Dd
D.A.E. de Gruijl
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2 records found
1
Much like wearable devices today, ingestible devices have emerged as a promising platform for continuous health monitoring, and potentially even intervention. Recent research has demonstrated the feasibility of ingestible devices with a retention mechanism, enabling them to remain in the stomach for weeks. Equipping these devices with sensors capable of measuring complex biomarkers, would open up an entirely new era of continuous health monitoring.
This research focuses on the design of a gas sensor array for a retentive ingestible device, targeting the measurement of volatile organic compounds in the gastrointestinal tract. The research specifically addresses the outpatient treatment of alcoholism, in collaboration with the emergency medicine department at Brigham and Women's Hospital, Harvard Medical School. Current treatment relies on manual registration of drinking behavior, and the incorporation of an ingestible device that is able to continuously monitor drinking behavior enables more accurate behavioral assessment, and more targeted support and treatment.
Given the novelty of this approach, a sensor array capable of assessing ethanol concentrations in the air was gradually subjected to more complex tasks. Furthermore, a key aspect of the research was the design of an artificial gastric environment, that replicates the conditions and challenges that the sensor array would encounter within the human body. This experimental environment played a crucial role by providing a realistic testing platform without the need for a fully functional ingestible device, which can be incredibly challenging and resource-intensive to manufacture.
To ensure functionality in humid conditions, the sensors are encapsulated using parylene C and polycaprolactone (PCL) - biocompatible materials commonly used in the design of biomedical devices. The impact of these encapsulation methods on the sensors is thoroughly assessed, to determine their viability for in-vivo applications.
The findings of this study reveal that employing a convolutional neural network can enable the accurate measurement of ethanol in air, using off-the-shelf air quality sensors and algorithms with low computational complexity. It is worth highlighting that the neural network is capable of performing inference directly on the ingestible device. Furthermore, initial results show that a combination of parylene C and PCL, achieved through dip-coating in a PCL-dichloromethane solvent, yields a sensor capable of reliably distinguishing ethanol percentages from 4 to 11 volume percentages, while continually submerged in a self-designed artificial gastric environment.
Overall, this research contributes to the advancement of ingestible sensors and their potential for continuous health monitoring, with a use case in alcoholism treatment. The outcomes show the potential to produce a sensor array encapsulated in biocompatible materials, with a data-driven sensor fusion algorithm that is deployable on-device, which brings us closer to practical in-vivo applications. Additionally, the design and utilization of an artificial gastric environment establishes a solid foundation for future studies and the data generation that is vital for this technology. ...
This research focuses on the design of a gas sensor array for a retentive ingestible device, targeting the measurement of volatile organic compounds in the gastrointestinal tract. The research specifically addresses the outpatient treatment of alcoholism, in collaboration with the emergency medicine department at Brigham and Women's Hospital, Harvard Medical School. Current treatment relies on manual registration of drinking behavior, and the incorporation of an ingestible device that is able to continuously monitor drinking behavior enables more accurate behavioral assessment, and more targeted support and treatment.
Given the novelty of this approach, a sensor array capable of assessing ethanol concentrations in the air was gradually subjected to more complex tasks. Furthermore, a key aspect of the research was the design of an artificial gastric environment, that replicates the conditions and challenges that the sensor array would encounter within the human body. This experimental environment played a crucial role by providing a realistic testing platform without the need for a fully functional ingestible device, which can be incredibly challenging and resource-intensive to manufacture.
To ensure functionality in humid conditions, the sensors are encapsulated using parylene C and polycaprolactone (PCL) - biocompatible materials commonly used in the design of biomedical devices. The impact of these encapsulation methods on the sensors is thoroughly assessed, to determine their viability for in-vivo applications.
