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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
521

Low Cost Manufacturing of Wearable and Implantable Biomedical Devices

Behnam Sadri (8999030) 16 November 2020 (has links)
Traditional fabrication methods used to manufacture biosensors for physiological, therapeutics, or health monitoring purposes are complex and rely on costly materials, which has hindered their adoption as single-use medical devices. The development of a new kind of wearable and implantable electronics relying on inexpensive materials for their manufacturing will pave the way towards the ubiquitous adoption of sticker-like health tracking devices.<div>One of growing and most promising applications for biosensors is the continuous health monitoring using mechanically soft, stretchable sensors. While these healthcare devices showed an excellent compatibility with human tissues, they still need highly trained personnel to perform multi-step, prolonged fabrication for several functioning layers of the device. In this dissertation, I propose low-cost, scalable, simple, and rapid manufacturing techniques to fabricate multifunctional epidermal and implantable sensors to monitor a range of biosignals including heart, muscle, or eye activity to characterizing of biofuids such as sweat. I have also used these devices as an implant to provide heat therapy for muscle regeneration and optical stimulation of neurons using optogenetics. These devices have also combined with those of triboelectric<br>nanogenerators to realize self-powered sensors for monitoring imperceptible mechanical biosignals such as respiratory and pulse rate.</div><div>Food health and safety has also emerged as another important frontier to develop biosensors and improve the human health and quality of life. The recent progresses on detecting microbial activity inside foods or their packages rely on development of highly functional materials. The existing materials for fabrication of food sensors, however,<br>are often costly and toxic for human health or the environment. In this dissertation, I proposed biocompatible food sensors using protein/PCL microfibers to reinforce the protein microfibrous structure in humid conditions and exploit their excellent hygroscopic properties to sense biogenic gas, as an indicator for early detection of food spoilage. Finally, my battery-free food sensors are capable of monitoring food safety with no need of extra measurement devices. Collectively, this dissertation proposes cost-effective solutions to solve human health issues, enabled by developing low-cost, functional materials and exploiting simple fabrication techniques.<br></div>
522

Low-Power Policies Based on DVFS for the MUSEIC v2 System-on-Chip

Mallangi, Siva Sai Reddy January 2017 (has links)
Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.
523

Development of Novel Wearable Sensor System Capable of Measuring and Distinguishing Between Compression and Shear Forces for Biomedical Applications

Dimitrija Dusko Pecoski (8797031) 21 June 2022 (has links)
<p>There are no commercially available wearable shoe in-sole sensors that are capable of measuring and distinguishing between shear and compression forces. Companies have already developed shoe sensors that simply measure pressure and make general inferences on the collected data with elaborate software [2, 3, 4, 5]. Researchers have also attempted making sensors that are capable of measuring shear forces, but they are not well suited for biomedical applications [61, 62, 63, 64]. This work focuses on the development of a novel wearable sensor system that is capable of identifying and measuring shear and compression forces through the use of capacitive sensing. Custom hardware and software tools such as materials test systems and capacitive measurement systems were developed during this work. Numerous sensor prototypes were developed, characterized, and optimized during the scope of this project. Upon analysis of the data, the best capacitive measurement system developed in this work utilized the CAV444 IC chip, whereas the use of the Arduino-derived measurement system required data filtering using median and Butterworth zero phase low pass filters. The highest dielectric constant reported from optimization experiments yielded 9.7034 (+/- 0.0801 STD) through the use of 60.2% by weight calcium copper titanate and ReoFlex-60 silicone. The experiments suggest certain sensors developed in this work feasibly measure and distinguish between shear and compressional forces. Applications for such technology focus on improving quality of life in areas such as managing diabetic ulcer formation, preventing injuries, optimizing performance for athletes and military personnel, and augmenting the scope of motion capture in biomechanical studies.</p>

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