-chips, which have been developed by groups at the TU Berlin and Fraunhofer IWS for predictive preclinical substance evaluation.
{"title":"Surface plasmon resonance based detection of human serum albumin as a marker for hepatocytes activity","authors":"A. Henseleit, J. Stuermer, C. Pohl, Natalie Haustein, F. Sonntag, T. Bley, E. Boschke","doi":"10.1109/ISSNIP.2014.6827601","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827601","url":null,"abstract":"Techniques for monitoring cell cultures and fermentation processes not only enable prompt feedback to variations in critical parameters (e.g., media composition and metabolites) but further improve our understanding of the processes themselves. In this context, surface plasmon resonance (SPR) spectroscopy is one of the methods of choice. This technique exploits angle shifting to follow molecular interactions in real-time. Therefore, it allows samples to be characterized without additional molecular labels and time-consuming sample preparation. The immobilization of receptors onto the chip surface is one of the most challenging requirements in SPR. Especially for measurements in crude samples, it is crucial to achieve a sufficient immobilization level and block the remaining sensitive area to prevent nonspecific binding. In this article, we present a SPR-based detection system for human serum albumin (HSA). As HSA is exclusively synthesized in the liver, it can be used to characterize the specific activity of in vitro cultivated human hepatocytes. These can be cultivated in so-called multi-organ-chips, which have been developed by groups at the TU Berlin and Fraunhofer IWS for predictive preclinical substance evaluation.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719240","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827628
C. Ledermann, H. Pauer, H. Woern, M. Seyfried, G. Domann, H. Wolter
In robot assisted minimally invasive surgery, flexible instruments are a highly interesting research topic, as they promise more flexibility and new possibilities for surgical interventions. Shape sensors are needed in order to retrieve information about the geometry and especially the tip position of the instrument. Those shape sensors are usually based on Fiber Bragg Gratings. In contrast to other research groups, which e.g. use nitinol wires as a core for their fibers, we follow the approach of casting the fibers in soft materials. For our latest prototype, a special ORMOCER® material has been used, which is an inorganic-organic hybridpolymer with adjustable properties. The fabrication of the sensor is described in detail. The reproducibility of the wavelength measurements has been validated for several shapes, proving the reasonableness of our approach.
{"title":"Using ORMOCER®s as casting material for a 3D shape sensor based on Fiber Bragg gratings","authors":"C. Ledermann, H. Pauer, H. Woern, M. Seyfried, G. Domann, H. Wolter","doi":"10.1109/ISSNIP.2014.6827628","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827628","url":null,"abstract":"In robot assisted minimally invasive surgery, flexible instruments are a highly interesting research topic, as they promise more flexibility and new possibilities for surgical interventions. Shape sensors are needed in order to retrieve information about the geometry and especially the tip position of the instrument. Those shape sensors are usually based on Fiber Bragg Gratings. In contrast to other research groups, which e.g. use nitinol wires as a core for their fibers, we follow the approach of casting the fibers in soft materials. For our latest prototype, a special ORMOCER® material has been used, which is an inorganic-organic hybridpolymer with adjustable properties. The fabrication of the sensor is described in detail. The reproducibility of the wavelength measurements has been validated for several shapes, proving the reasonableness of our approach.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116470334","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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