| Full text | |
| Author(s): |
Garcia, Felipe Aparecido
;
Ranieri, Caetano Mazzoni
;
Romero, Roseli A. F.
;
Colombini, EL
;
Junior, PLJD
;
Garcia, LTD
;
Goncalves, LMG
;
Sa, STD
;
Estrada, EDD
;
Botelho, SSD
Total Authors: 10
|
| Document type: | Journal article |
| Source: | 2019 LATIN AMERICAN ROBOTICS SYMPOSIUM, 2019 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR) AND 2019 WORKSHOP ON ROBOTICS IN EDUCATION (LARS-SBR-WRE 2019); v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
Human Activity Recognition (HAR) involves classifying one person's activity based on sensor data. In this work, inertial data, collected mainly from wearable sensors, are used to recognize HAR by using Convolutional Neural Networks (CNN) to extract features from the raw sensor data. Additionally, Temporal Convolutional Networks (TCN) are applied to classify the extracted features, comparing the overall performance of those layers with Long Short-Term Memory (LSTM) recurrent neural network layers. Several experiments are performed and the results show that TCN based architectures are able to outperform LSTM based architectures in sequence modeling. (AU) | |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/01687-0 - Architecture and applications for robotics in intelligent environments |
| Grantee: | Roseli Aparecida Francelin Romero |
| Support Opportunities: | Regular Research Grants |