Imagine you’re collecting seashells on a beach. You want to gather as many different types as you can, but some shells are rare and hard to find. To get a comprehensive collection, you have to spend a lot of time searching. Similarly, in the world of healthcare algorithms, scientists need a diverse range of biosignals to create accurate and personalized models. That’s where EpiBOX comes in. It’s like having an automated shell collector that tirelessly collects biosignals over long periods of time. EpiBOX is easy to use, even for people who aren’t tech-savvy, and it provides the customization researchers need for their studies. The EpiBOX framework has been extensively tested and found to have minimal latency, even under unstable network connections. The performance of the EpiBOX user interface is also top-notch, ensuring a smooth experience for users. With this platform, physicians, technicians, and research subjects can monitor the biosignal collection process directly, allowing researchers to gather a wealth of valuable data. This opens up exciting possibilities for advancing healthcare technology and improving patient outcomes. Check out the full article to learn more about how EpiBOX is revolutionizing biosignal collection!

Biosignals represent a first-line source of information to understand the behavior and state of human biological systems, often used in machine learning problems. However, the development of healthcare-related algorithms that are both personalized and robust requires the collection of large volumes of data to capture representative instances of all possible states. While the rise of flexible biosignal acquisition solutions has enabled the expedition of data collection, they often require complicated frameworks or do not provide the customization required in some research contexts. As such, EpiBOX was developed as an open-source, standalone, and automated platform that enables the long-term acquisition of biosignals, passable to be operated by individuals with low technological proficiency. In particular, in this paper, we present an in-depth explanation of the framework, methods for the evaluation of its performance, and the corresponding findings regarding the perspective of the end-user. The impact of the network connection on data transfer latency was studied, demonstrating innocuous latency values for reasonable signal strengths and manageable latency values even when the connection was unstable. Moreover, performance profiling of the EpiBOX user interface (mobile application) indicates a suitable performance in all aspects, providing an encouraging outlook on adherence to the system. Finally, the experience of our research group is described as a use case, indicating a promising outlook regarding the use of the EpiBOX framework within similar contexts. As a byproduct of these features, our hope is that by empowering physicians, technicians, and monitored subjects to supervise the biosignal collection process, we enable researchers to scale biosignal collection.

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