Laclé, Francis

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PhD Candidate SISSTEM (Junior Researcher & Junior Lecturer)
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Computer Science
Information and Communications Technology
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Last updated September 19, 2024
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Francis Laclé is a junior researcher at the University of Aruba and a doctoral student at KU Leuven. His doctoral research lies in the cross-section between health, machine learning, and digital signal processing. The greater aim is to support the elderly on islands such as Aruba to live longer, independently, and with a good quality of life. This involves identifying specific roles that technology can play in personalized care services and prototyping novel or innovative ideas. Francis has a Master's degree in Computer Science and a Bachelor's degree in Information and Communications Technology. His passions are sustainability science, artificial intelligence, and computational teaching. He further believes that science and education are our best tools for helping us face the greatest challenges of our time.

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Now showing 1 - 3 of 3
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    A scalable geometrical model for musculotendon units
    (Wiley, 2017) Laclé, Francis; Pronost, Nicolas
    Physics-based simulation of systems such as virtual humans has benefited from recent advances in muscle actuation. However, to be manageable for motion controllers, muscles are usually solely represented by their action line, a polyline that does not include data on the tridimensional geometry of the muscle. This paper focuses on combining, by a controllable enhancement process, a functional and biomechanical model of musculotendon units with its high resolution geometrical counterpart. The method was developed in order to be invariant to spatial and polygonal configurations and to be scalable in both longitudinal and latitudinal directions. Results with 48 musculotendon units for the lower body show a drop of 84% with respect to the number of vertices when compared with the high resolution model, while maintaining the functional information. A real-time simulation experiment resulted in a runtime of 135 Hz.
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    For who and when should we intervene in other’s health: An exploratory study of Aruba as a possible case of other small island states.
    (2023) Vanrumste Bart; Sultan, Salys; Laclé, Francis
    This presentation is aimed at researchers in small island states that are studying disease burden in environments where data is limited and incomplete. We share the available data sources, tools, and analysis techniques to address some of these challenges. The research questions are what diseases or disease categories give the most burden, and when should healthcare interventions be considered. Our results are as follows. The Aruban population experienced an overall increase in life expectancy, as well as an increase in national healthcare insurance costs that proportionally coincides with population ageing. However, the burden of disease (years of life lost due to disability or disease), as defined by the World Health Organization, is not known due to lack of data . To help answer the two research questions, we studied statistical and scientific publications, and conducted outreach to the neighboring island of Curaçao. The latter led to limited mortality data between 1999 and 2015. Based on cumulative analysis we have identified a group of top four causes of death, all cardiovascular that exhibit the strongest upward trend. Non-standardized mortality ratios for all ages show that between 1999 and 2015, mortality ratios have seen a decline in this group. However, when considering yearly differences per age category we see non-linear increases from age 40 upwards. Our findings for this given period show that for this probable largest group of amenable disease, preventive human suffering can be optimally reduced by intervening at the younger age of 40. To conclude, this exploratory study shares challenges encountered and methods for practice and research that were used to overcome these challenges. This study acts as a case that could help small island researchers during the development of targeted healthcare interventions in environments that contain limited information on the burden of disease. Data collection and partnerships with all stakeholders are crucial for risk management and increasing community resilience. Data management tools, such as Supervisory Control and Data Acquisition (SCADA) and Operational Analysis Simulations of Integrated Systems (OASIS) can be leveraged to increase understanding of underlying hydrologic and management processes and vulnerabilities. SCADA systems improve efficiency, functionality and are responsible for network data communication and graphical user interfaces. OASIS is a mass balance water analytical and simulation software that captures the operating hydrologic rules and has applications in river basin management, hydropower, water supply and conflict resolution. OASIS utilizes historical data to validate hydrologic models and facilitates prompt and informed decisions in the face of the future problematic events. This talk will explore the use of these tools in increasing communication and efficacy of management plans.