Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. Approach: The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18 g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions. Approach: The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

BlinkBoard: Guiding and monitoring circuit assembly for synchronous and remote physical computing education.

Motivated by the necessity of guiding and monitoring students when assembling electronic circuits during in-class activities, we propose BlinkBoard, an augmented breadboard that enhances synchronous and remote physical computing classes. BlinkBoard uses LEDs placed on each row of a breadboard to guide, via four distinct blinking patterns, how to place and connect components and wires. It also uses a set of Input/Output pins to sense voltage levels or to generate voltage output at user-specified rows. Our hardware uses an open protocol of JSON commands and responses that can be used directly via a command line interface to control the hardware. Alternatively, these commands can be issued within a front-end graphical application hosted on a computer for a more user-friendly interface, and to ensure bidirectional and real-time communication between the instructor's guiding and monitoring panel, and all the students' remote BlinkBoards. The BlinkBoard hardware is affordable and simple, partially due to a customized circuit configured via a hardware description language that handles the LEDs' patterns with minimal load on the Arduino microcontroller. Finally, we briefly show BlinkBoard in use during a workshop with high-school students and an undergraduate design course.

Mechanical Imaging of Soft Tissues With Miniature Climbing Robots.

Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depending on the skill of the practitioner. Research shows that tactile sensors could be more sensitive than a hand. We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. Robots are more systematic and repeatable than humans. Using the data collected with a cutomoter or indenter integrated into the miniature robot, we trained a convolutional neural network to classify the size and depth of the lumps. The classification works with 98.8% accuracy for cutometer and 99.6% for indenter for lump size with a diameter of 0 to 10 mm embedded in depth of 1 to 5 mm in a simulated tissue. We conducted a limited evaluation on a forearm, where the robot imaged dry skin with a cutometer. We hope to improve the ability to test tissues noninvasively, and ultimately provide better sensitivity and systematic data collection.

BioEssence: A Wearable Olfactory Display that Monitors Cardio-respiratory Information to Support Mental Wellbeing.

This work introduces a novel wearable olfactory display that provides just-in-time release of scents based on the physiological state of the wearer. The device can release up to three scents and passively captures subtle chest vibrations associated with the beating of the heart and respiration through clothes.BioEssenceiscontrolledviaacustom-madesmartphone app that allows the creation of physiological rules to trigger different scents (e.g., when the heart rate is above 80 beats per minute, release lavender scent). The device is wireless and lightweight, and it is designed to be used during daily life, clipped on clothes around the sternum area or used as a necklace. We provide a description of the design and implementation of the prototype and potential use cases in the context of mental wellbeing.