The findings of this study reveal that employing a convolutional neural network can enable the accurate measurement of ethanol in air, using off-the-shelf air quality sensors and algorithms with low computational complexity. It is worth highlighting that the neural network is capable of performing inference directly on the ingestible device. Furthermore, initial results show that a combination of parylene C and PCL, achieved through dip-coating in a PCL-dichloromethane solvent, yields a sensor capable of reliably distinguishing ethanol percentages from 4 to 11 volume percentages, while continually submerged in a self-designed artificial gastric environment.
Overall, this research contributes to the advancement of ingestible sensors and their potential for continuous health monitoring, with a use case in alcoholism treatment. The outcomes show the potential to produce a sensor array encapsulated in biocompatible materials, with a data-driven sensor fusion algorithm that is deployable on-device, which brings us closer to practical in-vivo applications. Additionally, the design and utilization of an artificial gastric environment establishes a solid foundation for future studies and the data generation that is vital for this technology. ...
Much like wearable devices today, ingestible devices have emerged as a promising platform for continuous health monitoring, and potentially even intervention. Recent research has demonstrated the feasibility of ingestible devices with a retention mechanism, enabling them to remain in the stomach for weeks. Equipping these devices with sensors capable of measuring complex biomarkers, would open up an entirely new era of continuous health monitoring.
This research focuses on the design of a gas sensor array for a retentive ingestible device, targeting the measurement of volatile organic compounds in the gastrointestinal tract. The research specifically addresses the outpatient treatment of alcoholism, in collaboration with the emergency medicine department at Brigham and Women's Hospital, Harvard Medical School. Current treatment relies on manual registration of drinking behavior, and the incorporation of an ingestible device that is able to continuously monitor drinking behavior enables more accurate behavioral assessment, and more targeted support and treatment.
Given the novelty of this approach, a sensor array capable of assessing ethanol concentrations in the air was gradually subjected to more complex tasks. Furthermore, a key aspect of the research was the design of an artificial gastric environment, that replicates the conditions and challenges that the sensor array would encounter within the human body. This experimental environment played a crucial role by providing a realistic testing platform without the need for a fully functional ingestible device, which can be incredibly challenging and resource-intensive to manufacture.
To ensure functionality in humid conditions, the sensors are encapsulated using parylene C and polycaprolactone (PCL) - biocompatible materials commonly used in the design of biomedical devices. The impact of these encapsulation methods on the sensors is thoroughly assessed, to determine their viability for in-vivo applications.
The findings of this study reveal that employing a convolutional neural network can enable the accurate measurement of ethanol in air, using off-the-shelf air quality sensors and algorithms with low computational complexity. It is worth highlighting that the neural network is capable of performing inference directly on the ingestible device. Furthermore, initial results show that a combination of parylene C and PCL, achieved through dip-coating in a PCL-dichloromethane solvent, yields a sensor capable of reliably distinguishing ethanol percentages from 4 to 11 volume percentages, while continually submerged in a self-designed artificial gastric environment.
Overall, this research contributes to the advancement of ingestible sensors and their potential for continuous health monitoring, with a use case in alcoholism treatment. The outcomes show the potential to produce a sensor array encapsulated in biocompatible materials, with a data-driven sensor fusion algorithm that is deployable on-device, which brings us closer to practical in-vivo applications. Additionally, the design and utilization of an artificial gastric environment establishes a solid foundation for future studies and the data generation that is vital for this technology.
This research focuses on the design of a gas sensor array for a retentive ingestible device, targeting the measurement of volatile organic compounds in the gastrointestinal tract. The research specifically addresses the outpatient treatment of alcoholism, in collaboration with the emergency medicine department at Brigham and Women's Hospital, Harvard Medical School. Current treatment relies on manual registration of drinking behavior, and the incorporation of an ingestible device that is able to continuously monitor drinking behavior enables more accurate behavioral assessment, and more targeted support and treatment.
Given the novelty of this approach, a sensor array capable of assessing ethanol concentrations in the air was gradually subjected to more complex tasks. Furthermore, a key aspect of the research was the design of an artificial gastric environment, that replicates the conditions and challenges that the sensor array would encounter within the human body. This experimental environment played a crucial role by providing a realistic testing platform without the need for a fully functional ingestible device, which can be incredibly challenging and resource-intensive to manufacture.
