Programmable interfaces for biomedical and neuroscience applications
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Anantha P. Chandrakasan and Polina Anikeeva.
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The rapidly changing fields of biomedical sciences and neuroscience increasingly adopt scientific and technological innovations to advance the diagnosis and treatment of disease. With the emergence of miniaturized and low-cost electronics, intelligent and computationally-efficient algorithms, together with new materials and fabrication techniques, an interdisciplinary approach becomes key in designing human-machine interface systems for these applications. This thesis explores the design of four programmable interface systems in this domain. First, we present an EMG-based facial gesture recognition platform. The system integrates a custom-designed EMG sensor interface for energy-efficient signal acquisition from a small footprint. The gesture recognition algorithm runs on the computer and achieves the classification of resting, clenching, chewing, and jaw opening activities in real-time. A wavelet-transform-based feature extraction improves the computational-efficiency.Next, we present an optoelectronic system for wireless neuromodulation during free behavior. The head-borne system interfaces with flexible, fiber-based, multifunctional brain probes that carry integrated [mu]LEDs for optical stimulation. The modular platform can also perform precise optical intensity control, in-vivo temperature sensing, and low-frequency neural recording when needed. The system uses BLE to communicate with the computer and can control multiple [mu]LEDs and multiple devices on different animals. Third, we present a strain-programmable artificial muscle, suitable for use in soft-robotics, neuroprosthetics, and smart-textiles applications. The fiber muscle is arbitrarily scalable and can be produced in kilometer-long scales. The strain-programmability allows precise control of the mechanical and electrical properties. It can carry 650 times of its weight, achieves a power-to-mass-ratio of 90 W/kg, and latency levels as low as 0.02 seconds.The conductive versions allow for direct piezoresistive feedback. Multiple fibers can be used in parallel to form bundle structures similar to human muscle. Finally, we present a biometric interface integrated into transparent, long-lasting respirators. The respirators are alternatives to commonly-used N95 masks. The electronic interface uses one of the filter insert locations to measure temperature, humidity, pressure, and air quality. The system uses BLE and sends real-time sensor information to a phone or a computer. The data can be used to inform the user regarding mask fit, fatigue, mask condition, and potential diagnostic information.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, February, 2021Cataloged from the official PDF of thesis.Includes bibliographical references (pages 143-157).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.