docs: Add manuscript for JOSS journal

Add Biostream manuscript for Journal of Open Source Software (JOSS) submission

Author: marianoalbaladejo

docs/manuscript/bibliography.bib

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+@Article{app14156649,
+    AUTHOR = {Migliaccio, Gian Mario and Padulo, Johnny and Russo, Luca},
+    TITLE = {The Impact of Wearable Technologies on Marginal Gains in Sports Performance: An Integrative Overview on Advances in Sports, Exercise, and Health},
+    JOURNAL = {Applied Sciences},
+    VOLUME = {14},
+    YEAR = {2024},
+    NUMBER = {15},
+    ARTICLE-NUMBER = {6649},
+    ISSN = {2076-3417},
+    DOI = {10.3390/app14156649}
+}
+@article{Zhang2020,
+  title = {Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury},
+  volume = {21},
+  ISSN = {1471-2105},
+  DOI = {10.1186/s12859-020-03814-w},
+  number = {S17},
+  journal = {BMC Bioinformatics},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Zhang,  Ping and Roberts,  Tegan and Richards,  Brent and Haseler,  Luke J.},
+  year = {2020},
+  month = dec 
+}
+@Article{s21103461,
+    AUTHOR = {Hickey, Blake Anthony and Chalmers, Taryn and Newton, Phillip and Lin, Chin-Teng and Sibbritt, David and McLachlan, Craig S. and Clifton-Bligh, Roderick and Morley, John and Lal, Sara},
+    TITLE = {Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review},
+    JOURNAL = {Sensors},
+    VOLUME = {21},
+    YEAR = {2021},
+    NUMBER = {10},
+    ARTICLE-NUMBER = {3461},
+    PubMedID = {34065620},
+    ISSN = {1424-8220},
+    DOI = {10.3390/s21103461}
+}
+@article{Stolfi2020,
+  title = {Potential predictors of type-2 diabetes risk: machine learning,  synthetic data and wearable health devices},
+  volume = {21},
+  ISSN = {1471-2105},
+  DOI = {10.1186/s12859-020-03763-4},
+  number = {S17},
+  journal = {BMC Bioinformatics},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Stolfi,  Paola and Valentini,  Ilaria and Palumbo,  Maria Concetta and Tieri,  Paolo and Grignolio,  Andrea and Castiglione,  Filippo},
+  year = {2020},
+  month = dec 
+}
+@Inbook{AlbaladejoGonzalezBook,
+    author="Albaladejo-Gonz{\'a}lez, Mariano
+    and Ruip{\'e}rez-Valiente, Jos{\'e} A.",
+    editor="Ifenthaler, Dirk
+    and Seufert, Sabine",
+    title="Supporting Stress Detection Via AI and Non-invasive Wearables in the Context of Work",
+    bookTitle="Artificial Intelligence Education in the Context of Work",
+    year="2022",
+    publisher="Springer International Publishing",
+    address="Cham",
+    pages="77--97",
+    isbn="978-3-031-14489-9",
+    doi="10.1007/978-3-031-14489-9_5",
+}
+
+
+@ARTICLE{10584460,
+  author={Chettri, Nishan and Aprile, Antonio and Bonizzoni, Edoardo and Malcovati, Piero},
+  journal={IEEE Sensors Journal}, 
+  title={Advances in PPG Sensors Data Acquisition With Light-to-Digital Converters: A Review}, 
+  year={2024},
+  volume={24},
+  number={16},
+  pages={25261-25274},
+  doi={10.1109/JSEN.2024.3420170}
+}
+@article{Vliaho2022,
+  title = {Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation},
+  volume = {12},
+  ISSN = {1664-042X},
+  DOI = {10.3389/fphys.2021.778775},
+  journal = {Frontiers in Physiology},
+  publisher = {Frontiers Media SA},
+  author = {V\"{a}liaho,  Eemu-Samuli and Lipponen,  Jukka A. and Kuoppa,  Pekka and Martikainen,  Tero J. and J\"{a}ntti,  Helena and Rissanen,  Tuomas T. and Castrén,  Maaret and Halonen,  Jari and Tarvainen,  Mika P. and Laitinen,  Tiina M. and Laitinen,  Tomi P. and Santala,  Onni E. and Rantula,  Olli and Naukkarinen,  Noora S. and Hartikainen,  Juha E. K.},
+  year = {2022},
+  month = jan 
+}
+
+@article{Wei2021,
+  title = {Using machine learning to determine the correlation between physiological and environmental parameters and the induction of acute mountain sickness},
+  volume = {22},
+  ISSN = {1471-2105},
+  DOI = {10.1186/s12859-022-04749-0},
+  number = {S5},
+  journal = {BMC Bioinformatics},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Wei,  Chih-Yuan and Chen,  Ping-Nan and Lin,  Shih-Sung and Huang,  Tsai-Wang and Sun,  Ling-Chun and Tseng,  Chun-Wei and Lin,  Ke-Feng},
+  year = {2021},
+  month = nov 
+}
+@article{Schuurmans2020,
+  title = {Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG)},
+  volume = {44},
+  ISSN = {1573-689X},
+  DOI = {10.1007/s10916-020-01648-w},
+  number = {11},
+  journal = {Journal of Medical Systems},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Schuurmans,  Angela A. T. and de Looff,  Peter and Nijhof,  Karin S. and Rosada,  Catarina and Scholte,  Ron H. J. and Popma,  Arne and Otten,  Roy},
+  year = {2020},
+  month = sep 
+}
+@article{AlbaladejoGonzlez2025,
+  title = {A multimodal and adaptive gamified system to improve cybersecurity competence training},
+  volume = {28},
+  ISSN = {1573-7543},
+  DOI = {10.1007/s10586-025-05264-6},
+  number = {9},
+  journal = {Cluster Computing},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Albaladejo-González,  Mariano and Nespoli,  Pantaleone and Gómez Mármol,  Félix and Ruipérez-Valiente,  José A.},
+  year = {2025},
+  month = aug 
+}
+
+
+

