A New Lens on Life: Cross-Contextual Sensing Technologies from Human Insights to Wildlife Conservation
Author(s)
Chwalek, Patrick C.
DownloadThesis PDF (107.6Mb)
Advisor
Paradiso, Joseph A.
Terms of use
Metadata
Show full item recordAbstract
The growing complexity of human-centric and ecological systems demands new sensing technologies capable of capturing holistic, contextual insights in real-world environments. However, a critical gap exists in the availability of integrated, intelligent platforms that can be adapted across these diverse domains. This dissertation addresses this challenge by designing, engineering, and validating a series of novel, multi-modal sensing platforms to provide a new and more insightful lens on a broad range of life.
The research spans two primary contexts. First, to explore the nuances of human well-being, the AirSpecs smart-eyeglass platform was developed and deployed. This system holistically measures an individual's proximate environment and physiological parameters, and was validated in a multi-site international study to investigate the dynamics of human comfort "in-the-wild". Second, to advance ecological monitoring, a progression of acoustic platforms was engineered. The SoundSHROOM system was created as a robust, multi-channel recorder for harsh environments and successfully deployed in the Arctic. Building on this, the BuzzCam system was developed for targeted pollinator monitoring, culminating in an end-to-end pipeline for on-device AI classification of endangered and invasive bee species in Patagonia. Finally, the CollarID platform was engineered and characterized as a versatile, low-power, multi-modal animal-borne sensor for wildlife tracking, integrating inertial, bioacoustic, and comprehensive environmental sensing to move beyond the limitations of location-only devices.
Key contributions of this work include the validated hardware platforms themselves; several unique, publicly available datasets from urban, Arctic, and Patagonian deployments; and a demonstrated methodology for implementing on-device AI to address the data-to-insight bottleneck in ecological monitoring. Collectively, this research provides the scientific community with a new suite of powerful research tools and demonstrates a cross-contextual design philosophy, leveraging engineering principles across disparate fields to enable a deeper understanding of organisms and their complex interactions with their environments.
Date issued
2025-09Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)Publisher
Massachusetts Institute of Technology