Software Engineering for Healthcare (SEH) Laboratory

In the last years web, mobile, and IoT systems, as well as, machine learning-based solutions are evolving at a rapid pace and their impact on our society grows every day. Healthcare is one of the sectors that mainly benefits from these technological advances. Indeed Healthcare Informatics is one of the fastest growing economic sectors in the world today.

Given the critical role of software in Healthcare, adapting Software Engineering best practices to this novel context is absolutely relevant.

The SEH Lab was established in June 2017 with the goal of devising and evaluating approaches for improving the quality of web, mobile, and IoT-based Healthcare systems possibly including machine learning-solutions. We carry out both academic and industrial research.

In particular, the research activities includes:


  • Analysis and technological evaluation of wearable devices to be used in clinical trials
  • Analysis and classification of data recorded by wearable devices
  • Daily life activity recognition form data obtained with wearable devices
  • Integration of mobile devices (e.g., smartphones, wearables) in distributed systems for data collection
  • Software quality assurance of distributed systems, including web, mobile interface, IoT systems, and machine learning-based data analysis software

Members


Dr. Maurizio Leotta and Prof. Filippo Ricca are co-directing the SEH Lab that is partially supported by Janssen Italia (previously by Actelion Pharmaceuticals Italia). Other members of the lab include a PhD student, Andrea Fasciglione, and several Master and Bachelor students.

Various faculty members collaborate with the SEH Lab both in the fields of Machine Learning and Software Quality Assurance (see Publication list).


Recent Highlights


  • Our study on the Effects of Implementation Bugs in Machine Learning Algorithms has been accepted in the Future Generation Computer Systems (FGCS) journal
  • Our study analyzing the Reproducibility in Activity Recognition based on Wearable Devices has been accepted at the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2022)
  • Our study on Improving Activity Recognition while Reducing Misclassification of Unknown Activities has been accepted at the 30th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2021)
  • We proposed a challenge at the MAKEathon 2020 organized by the FHNW University of Applied Sciences and Arts Northwestern Switzerland. The Team that selected our challenge has been awarded with the "Best MAKEathon 2020 Award"!
  • We published on Harvard Dataverse a new open dataset recorded with medical-grade devices!

DIBRIS Unige
Main Sponsor:

Janssen Italia


azienda del gruppo Johnson & Johnson
Address: DIBRIS, Università degli Studi di Genova
Via Dodecaneso, 35
16146 Genova - ITALY

Contacts: Dr. Maurizio Leotta
maurizio [dot] leotta [at] unige [dot] it

Prof. Filippo Ricca
filippo [dot] ricca [at] unige [dot] it

Open Positions (Theses)


We are currently searching for students (both Master and Bachelor) interested in the following topics:

  • Classification of the activities performed by patients starting from data collected by wearable devices
  • Software Quality Assurance of machine learning-solutions
  • Software Quality Assurance of web, mobile, and IoT-based Healthcare systems

Detailed proposals can be found here. Contact Maurizio Leotta for more information.


