3 research outputs found
Ensemble residual network-based gender and activity recognition method with signals
Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals. © 2020, Springer Science+Business Media, LLC, part of Springer Nature
WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition
In recent times, various modules such as squeeze-and-excitation, and others
have been proposed to improve the quality of features learned from wearable
sensor signals. However, these modules often cause the number of parameters to
be large, which is not suitable for building lightweight human activity
recognition models which can be easily deployed on end devices. In this
research, we propose a feature learning module, termed WSense, which uses two
1D CNN and global max pooling layers to extract similar quality features from
wearable sensor data while ignoring the difference in activity recognition
models caused by the size of the sliding window. Experiments were carried out
using CNN and ConvLSTM feature learning pipelines on a dataset obtained with a
single accelerometer (WISDM) and another obtained using the fusion of
accelerometers, gyroscopes, and magnetometers (PAMAP2) under various sliding
window sizes. A total of nine hundred sixty (960) experiments were conducted to
validate the WSense module against baselines and existing methods on the two
datasets. The results showed that the WSense module aided pipelines in learning
similar quality features and outperformed the baselines and existing models
with a minimal and uniform model size across all sliding window segmentations.
The code is available at https://github.com/AOige/WSense