Human Augmentation Technologies
Andrew J. Mason | firstname.lastname@example.org | www.egr.msu.edu/hatlab/
The mission of the Human Augmentation Technologies Laboratory (HATlab) is to develop innovative integrated circuit and microsystem approaches that bridge the gap between novel sensor technologies and high-impact biomedical and environmental applications.
Miniaturize Ionic Liquid Electrochemical Gas Sensors
The growing impact of airborne pollutants and explosive gasses on human health has escalated the demand for hazardous gas sensors. Our goal is to develop a wearable autonomous multi-gas sensor system for real-time monitoring of gaseous pollutants and hazards. Sensors, electronics, and data analysis algorithms are being synergistically integrated to overcome challenges in real world deployment. Electrochemical sensors featuring room temperature ionic liquid are utilized for low-power operation, high sensitivity and selectivity, and long life with low maintenance. Microfabricated electrodes and chip-scale instrumentation electronics are being developed to miniaturize a personal health monitor.
Portable Malaria Detection System
Malaria is one of the most deadly infectious diseases in the world, claiming more than 1.24 million lives annually (2010). This project aims to implement a system for automated, fast and reliable clinical detection of deadly plasmodium falciparum malaria parasites. The system will be usable by any individual with access to a smart phone and minimal technical knowledge allowing broad scale testing deep within infected regions to greatly reduce the death rate of infections. To enable miniaturized and autonomous sensing, our team is developing highly sensitive and low power sensor instrumentation hardware to measure electrochemical biosensor data and deliver it to a generic smart phone for user interactions.
High Channel Count Neural Signal Processing
Future brain-machine interface applications, including neutrally-controlled prosthetics, require simultaneous recording from thousands of neurons. Our research aims to develop an implantable neural signal processor capable of preserving neural information while achieving a high data compression rate. We are applying techniques from signal detection theory and pattern recognition to produce novel spike-sorting algorithms, and we are developing streaming digital hardware architectures to implement these algorithms on low power implantable chips capable of processing data from thousands of recorded neurons.
High-density biosensor arrays require tight integration of sensors, instrumentation, and micro-fluidics. We have pioneered a “lab-on-CMOS” concept for integrating high-density biosensors on the surface of CMOS instrumentation chips with multi-channel microfluidics using a unique packaging process. This platform can greatly improve measurement throughput in a variety of biomedical research and clinical applications.
Automating and Organizing Sensor Array Testing
We are developing a general-purpose software application integrated with computer-controlled instrumentation hardware for automatically executing large volumes of sensor characterization experiments. We believe that this work will yield better tools for methodical execution of experiments and differential analysis of results to improve the reusability and reliability of massive datasets collected from complex workflows, with potential impact in many research fields.