Publications


International Journals and Proceedings

  1. Maurizio Leotta, Dario Olianas, Filippo Ricca.
    A Large Experimentation to Analyze the Effects of Implementation Bugs in Machine Learning Algorithms.
    Future Generation Computer Systems (FGCS), Volume 133, pp.184-200, Editor: Michela Taufer. Elsevier, 2022.
    DOI: 10.1016/j.future.2022.03.004, ISSN: 0167-739X.
  2. Andrea Fasciglione, Maurizio Leotta, Alessandro Verri.
    Reproducibility in Activity Recognition Based on Wearable Devices: a Focus on Used Datasets.
    Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 2022), 9-12 October 2022, Prague, Czech Republic, pp(8), IEEE, 2022 (accepted full paper).
  3. Andrea Fasciglione, Maurizio Leotta, Alessandro Verri.
    Improving Activity Recognition while Reducing Misclassification of Unknown Activities.
    Proceedings of 30th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2021), 27-29 October 2021, Bayonne, France, pp.153-158, IEEE, 2021.
    DOI: 10.1109/WETICE53228.2021.00039, ISBN: 978-1-6654-2789-0.
  4. Maurizio Leotta, Andrea Fasciglione, Alessandro Verri.
    Daily Living Activity Recognition Using Wearable Devices: A Features-rich Dataset and a Novel Approach. PDF     Dataset Download     MAKEathon 2020
    Proceedings of 25th International Conference on Pattern Recognition Workshops (ICPR 2021 Workshops), Milan, Italy, 10-15 January 2021, pp.171-187, Volume 12662, LNCS, Editors: A. Del Bimbo et al. Springer, 2021.
    DOI: 10.1007/978-3-030-68790-8_15, ISBN: 978-3-030-68790-8.
  5. Maurizio Leotta, Dario Olianas, Filippo Ricca, Nicoletta Noceti.
    How Do Implementation Bugs Affect the Results of Machine Learning Algorithms?
    Proceedings of 34th ACM/SIGAPP Symposium on Applied Computing (SAC 2019), Limassol, Cyprus, 8-12 April 2019, pp.1304-1313, ACM, 2019.
    DOI: 10.1145/3297280.3297411, ISBN: 978-1-4503-5933-7.
  6. Maurizio Leotta, Diego Clerissi, Dario Olianas, Filippo Ricca, Davide Ancona, Giorgio Delzanno, Luca Franceschini, Marina Ribaudo.
    An Acceptance Testing Approach for Internet of Things Systems.
    IET Software Journal, Volume 12, Issue 5, pp.430–436, Editor: Hana Chockler. IET, 2018.
    DOI: 10.1049/iet-sen.2017.0344, ISSN: 1751-8814.
  7. Diego Clerissi, Maurizio Leotta, Gianna Reggio, Filippo Ricca.
    Towards an Approach for Developing and Testing Node-RED IoT Systems.
    Proceedings of 1st ACM/SIGSOFT International Workshop on Ensemble-Based Software Engineering (EnSEmble 2018 co-located with ESEC/FSE 2018), 4 November 2018, Lake Buena Vista, FL, USA, ACM, pp.1-8, 2018.
    DOI: 10.1145/3281022.3281023, ISBN: 978-1-4503-6054-8.
  8. Maurizio Leotta, Davide Ancona, Luca Franceschini, Dario Olianas, Marina Ribaudo, Filippo Ricca.
    Towards a Runtime Verification Approach for Internet of Things Systems.
    Proceedings of 2nd International Workshop on Engineering the Web of Things (EnWoT 2018 co-located with ICWE 2018), 05 June 2018, Cáceres, Spain. pp.83-96, Volume 11153, LNCS, Editors: Cesare Pautasso, Fernando Sánchez-Figueroa, Kari Systä, Juan Manuel Murillo Rodríguez. Springer, 2018.
    DOI: 10.1007/978-3-030-03056-8_8, ISBN: 978-3-030-03055-1.
  9. Maurizio Leotta, Filippo Ricca, Diego Clerissi, Davide Ancona, Giorgio Delzanno, Marina Ribaudo, Luca Franceschini.
    Towards an Acceptance Testing Approach for Internet of Things Systems. PDF
    Proceedings of 1st International Workshop on Engineering the Web of Things (EnWoT 2017 co-located with ICWE 2017), 05 June 2017, Roma, Italy, pp.125-138, Volume 10544, LNCS, Editors: Irene Garrigós, Manuel Wimmer. Springer, 2018.
    DOI: 10.1007/978-3-319-74433-9_11, ISBN: 978-3-319-74432-2.
Theses

  • Andrea Fasciglione.
    Recognizing Activities of Daily Living Using a Wearable Device and Machine Learning.
    MSc in Computer Science, University of Genova, 2020.
  • Dario Olianas.
    An Automated Acceptance Testing Approach for Internet of Things Systems.
    MSc in Computer Science, University of Genova, 2019.
  • Filippo Vaccaro.
    Towards an Approach to Gait Analysis based on a Wearable Device.
    BSc in Computer Science, University of Genova, 2019.
  • Simone Maddaleno.
    Gait Analysis based on a Wearable Device: Effect of Placement and Road Inclination.
    BSc in Computer Science, University of Genova, 2018.
  • Stefano Castello.
    Study and Testing of a Wearable Device and of the Corresponding Actigraphy Data Analysis Software Platform.
    BSc in Computer Science, University of Genova, 2018.