Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation

Visual inertial odometry (VIO) is a process for fusing visual and kinematic data to understand a machine's state in a navigation task. Olfactory inertial odometry (OIO) is an analog to VIO that fuses signals from gas sensors with inertial data to help a robot navigate by scent. Gas dynamics and environmental factors introduce disturbances into olfactory navigation tasks that can make OIO difficult to facilitate. With our work here, we define a process for calibrating a robot for OIO that generalizes to several olfaction sensor types. Our focus is specifically on calibrating OIO for centimeter-level accuracy in localizing an odor source on a slow-moving robot platform to demonstrate use cases in robotic surgery and touchless security screening. We demonstrate our process for OIO calibration on a real robotic arm and show how this calibration improves performance over a cold-start olfactory navigation task.

Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy towards assessing blood glucose modulations

There has been a recent tremendous interest in label-free detection of biomarkers which is a critical enabler of point-of-need diagnostics. A low-power, small form factor, multiplexed wearable system is proposed for continuous detection of glucose in passively expressed sweat using electrochemical impedance spectroscopy (EIS) measurement. The wearable EIS system consists of a sensing analog front end integrated with low-volume (1-5 $\mu$L) ultra-sensitive flexible biosensors. A passive sweat sensor was designed to integrate a glucose oxidase electrochemical system on active semiconducting material. The non-faradaic EIS response of the biosensor was used to calibrate the analog front end response using ratiometric Discrete Fourier Transform (DFT) for a shorter measurement time. In this work, a stringent assessment of a continuous glucose sensing platform is performed in a bottom-up approach, going from the biosensor to the system to the interaction with a human subject. The active semiconductor-based biosensors are dosed with glucose concentrations ranging from 5-200 mg/dL and detection is performed using the analog front end. In addition, a detailed analysis of battery life and performance of a wearable EIS system is discussed to define a figure of merit for an optimally integrated design. Moreover, a continuous glucose detection test is performed on a healthy human subject cohort to investigate the stability of the sensor-system mechanism for an 8-hour period, and a time-series-based, auto-regressive (AR) model was created for the system.