Tufts University  |  School of Arts and Sciences  |  School of Engineering  |  Find People  | 

Research Projects

Design of an Integrated Analog-to-Information Converter Based Sensor System for Neurophysiological Monitoring in Animals

Robbie D'Angelo, PhD candidate in Electrical and Computer Engineering

Sameer Sonkusale, Electrical and Computer Engineering
Barry Trimmer, Biology

Low power wireless sensor systems are of increasing interest in a variety of markets including medicine, robotics, gaming and consumer electronics. However, although many commercial examples of wireless sensors are in product circulation, measuring physiological events in small animals such as insects remains a challenge due to limited size and power requirements. A primary limitation on power consumption is the amount of data that must be transmitted. Typically, an analog-to-digital converter (ADC) is used to sample at or above the Nyquist rate to acquire a digital representation of the signals of interest. This digital data is then sent over a communication channel. In most systems both ADCs and transmitters consume power proportional to the number of samples that are taken.

We propose to reduce power consumption by reducing the number of digital samples that must be acquired to gather the signals of interest. This can be done by using an analog-to-information converter (AIC), instead of a traditional ADC, to extract features of the physiological events rather than the raw data. These extracted features often have a much more concise digital representation, decreasing the data transmission rate which consequently decreases the power consumption.

In this project we would like to apply the AIC concept to the design of a physiological monitoring system for studying the neurophysiology of animals. The information gathered could be used to inform the design of novel biomimetic robots. The end product would be a compact system that integrates the sensing electrodes and front end electronics with the AIC circuitry. If this is successful, wireless power and data telemetry could also be integrated to fully eliminate the need for tethering, which may allow the animal to move more naturally.

This project will use the manduca sexta as a model system due to its complex motor mechanics, which, if unfolded, could have a profound impact on the design of soft robot control systems. A miniaturized sensing system of this type could be easily modified for several other physiological monitoring contexts. Experiments with the model system to verify the applicability of the sensor platform will be conducted in collaboration with the Neuromechanics and Biomimetic Devices Laboratory at Tufts University under Dr. Barry Trimmer.