|Real-time mental stress detection based on smartwatch|
L Ciabattoni, F Ferracuti, S Longhi, L Pepa, L Romeo, F Verdini
2017 IEEE International Conference on Consumer Electronics (ICCE), 110-111, 2017
|Accuracy evaluation of the kinect v2 sensor during dynamic movements in a rehabilitation scenario|
M Capecci, MG Ceravolo, F Ferracuti, S Iarlori, S Longhi, L Romeo, ...
2016 38th Annual International Conference of the IEEE Engineering in …, 2016
|Robotic retail surveying by deep learning visual and textual data|
M Paolanti, L Romeo, M Martini, A Mancini, E Frontoni, P Zingaretti
Robotics and Autonomous Systems 118, 179-188, 2019
|Physical rehabilitation exercises assessment based on hidden semi-markov model by kinect v2|
M Capecci, MG Ceravolo, F Ferracuti, S Iarlori, V Kyrki, S Longhi, ...
2016 IEEE-EMBS International Conference on Biomedical and Health Informatics …, 2016
|A Smart Sensing Architecture for Domestic Monitoring: Methodological Approach and Experimental Validation|
A Monterił, M Prist, E Frontoni, S Longhi, F Pietroni, S Casaccia, ...
Sensors 18 (7), 2310, 2018
|Machine Learning approach for Predictive Maintenance in Industry 4.0|
M Paolanti, L Romeo, A Felicetti, A Mancini, E Frontoni, J Loncarski
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded …, 2018
|A Hidden Semi-Markov Model based approach for rehabilitation exercise assessment|
M Capecci, MG Ceravolo, F Ferracuti, S Iarlori, V Kyrki, A Monterił, ...
Journal of biomedical informatics 78, 1-11, 2018
|Discovering the type 2 diabetes in electronic health records using the sparse balanced support vector machine|
M Bernardini, L Romeo, P Misericordia, E Frontoni
IEEE Journal of Biomedical and Health Informatics 24 (1), 235-246, 2019
|A sequential deep learning application for recognising human activities in smart homes|
D Liciotti, M Bernardini, L Romeo, E Frontoni
Neurocomputing 396, 501-513, 2020
|An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept|
M Capecci, MG Ceravolo, F Ferracuti, M Grugnetti, S Iarlori, S Longhi, ...
Journal of biomechanics 69, 70-80, 2018
|A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario|
M Capecci, MG Ceravolo, F D'Orazio, F Ferracuti, S Iarlori, G Lazzaro, ...
2015 37th Annual International Conference of the IEEE Engineering in …, 2015
|Evaluation of unimodal and multimodal communication cues for attracting attention in human–robot interaction|
E Torta, J van Heumen, F Piunti, L Romeo, R Cuijpers
International Journal of Social Robotics 7 (1), 89-96, 2015
|A novel computer vision based e-rehabilitation system: From gaming to therapy support|
L Ciabattoni, F Ferracuti, S Iarlori, S Longhi, L Romeo
2016 IEEE International Conference on Consumer Electronics (ICCE), 43-44, 2016
|Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0|
L Romeo, J Loncarski, M Paolanti, G Bocchini, A Mancini, E Frontoni
Expert Systems with Applications 140, 112869, 2020
|Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection|
M Paolanti, L Romeo, D Liciotti, R Pietrini, A Cenci, E Frontoni, ...
Sensors 18 (10), 3471, 2018
|Modular design of a novel wireless sensor node for smart environments|
M Grisostomi, L Ciabattoni, M Prist, L Romeo, G Ippoliti, S Longhi
2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded …, 2014
|SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0|
M Calabrese, M Cimmino, F Fiume, M Manfrin, L Romeo, S Ceccacci, ...
Information 11 (4), 202, 2020
|The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation|
M Capecci, MG Ceravolo, F Ferracuti, S Iarlori, A Monterił, L Romeo, ...
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (7 …, 2019
|Multiple Instance Learning for Emotion Recognition using Physiological Signals|
L Romeo, A Cavallo, L Pepa, N Berthouze, M Pontil
IEEE Transactions on Affective Computing, 2019
|TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records|
M Bernardini, M Morettini, L Romeo, E Frontoni, L Burattini
Computers in biology and medicine 112, 103358, 2019