Research Area
Human activity recognition, artificial intelligence.
Current Research Project(s):
- WEALTH (Wearable sensor assessment of physical and eating behaviours)
- BIOCLITE (Digital biomarkers for motor status assessment of Parkinson's disease patients for clinical and therapeutic application)
Ph.D. in Mechanical Engineering, Master in Big Data and Acoustic Engineering (Technical University of Madrid and Analytics from the European University of Madrid, respectively). My research involves the automatic detection of human movement using wearable sensors and artificial intelligence, applied to neurogenerative diseases and physical activity monitoring. Currently I'm working, as post-doctoral researcher in the WEALTH (Wearable sensor assessment of physical and eating behaviours), and the BIOCLITE (Digital biomarkers for motor status assessment of Parkinson's disease patients for clinical and therapeutic application).
Publications
Sigcha, L., Borzì, L., & Olmo, G. (2024) Deep Learning Algorithms for Detecting Freezing of Gait in Parkinson's Disease: A Cross-Dataset Study. Preprint Available at SSRN 4745439. DOI: 10.2139/ssrn.4745439
Sigcha, L., et al. (2023) Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review. Expert Systems with Applications 23(120541) DOI: https://doi.org/10.1016/j.eswa.2023.120541
Sigcha, L., et al. (2023). "Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones". Frontiers in Neurology, 14, 1326640. https://doi.org/10.3389/fneur.2023.1326640.