After completing my Computer Science Degree at the University of Castilla-La Mancha, I pursued a Master's in Systems and Control Engineering at the University Institute of Distance Education. In 2021, I began a doctoral program in Advanced Information Technologies at UCLM. My Undergraduate and Master's Final Projects focused on developing sensorized insoles, a type of wearable device. These projects laid the groundwork for my doctoral thesis, which expands this research into a broader approach. The thesis explores human health aspects using non-invasive sensors in wearable devices, adopting a 'long-term' monitoring approach for continuous collection of psychomotor parameters and vital signs.
Much effort is dedicated to designing, developing, and validating wearable devices, as well as studying suitable processing techniques for the acquired time series data. From these, we construct quantitative indicators with potential applications in preventive medicine, medical rehabilitation, and assessing patients' general physiological state. The technological and research challenges addressed include integrating embedded sensors and their low-level hardware communication, implementing wireless communication protocols and synchronization between different wearables in the WBSN, and addressing computational challenges at various levels. These range from data processing on the wearable itself to using Artificial Intelligence techniques with higher computational demands on Cloud servers.
After completing my Computer Science Degree at the University of Castilla-La Mancha, I pursued a Master's in Systems and Control Engineering at the University Institute of Distance Education. In 2021, I began a doctoral program in Advanced Information Technologies at UCLM. My Undergraduate and Master's Final Projects focused on developing sensorized insoles, a type of wearable device. These projects laid the groundwork for my doctoral thesis, which expands this research into a broader approach. The thesis explores human health aspects using non-invasive sensors in wearable devices, adopting a 'long-term' monitoring approach for continuous collection of psychomotor parameters and vital signs.
Much effort is dedicated to designing, developing, and validating wearable devices, as well as studying suitable processing techniques for the acquired time series data. From these, we construct quantitative indicators with potential applications in preventive medicine, medical rehabilitation, and assessing patients' general physiological state. The technological and research challenges addressed include integrating embedded sensors and their low-level hardware communication, implementing wireless communication protocols and synchronization between different wearables in the WBSN, and addressing computational challenges at various levels. These range from data processing on the wearable itself to using Artificial Intelligence techniques with higher computational demands on Cloud servers.