This book provides a practical end-to-end approach to open source technology in biomedical engineering, covering topics that range from hardware and software design to data acquisition, processing tools, and cloud-based storage. Biomedical device conceptualization, design of experimental evaluation studies, and moving from early-stage prototypes to shelve-worthy products benefiting from open source technologies are also covered. The technical chapters are complemented by working examples and address problems that new entrants and professionals encounter when developing work in biomedical engineering, human-computer interaction, physiological computing, psychophysiology, physiotherapy, and related areas. The book is enriched by case studies where open source technologies have been successfully used to accelerate new developments in biomedical engineering. Contributions are rooted in the state-of-the-art and latest advances in hardware platforms, Python for the signal processing and analysis components, and web-based technologies for the user interface components.
- Provides hardware, software, and product design guidelines;
- Includes source code, case studies, and application examples;
- Accessible to a broad audience interested in moving quickly from a biomedical idea to a solution.
Ch 1: Structured Methodologies for Iterative Concept Design and
Biomedical Device Development.- Ch 2: ScientISST CORE: A Novel Hardware
Platform for Rapid Prototyping.- Ch 3: Abstracting Low-level Hardware and
Firmware Specificities Through Application Programming Interfaces (APIs).- Ch
4: BioSPPy: Opening Physiological Signal Processing to the World.- Ch 5:
Interactive Machine Learning: Strategies for Live Performance Using
Electromyography.- Ch 6: REPOVIZZ: An Online Platform for Open Access and
Visualization of Time-Aligned Multimodal Data Streams.- Ch 7: Creating a
Biomedical Dataset: Conceiving a Purpose Statement, Developing an
Experimental Protocol, and Addressing Data Management.- Ch 8: Using
open-source Hardware for Enhancing Lean Entrepreneurship.- Ch 9: Technology
Transfer: From Research to Industrialization.- Ch 10: Case Study: Towards
Firefighter Monitoring in the Real-World.- Ch 11: Case Study: Sympathia
Sense.- Ch 12: Case Study: COPD Pulmonary Rehabilitation with Biofeedback
Physiotherapy.
Hugo Plácido da Silva is an award-winning biomedical researcher, inventor, and entrepreneur. He obtained his PhD with Distinction and Honour in Electrical and Computer Engineering and his Habilitation in Biomedical Engineering, both from the Instituto Superior Técnico (IST) University of Lisbon (UL). Dr. Silva is currently a Senior Researcher at the IT - Instituto de Telecomunicações and a Professor at IST-UL. He is co-founder of multiple innovative technology-based companies operating in the fields of biomedical devices and artificial intelligence for healthcare and quality of life. Both at a technical and scientific level, he has actively contributed to and participated in more than 60 national and international projects. Dr. Silva holds 10 granted patents and published 270+ papers in peer reviewed journals, international refereed conferences, and book chapters. His work has been distinguished both internationally and domestically with several awards, including the IEEE Entrepreneurship Impact Award, Career Award alumniIPS, or the Ordem dos Engenheiros Young Engineer Innovation Award, just to name a few. His main research interests include biosignal acquisition and knowledge extraction.
Patrícia Bota holds a PhD with Distinction and Honour from Instituto Superior Técnico (IST) University of Lisbon (UL), where she specialized in the study of human group emotions using physiological data and artificial intelligence. Her research has been published in leading journals, including IEEE Transactions on Affective Computing. Her research journey began at the Fraunhofer Portugal and later continued at the IT - Instituto de Telecomunicações, where she developed innovative algorithms for real-world biomedical applications. Dr. Bota has actively contributed to multiple open-source projects widely used in academia and industry, including ScientISST NOTES (a Jupyter Notebooks repository for biomedical engineering education), BioSPPy (a Python toolbox for biosignal processing), and TSFEL (a feature extraction library for time-series analysis). With over 1,000 citations across more than 10 peer-reviewed publications, Patrícia has been involved in over 15 international collaborations and has presented her work at prestigious events, including a NATO-related talk, a showcase at WebSummit 2019, and the European Commissions Resonances III Festival. Her expertise spans physiological signal data collection, signal processing, and machine learning, with a focus on AI-driven insights into human behavior and healthcare.
Ana Sofia Carmo is a biomedical engineer and award-winning researcher specializing in enabling technologies for physiological monitoring and seizure forecasting in epilepsy. She is completing a PhD in Biomedical Engineering at Instituto Superior Técnico (IST) Universidade de Lisboa (UL), integrated in IT - Instituto de Telecomunicações, focusing on smart health and machine learning applications. She has actively contributed to multiple open-source projects, including SeFEF (Seizure Forecast Evaluation Framework) and EpiBOX, a system deployed in three hospitals across two research settings. Additionally, she plays a key role in PreEpiSeizures, a collaborative, multi-center project, where she is one of the most active contributors. Ana Sofia has co-authored seven scientific publications in leading journals, including the Journal of Neurology and IEEE Transactions on Biomedical Engineering. Her research interests are centered on developing innovative tools that bridge the gap between research and real-world healthcare applications. Beyond research, she is passionate about engineering outreach. She is a teaching assistant for biosignal instrumentation courses and actively contributes to ScientISST, an initiative promoting accessible biomedical instrumentation. Her contributions to ScientISST helped it gain recognition as a Best Practice by ObservIST (Técnico-Lisboas Best Practices Observatory).
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