Bio
Dr Andrea Mannini is currently Assistant Professor with The BioRobotics Institute, Pisa, Italy.
He was born in Empoli in 1984, he received the M.Sc. degree in biomedical engineering from the University of Pisa, Pisa, Italy, in 2009 and the Ph.D. degree in Innovative Technologies from Scuola Superiore Sant'Anna, Pisa, in 2013. Since then, he worked as a Research Assistant with the BioRobotics Institute, Pisa until September 2017. From March to August 2012 he joined as a visiting scholar the mHealth group at the College of Computer and Information Science, Northeastern University, Boston MA, USA, where he worked on human activity classification from wrist accelerometer data.
He has been involved in international projects DeTOP(Horizon 2020), MyKI(ERC), iSUPPORT(Horizon 2020) , IDONTFALL(EU-CIP) and national projects ARLEM (FARE-MIUR) and PRIN2011 (MIUR).
He has authored two book chapters and 31 indexed international publications (14 journal papers, 17 indexed conference proceedings) and regularly serves as a reviewer for international ISI journals and for the Swiss National Science Foundation.
Currently, he is leading the Scuola Sant'Anna research unit of two projects:
- the TRAINED project "mulTifeature analysis of heaRt rate variability and gaIt features in cliNical Evaluation of Depression" aiming at quantifying the movement changes in daily life associated to depression. Three years project starting in December 2019;
- the WAVE project: "Wearable Assistant for VEterans in sport", aiming at analyzing the athletic gesture in paralympic athletes to foster their adherence to sports practice and preventing injuries. Three years project starting in February 2020.
Awards
2014: MDPI publishing: award for the most cited papers in the journal "Sensors" (4th most cited paper published in 2010)
2013: Italian Bioengineering Group Award for the best Ph.D. thesis
2011: Italian Society for Movement Analysis, best methodological paper presented at the national conference on movement analysis
2011: Italian Society for Movement Analysis, best young researcher paper presented at the national conference on movement analysis
2009: Italian Bioengineering Group Award for the best master thesis
Ricerca
His research interests are focused on machine learning algorithms applied to the human movement recognition and analysis. He is interested in innovative strategies for postures and movements classification from inertial sensors data and electromyographic recordings. Significant efforts in his works have been devoted to activity classification algorithms, applied to physical activity level assessment, to movement artefacts rejection and to the improvement of the estimates of biomechanical quantities such as energy expenditure and gait parameters.
He has been involved in international projects DeTOP(Horizon 2020), MyKI(ERC), iSUPPORT(Horizon 2020), IDONTFALL(EU-CIP) and national projects ARLEM (FARE-MIUR) and PRIN2011 (MIUR).
In such projects, he has been focusing on signal processing methods (with a particular focus on machine learning) aimed at fall detection/prevention systems, gait parameters estimation, context awareness and in the development of machine learning algorithms for upper limb prosthetics control and assessment.
Master Thesis Stages Available