Characterization of postures to analyze people’s emotions using Kinect technology
This article synthesizes the research undertaken into the use of classification techniques that characterize people's positions, the objective being to identify emotions (astonishment, anger, happiness and sadness). We used a three-phase exploratory research methodology, which resulted in techn...
- Autores:
-
Monsalve-Pulido, Julián Alberto
Parra-Rodríguez, Carlos Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/68524
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/68524
http://bdigital.unal.edu.co/69557/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
análisis de emociones
reconocimiento de posturas
software libre
Kinect
KNN
analysis of emotions
recognition of postures
free software
Kinect
KNN
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This article synthesizes the research undertaken into the use of classification techniques that characterize people's positions, the objective being to identify emotions (astonishment, anger, happiness and sadness). We used a three-phase exploratory research methodology, which resulted in technological appropriation and a model that classified people’s emotions (in standing position) using the Kinect Skeletal Tracking algorithm, which is a free software. We proposed a feature vector for pattern recognition using classification techniques such as SVM, KNN, and Bayesian Networks for 17,882 pieces of data that were obtained in a 14-person training sample. As a result, we found that that the KNN algorithm has a maximum effectiveness of 89.0466%, which surpasses the other selected algorithms. |
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