Name
Title | ||
|---|---|---|
A Self-Tuned Architecture for Human Activity Recognition Based on a Dynamical Recurrence Analysis of Wearable Sensor Data |
Abstract | ||
|---|---|---|
Human activity recognition (HAR) is encountered in a plethora of applications, such as pervasive health care systems and smart homes. The majority of existing HAR techniques employs features extracted from symbolic or frequency-domain representations of the associated data, whilst ignoring completely the behavior of the underlying data generating dynamical system. To address this problem, this work proposes a novel self-tuned architecture for feature extraction and activity recognition by modeling directly the inherent dynamics of wearable sensor data in higher-dimensional phase spaces, which encode state recurrences for each individual activity. Experimental evaluation on real data of leisure activities demonstrates an improved recognition accuracy of our method when compared against a state-of-the-art motif-based approach using symbolic representations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902969 | 2019 27th European Signal Processing Conference (EUSIPCO) |
Keywords | Field | DocType |
Human activity recognition,recurrence quantification analysis,nonlinear data analysis,motif discovery,wearable sensors | ENCODE,Architecture,Activity recognition,Wearable computer,Computer science,Feature extraction,Artificial intelligence,Recurrence quantification analysis,Machine learning,Dynamical system | Conference |
ISSN | ISBN | Citations |
2219-5491 | 978-1-5386-7300-3 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M.-A. Zervou | 1 | 0 | 0.34 |
| George Tzagkarakis | 2 | 0 | 0.34 |
| Athanasia Panousopoulou | 3 | 22 | 5.36 |
| P. Tsakalides | 4 | 954 | 120.69 |