Non-contact infrared thermal imaging provides a non-invasive, safe and accurate means for screening subjects. COVID-19 (a disease caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2)) has changed how health care workers (HCW) interact with patients. Despite the usage of personal protective equipment (PPE), social distancing protocols, and unique facilities to screen individuals, the increase of infection rates among HCW is high. This has enormous consequences as HCW cannot provide direct patient care, thereby decreasing the workforce’s availability during the pandemic and has fuelled the urgent need for a non-invasive IR system to screen for potential cases of infections. Existing remote screening systems are susceptible to shortcomings caused by location and technique used for measurement, distance-ambient temperature associated inaccuracies and inefficient integration of hardware and software modules, invalidating their utility in the current situation. During this project, a non-invasive screening prototype was developed that mitigates these shortcomings by using an adaptive measurement method with automatic environmental compensation for screening elevated skin temperature and respiratory rate. The prototype is tested and validated in a controlled environment.

With over 75,000 attributed deaths in 2020, opioid overdose is now the leading cause of death amongst Americans under the age of 50. With record-breaking increases in fatalities for the past 10 years, fentanyl in particular has many experts worried about the future of the opioid epidemic. To reverse opioid overdose there exists a drug called Naloxone, an opioid antagonist that completely restores the subject’s vitality within minutes. As the US opioid epidemic is surging by the increase of synthetic opioid-related overdoses, current methods of Naloxone administration are unsuccessful in suppressing the rise. One large obstacle in the rate of success for Naloxone is related to the fact that opioid users are not able to administer the antagonist to themselves due to their overdosing symptoms, which includes unconsciousness. Therefore, to save a life with Naloxone, one has to rely on a bystander that is present during the overdose, recognizes it, possesses Naloxone, and is able to administer it in time. The rising death toll of opioid overdose reveals that one of these conditions are often not met. This thesis presents a fully implantable device that is capable of autonomously detecting and treating opioid overdose to provide an alternative solution that takes the said bystander out of the equation. To achieve this, three different sensors (an Inertial Measurement Unit (IMU), Electrocardiography (ECG), and Photoplethysmography (PPG) sensor) are used for the monitoring of heart rate and respiratory rate. These vital sings are continuously fed into an advanced decision-making system that is capable of detecting opioid overdose within 81 seconds. Once detected, instructions are sent to a novel drug delivery pump that is capable of delivering high volumes of Naloxone within only 3.25 seconds, while outperforming similar technologies in terms of energy efficiency. The proposed subcutaneous implantable device can be refilled through a refill port, be wirelessly charged, and has a battery capacity that lasts up to 78 days. It is concluded that the proposed closed-loop drug delivery system is a feasible and effective autonomous tool that could complement existing Naloxone administration solutions in the ongoing fight against the opioid epidemic.

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.