Related papers: A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation

A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation

URL: http://arxiv.org/abs/2303.11100v1
Date: Mon, 20 Mar 2023 13:34:28 GMT
Title: A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation
Authors: Furong Duan, Tao Zhu, Jinqiang Wang, Liming Chen, Huansheng Ning, Yaping Wan
Abstract summary: We propose a new deep neural network to solve the two tasks simultaneously. The proposed network adopts selective convolution and features multiscale windows to segment activities of long or short time durations. Our proposed method outperforms the state-of-the-art methods both for activity recognition and segmentation.
Score: 4.987833356397567
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation and recognition as two separate tasks. In practice, performing data stream segmentation is very challenging. We believe that both activity segmentation and recognition may convey unique information which can complement each other to improve the performance of the two tasks. In this paper, we firstly proposes a new multitask deep neural network to solve the two tasks simultaneously. The proposed neural network adopts selective convolution and features multiscale windows to segment activities of long or short time durations. First, multiple windows of different scales are generated to center on each unit of the feature sequence. Then, the model is trained to predict, for each window, the activity class and the offset to the true activity boundaries. Finally, overlapping windows are filtered out by non-maximum suppression, and adjacent windows of the same activity are concatenated to complete the segmentation task. Extensive experiments were conducted on eight popular benchmarking datasets, and the results show that our proposed method outperforms the state-of-the-art methods both for activity recognition and segmentation.