To ensure functionality in humid conditions, the sensors are encapsulated using parylene C and polycaprolactone (PCL) - biocompatible materials commonly used in the design of biomedical devices. The impact of these encapsulation methods on the sensors is thoroughly assessed, to determine their viability for in-vivo applications.
The findings of this study reveal that employing a convolutional neural network can enable the accurate measurement of ethanol in air, using off-the-shelf air quality sensors and algorithms with low computational complexity. It is worth highlighting that the neural network is capable of performing inference directly on the ingestible device. Furthermore, initial results show that a combination of parylene C and PCL, achieved through dip-coating in a PCL-dichloromethane solvent, yields a sensor capable of reliably distinguishing ethanol percentages from 4 to 11 volume percentages, while continually submerged in a self-designed artificial gastric environment.
Overall, this research contributes to the advancement of ingestible sensors and their potential for continuous health monitoring, with a use case in alcoholism treatment. The outcomes show the potential to produce a sensor array encapsulated in biocompatible materials, with a data-driven sensor fusion algorithm that is deployable on-device, which brings us closer to practical in-vivo applications. Additionally, the design and utilization of an artificial gastric environment establishes a solid foundation for future studies and the data generation that is vital for this technology.
Electronic markers for geological research
Tag Design
Bachelor thesis
(2019)
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Michael Kraaijeveld, David de Gruijl, Marco Postma, Stefano Roos, Daan de Groot, Emiel van der Meijs, Ger de Graaf, Alle-Jan van der Veen, Akira Endo
In this thesis, the implementation of a passive, chipless, frequency coded Radio-Frequency Identification (RFID) tag for bedload transport studies is proposed. The proposed tag will be deployed in the semi-arid Río Colorado river, Bolivia with the aim to develop quantitative sediment transport models that relate transport to grain size. The designed tag is an open-loop resonator with a fragment-loading structure, that has an op- timised configuration based on a Multiobjective Evolutionary Algorithm based on Decomposition combined with Enhanced Genetic Operators (MOEA/D-GO). The designed RFID tag can ideally reach a size of 4 by 4 millimetres with a maximum calculated reading range of 1.3 meters, and operates in the ultra wide band from 3 to 7 gigahertz. Numerous simulations on the tags were run to verify their properties. The tags proved to have a good directivity, quality factor and radio cross section on its resonant frequency. The tags could reach resonance frequencies as low as 2.9 gigahertz and quality factors as high as 130. The proof of concept on a Printed Circuit Board with an FR-4 substrate results in a tag of 6.4 by 3.4 millimetres. Unfortunately, these properties could not yet be verified by measuremen
...
In this thesis, the implementation of a passive, chipless, frequency coded Radio-Frequency Identification (RFID) tag for bedload transport studies is proposed. The proposed tag will be deployed in the semi-arid Río Colorado river, Bolivia with the aim to develop quantitative sediment transport models that relate transport to grain size. The designed tag is an open-loop resonator with a fragment-loading structure, that has an op- timised configuration based on a Multiobjective Evolutionary Algorithm based on Decomposition combined with Enhanced Genetic Operators (MOEA/D-GO). The designed RFID tag can ideally reach a size of 4 by 4 millimetres with a maximum calculated reading range of 1.3 meters, and operates in the ultra wide band from 3 to 7 gigahertz. Numerous simulations on the tags were run to verify their properties. The tags proved to have a good directivity, quality factor and radio cross section on its resonant frequency. The tags could reach resonance frequencies as low as 2.9 gigahertz and quality factors as high as 130. The proof of concept on a Printed Circuit Board with an FR-4 substrate results in a tag of 6.4 by 3.4 millimetres. Unfortunately, these properties could not yet be verified by measuremen