docs/manuscript/images/BioStream_architecture.pdf

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+
+---
+BioStream: An open-source solution for biometric data collection through smartwatches
+
+
+title: 'BioStream: An open-source solution for biometric data collection through smartwatches'
+
+tags:
+  - Biometrics
+  - Physiological Signals
+  - Wear OS
+  - Smartwatches
+  - Data Collection
+
+authors:
+  - name: Mariano Albaladejo-González
+    affiliation: 1
+    corresponding: true
+  - name: Guillermo Vidal-Pina
+    affiliation: 1
+  - name: Félix Gómez Mármol
+    affiliation: 1
+  - name: José A. Ruipérez-Valiente
+    affiliation: 1
+
+affiliations:
+ - name: Universidad de Murcia, Calle Campus Universitario, Murcia, 30100, Murcia, Spain
+   index: 1
+
+date: 29 December 2025
+
+bibliography: bibliography.bib
+---
+
+# Summary
+
+BioStream is an integrated open-source infrastructure for biometric research through affordable devices, composed of two applications: SmartBioStream and ServerBioStream. SmartBioStream is a Wear OS application that enables the collection of biometric data through user-friendly smartwatches. In addition, we provide a web platform, ServerBioStream, to receive, store, and download the data sent by the smartwatches. The combination of both applications supports conducting biometric studies without the need for expensive research-oriented devices. SmartBioStream can also transmit biometric data to other systems for their analysis, such as educational and workplace platforms. The flexibility, ease of use, and cost-effectiveness of both applications make them valuable tools for democratizing biometric data collection and analysis.
+
+# Statement of need
+
+Biometric data provide valuable insights into individuals' physical and psychological states [@app14156649] [@Zhang2020]. In recent years, we have witnessed significant advancements in sensor technology, improving their precision and reducing their cost and size [@s21103461] [@Stolfi2020]. These improvements have also made it possible to include biometric sensors in commercial and user-oriented devices such as smartwatches [@AlbaladejoGonzalezBook].
+
+Despite the widespread adoption of biometric sensors in user-oriented devices, researchers cannot easily employ them because they do not provide a user-friendly application to collect and send data [@s21103461] [@10584460]. For this reason, researchers without software development skills or available time are forced to acquire expensive and complex research-oriented devices [@Vliaho2022] [@Wei2021]. However, Bachelor's, Master's, and PhD students sometimes cannot afford research-oriented devices such as the Empatica E4 [@Schuurmans2020]. This issue is magnified because some of these devices require purchasing and configuring intermediary devices, such as a mobile phone [@AlbaladejoGonzalezBook]. In addition, using expensive research-oriented devices makes it difficult to conduct long-term case studies with different participants.
+
+# Implementation
+
+BioStream enables the collection and transmission of biometric data from a smartwatch to a server via Wi-Fi. This application supports two different use cases: conducting biometric research and integrating biometric data into other end platforms. In biometric research, the application collects biometrics from the participants in a study and transmits them to a server along with an experiment identifier. We also developed ServerBioStream to receive, store, and export the data sent by the smartwatches. The second use case focuses on integrating SmartBioStream into the main server of an organization. Educational and work institutions can use SmartBioStream to provide biometric data to their software tools. 
+
+## Architecture
+
+SmartBioStream is a Kotlin application developed for Wear OS smartwatches. This application supports both aforementioned use cases by allowing users to select the appropriate configuration from the options menu. ServerBioStream is a Django web platform that integrates an Application Programming Interface (API) to receive and store the data sent by the smartwatches using SmartBioStream. ServerBioStream uses SQLite for accessible data storage and visualization, though it also supports PostgreSQL for better performance in concurrent experiments. Figure 1 illustrates the architecture of both applications. The communication between SmartBioStream and a server is summarized in Figure 2.
+
+
+![Architecture of SmartBioStream and ServerBioStream for biometric research](images/BioStream_architecture.pdf)
+
+
+![Summary of the communication between SmartBioStream and a server, such as ServerBioStream](images/BioStream_communication.pdf)
+
+## Software functionalities
+
+This subsection describes the main functionalities of both developed applications. The main functionality of SmartBioStream is to collect and transmit biometric data from smartwatches. ServerBioStream is an example of a platform that stores the data emitted by SmartBioStream. In addition to testing and verifying the data transmission, this application is highly valuable for researchers, as it enables them to conduct biometric case studies through Wear OS smartwatches.
+
+### SmartBioStream
+
+This application's user interface was designed to be simple and intuitive, especially the views of the end-users. This application integrates three main functionalities:
