Name
Abstract | ||
|---|---|---|
The study of preterm infants neuro-motor status can be performed by analyzing infants spontaneous movements. Nowadays, available automatic methods for assessing infants motion patterns are still limited. We present a novel pipeline for the characterization of infants spontaneous movements, which given RGB videos leverages on network analysis and NLP. First, we describe a body configuration for each frame considering landmark points on infants bodies as nodes of a network and connecting them depending on their proximity. Each configuration can be described by means of attributed graphettes. We identify each attributed graphette by a string, thus allowing to study videos as texts, i.e. sequences of strings. This allows us exploiting NLP methods as topic modelling to obtain interpretable representations. We analyze topics to describe both global and local differences in infants with normal and abnormal motion patters. We find encouraging correspondences between our results and evaluations performed by expert physicians. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-93409-5_8 | COMPLEX NETWORKS & THEIR APPLICATIONS X, VOL 1 |
Keywords | DocType | Volume |
Temporal networks, Attributed graphettes, Preterm infants motion, Markerless human motion analysis, Deep learning, Natural language processing, Latent dirichlet allocation | Conference | 1015 |
ISSN | Citations | PageRank |
1860-949X | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Garbarino | 1 | 0 | 0.34 |
| Matteo Moro | 2 | 0 | 1.35 |
| Chiara Tacchino | 3 | 0 | 0.68 |
| Paolo Moretti | 4 | 0 | 0.68 |
| Maura Casadio | 5 | 0 | 1.01 |
| Francesca Odone | 6 | 0 | 0.34 |
| Annalisa Barla | 7 | 0 | 0.34 |