Activity Recognition based on Wearable Sensors

Activity Recognition based on Wearable Sensors

Human activity recognition based on wearable sensors has received great attention in areas such as healthcare, homeland security and smart environments, mainly because it enables easy data acquisition and processing. This task consists of assigning a category of activity to signals provided by wearable sensors such as accelerometers, gyroscopes and magnetometers.

Software

Publications

1.Artur Jordão, Ricardo Barbosa Kloss, William Robson Schwartz (2018): Latent HyperNet Exploring the Layers. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018. (Type: Inproceeding | Links | BibTeX)
2.Jessica Sena, Jesimon Barreto Santos, William Robson Schwartz (2018): Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors. In: 26th European Signal Processing Conference (EUSIPCO 2018), pp. 1-5, 2018. (Type: Inproceeding | Links | BibTeX)
3.Artur Jordão, Leonardo Antônio Borges Torres, William Robson Schwartz (2018): Novel Approaches to Human Activity Recognition based on Accelerometer Data. In: Signal, Image and Video Processing, 12 (7), pp. 1387–1394, 2018. (Type: Article | Links | BibTeX)
4.Artur Jordão, Antonio C. Nazare Jr., Jessica Sena, William Robson Schwartz (2018): Human Activity Recognition Based on Wearable A Standardization of the state-of-the-art. In: arXiv, pp. 1-12, 2018. (Type: Article | Links | BibTeX)