Date of Award
Summer 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
Program/Concentration
Computer Science
Committee Director
Sampath Jayarathna
Committee Member
Jian Wu
Committee Member
Vikas G. Ashok
Committee Member
Yi He
Committee Member
Keerthi Nawarathna
Abstract
In human subjects research, biosignals such as eye movements, heart rate, and brain activity, are often collected and analyzed to find patterns with tangible real-world implications. Modern advancements in technology have sparked interest towards analyzing biosignals in realtime. When developing such algorithms, one may expect to find free, open-source tools that provide easy access to live, recorded, and simulated data streams. Yet, biosignal interfaces are often vendor-specific, making cross-vendor biosignal streaming non-trivial. Likewise, reading biosignal datasets is also non-trivial, as their content may be arranged quite differently.
To combat this divide, we provide the scientific community with a realtime biosignal processing framework, Streaminghub, which decouples data access from science. Streaminghub provides simple APIs to stream biosignal data and is extensible to new devices and data formats. It consists of three key components: (1) DFDS – JSON schema to describe data sources by the streams they generate, (2) Curator – a web application to create DFDS-enriched streamable datasets, and (3) DataMux – an API to define and run pipelines on biosignal data streams with less code.
Through user studies, we establish the need for Streaminghub, and find Streaminghub to generate clear, reusable code, and provide excellent usability for realtime biosignal analysis. Through case studies, we show that Streaminghub simplifies realtime biosignal processing. Through performance tests, we show that Streaminghub yields stable performance across different biosignals and runtimes.
Rights
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DOI
10.25777/sj9t-r102
ISBN
9798384444251
Recommended Citation
Jayawardana, Yasith.
"Streaminghub - A Realtime Biosignal Processing Framework for Lab Scale Experimentation"
(2024). Doctor of Philosophy (PhD), Dissertation, Computer Science, Old Dominion University, DOI: 10.25777/sj9t-r102
https://digitalcommons.odu.edu/computerscience_etds/182
ORCID
0000-0001-5992-6818
Included in
Bioinformatics Commons, Computer Sciences Commons, Data Science Commons, Neuroscience and Neurobiology Commons, Physiology Commons