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DeepSeg: Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi
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DOI：10.1109/JIOT.2020.3033173.png)
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Abstract
En 中文
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.
Keywords:
Activity recognition
Wireless fidelity
Principal component analysis
Internet of Things
Data mining
Motion segmentation
Fans
Activity recognition
change point detection (CPD)
channel state information (CSI)
segmentation
time series
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