+
+* **Options.** This functionality enables users to adjust settings related to communication with the server. The views of this functionality enable the selection of the server's IP address, the port, the protocol (HTTP or HTTPS), whether the application should verify the HTTPS certificate, and the authentication method (username and password or identifier). 
+
+* **Connection test.** This view checks the connectivity between the application and the server. Before starting an experiment, a researcher should verify the server's availability.
+
+* **Data collection.** These views collect and send biometrics from the end-users. Depending on the application settings, this functionality may require an experiment identifier or a username and password. After registration, the users select from the available sensors, including heart rate, accelerometer, gyroscope, and temperature. Then, the users access the recording view, where they can temporarily pause the data collection, return to the sensor menu, or navigate back to the main menu. Figure 3 summarizes the data collection and options views.
+
+![Data collection and option views of SmartBioStream](images/SmartBioStream.pdf)
+
+
+
+
+### ServerBioStream
+
+This web platform receives and stores the data collected from SmartBioStream. We have developed this tool to provide access to the data gathered during the various experiments of a case study. We define an experiment as the participation of one user in a case study. This platform is intended for researchers and administrators rather than the experiments' participants. Consequently, it supplies the following functionalities:
+
+* **JSON API.** This API receives the data emitted by one or multiple smartwatches. The messages are processed, and each measurement is stored in the database.
+* **User management.** ServerBioStream considers two roles: researchers and administrators. Researchers can connect to the platform and download the stored data. Administrators are the only ones who can create new users. Both of them have to authenticate with a username and password before accessing the web platform.
+* **Real-time data monitoring.** Researchers and administrators can visualize the data received and stored for each experiment. This feature also helps ensure the proper collection of the biometrics during the experiments.
+* **Data export.** The users of this platform can download the data collected from each experiment in CSV, XLSX, and PDF formats. This functionality enables the researchers to develop their specific analyses with the necessary software, such as Excel and Python.
+
+# Use cases
+
+In this section, we provide two use cases utilizing BioStream: one to conduct a biometric study and the other to supply biometric data to an external platform. For both case studies, we used Samsung Galaxy Watch 6 smartwatches. However, any other device with Wear OS operating system and a Wi-Fi module could be used.
+
+## Research setting
+
+We present a biometric case study as an illustrative example of how BioStream supports biometric research. For this case study, researchers collected participants' heart rates and movements (through the accelerometers). One potential application is to evaluate the effect of different stressors or tasks on participants' biometrics. During the case study, researchers can utilize SmartBioStream to monitor each experiment's biometrics in real-time. As Figure 4 shows, ServerBioStream displays two tables: the first summarizes all the experiments, and the second shows the collected data for the experiments selected in the first table. During and after the experiments, researchers can download the collected data in CSV, XLSX, and PDF formats. The downloaded files can then be analyzed using various data analysis software, such as Python or Excel. 
+
+![Data collection and visualization of a biometric case study through ServerBioStream](images/Research_data_collection.png)
+
+
+## SmartBioStream to improve cybersecurity training
+
+To illustrate the integration of SmartBioStream with end platforms, we have utilized it with a Cyber Range, an educational platform designed to provide hands-on training for cybersecurity professionals [@AlbaladejoGonzlez2025]. Effective stress management is crucial for these professionals, as they must make rapid, high-stakes decisions during cyber incidents. In the cybersecurity simulations, we measure students' heart rates using Wear OS smartwatches and SmartBioStream. This application sends the heart rates to our Cyber Range, enabling cybersecurity educators to analyze students' heart rates during the simulations. The collected heart rates are displayed in the Cyber Range as another simulation statistic. Figure 5 shows four students' heart rates and stress levels in a cybersecurity simulation.
+
+![Summary statistics of a cybersecurity simulation conducted on a Cyber Range, with heart rates collected through SmartBioStream](images/Cyber_range.png)
+
+
+
+
+# Software availability
+
+BioStream is openly available to the research community and the general public through its official repository at [https://github.com/CyberDataLab/BioStream](https://github.com/CyberDataLab/BioStream). 
+
+# Acknowledgment
+
+This work has been partially funded by PID2021-122466OB-I00 and PRE2022-102391 by MCIN/AEI/10.13039/501100011033/FEDER.
+
+# References
+