Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Joana Silva (Author) Other; Inês Sousa (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA, July 18-21, 2018

Pages: 3509-3512

Indexing

Scopus - 2 Citations

INSPEC

Other information

Authenticus ID: P-00P-YC5

DOI: 10.1109/embc.2018.8513001

Abstract (EN): Falls are very rare and extremely difficult to acquire in free living conditions. Due to this, most of prior work on fall detection has focused on simulated datasets acquired in scenarios that mimic the real-world context, however, the validation of systems trained with simulated falls remains unclear. This work presents a transfer learning approach for combining a dataset of simulated falls and non-falls, obtained from young volunteers, with the real-world FARSEEING dataset, in order to train a set of supervised classifiers for discriminating between falls and non-falls events. The objective is to analyze if a combination of simulated and real falls could enrich the model. In the real-world, falls are a sporadic event, which results in imbalanced datasets. In this work, several methods for imbalance learning were employed: SMOTE, Balance Cascade and Ranking models. The Balance Cascade obtained less misclassifications in the validation set.There was an improvement when mixing the real falls and simulated non-falls compared to the case when only simulated falls were used for training. When testing with a mixed set with real falls and simulated non-falls, it is even more important to train with a mixed set. Moreover, it was possible to onclude that a model trained with simulated falls generalize better when tested with real falls, than the opposite. The overall accuracy obtained for the combination of different datasets were above 95 %. © 2018 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4