Name
Title | ||
|---|---|---|
DeepSeg: Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi |
Abstract | ||
|---|---|---|
Due to its nonintrusive character, WiFi channel state information (CSI)-based activity recognition has attracted tremendous attention in recent years. Since activity recognition performance heavily relies on activity segmentation results, a number of activity segmentation methods have been designed, and most of them focus on seeking optimal thresholds to segment activities. However, these threshold-based methods are strongly dependent on designers' experience and might suffer from performance decline when applying to the scenario, including both fine-grained and coarse-grained activities. To address these challenges, we present DeepSeg, a deep learning-based activity segmentation framework for activity recognition using WiFi signals. In this framework, we transform segmentation tasks into classification problems and propose a CNN-based activity segmentation algorithm, which can reduce the dependence on experience and address the performance degradation problem. To further enhance the overall performance, we design a feedback mechanism, where the segmentation algorithm is refined based on the feedback computed using activity recognition results. The experiments demonstrate that DeepSeg acquires remarkable gains compared with state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2020.3033173 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Activity recognition,change point detection (CPD),channel state information (CSI),segmentation,time series | Journal | 8 |
Issue | ISSN | Citations |
7 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunjing Xiao | 1 | 18 | 3.66 |
| Lei Yue | 2 | 4 | 2.45 |
| Yongsen Ma | 3 | 52 | 4.33 |
| Fan Zhou | 4 | 101 | 23.20 |
| Zhiguang Qin | 5 | 321 | 63.02 |