Name
Title | ||
|---|---|---|
Embedded Emotion Recognition within Cyber-Physical Systems using Physiological Signals. |
Abstract | ||
|---|---|---|
Cyber-Physical Systems (CPSs) are systems designed as a network of different interacting elements, which integrate computational and physical capabilities. The human-machine interaction plays a significant role in CPSs, especially in applications where people are an active element. In this context, emotion recognition is a relevant aspect to achieve a more efficient, collaborative, and resilient machine performance in collaboration with humans. On this basis, this paper proposes an embedded machine learning approach for emotion recognition fully implemented in an ultra low-power System-on-Chip (SoC) with limited resources. To this end, the intelligence system considers a reduced set of raw physiological signals within an approximate computing focus. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DCIS.2018.8681496 | DCIS |
Keywords | Field | DocType |
Sensors,Emotion recognition,Training,Tools,Feature extraction,Biomedical monitoring,Databases | Emotion recognition,Wearable computer,Computer science,Human–computer interaction,Cyber-physical system,Approximate computing | Conference |
ISSN | ISBN | Citations |
2471-6170 | 978-1-7281-0171-2 | 4 |
PageRank | References | Authors |
0.81 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Angel Miranda Calero | 1 | 4 | 0.81 |
| Rodrigo Marino | 2 | 8 | 1.55 |
| José Manuel Lanza-Gutiérrez | 3 | 71 | 9.31 |
| Teresa Riesgo | 4 | 351 | 33.95 |
| Mario García-valderas | 5 | 19 | 5.29 |
| Celia López-ongil | 6 | 16 | 5.18 |