Research Interests

The central focus of my research has been the development of skin-like wearable electronics and sensors that seamlessly integrate with the human body and provide hospital quality physiological monitoring and diagnostics in a simple, minimally obtrusive platform. My efforts may be classified in 4 major categories: 

  1. Human-interfaced sensors and electronics

  2. Fully printed sensor manufacturing

  3. Biosignal processing

  4. Machine learning

Nathan Zavanelli

Human Interfaced Sensors and Electronics

The human body is soft, and rigid electronics suffer a mechanical mismatch that degrades signal quality and long-term wearability. To address this critical shortcoming, I have conducted analytical, computational, empirical, and human subjects studies in soft materials and interfaces to enable a new class of wearable, wireless devices and sensors with mechanics finely tuned to transduce electrical, mechanical, and optical bio-signals from the human body.

Fully Printed Sensor Manufacturing

I am developing an additive manufacturing approach based on using screen, aerosol and inkjet printing to pattern  functionalized nanomaterials for thin film sensors. This scalable, versatile and high throughput manufacturing approach could revolutionize wearable physiological sensing.

Biosignal Processing

Crucial to my work is the development of new signal processing techniques, including wavelet leader and bulk modulus maxima estimates, to translate the wealth of physiological signals the body produces into actionable metrics for clinicians, control systems, or human-computer interfaces. 

Machine Learning

I experiment with a wide variety of machine learning techniques, deep and traditional, supervised and unsupervised for physiological state classification and regression. These results may then be used for control and diagnostics applications.