Human Activity Recognition Across Scenes and Categories Based on CSI
- 发表刊物：IEEE Transactions on Mobile Computing
- 关键字：Activity recognition,meta-learning,channel state information,time encoding
- 摘要：Activity recognition based on channel state information (CSI) plays an increasingly important role in human
computer interaction. However most CSI activity recognition systems need to re-collect a large amount of samples and
retrain model when they are used in new environments or recognize new types of activities, which greatly reduces
the practicality of CSI activity recognition. To address this problem we design an adaptable CSI activity recognition
system based on meta-learning, which only needs to fine-tune model with very little train effort when it is used in new
environments or recognize new types of activities. Specifically, we first use meta-learning algorithm to get the pre-trained
model that adapts to task distribution, when the environment or activity category changes, our system doesn’t need to
retrain the model and has maximal performance after updates the pre-trained model through one or more gradient steps
computed with a small amount of samples from new activities. To prevent the loss of CSI time information after feature
extraction with multi-layer CNN, we add time encoding on CSI data as the input of CNN neural network. Considering
that CSI data may be labeled incorrectly during labeling process, we improve categorical cross entropy loss(CCE) to
enhance the system’s robustness to these mislabeled data. We test our system on the gesture dataset and the body
activity dataset, and the experimental results show that our system achieves average accuracy of 72% with 1 sample of
each new activity and 89.6% with 5 samples of each new activity.
- 论文类型：期刊论文
- 是否译文：否
- 收录刊物：SCI、EI
