.png){kind=icon}
Return
Smartphone-based human activity recognition using lightweight multiheaded temporal convolutional network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.120132.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sensor-based human activity recognition (HAR) has drawn extensive attention from the research community due to its potential applications in various domains, including interactive gaming, activity monitoring, healthcare, etc. Although plentiful approaches (i.e., handcrafted feature-based and deep learning methods) have been proposed throughout the years, there are still several challenges in developing an efficient and effective HAR system. For instance, handcrafted feature-based methods rely on manual feature engineering by experts and require time-consuming feature selection methods. Conversely, deep learning methods can automatically capture salient features without domain experts. However, some deep learning methods, especially Convolu-tional Neural Networks (CNN), cannot extract temporal features effectively, which are significant to motion analysis. Unlike CNN, recurrent models are exceptional at capturing temporal characteristics, but these models contain gigantic model parameters, requiring tremendous computation. This may limit the deployment of such models, especially to low-spec or embedded devices. Hence, this paper proposes a lightweight deep learning model, Lightweight Multiheaded TCN (Light-MHTCN), for human activity recognition. Light-MHTCN extracts the multiscale features of the inertial sensor signals through the parallelly organised Convolutional Heads to capture richer information. Further, integrating dilated causal convolutions and residual connections preserves longer-term dependency, which can boost the overall model performance. The performance of Light-MHTCN is assessed on three popular smartphone-based HAR databases: UCI HAR, WISDM V1 and UniMiB SHAR. With only similar to 0.21 million parameters, our lightweight model is able to achieve state-of-the-art performance with recognition accuracies of 96.47%, 99.98% and 98.63% on these databases, respectively.
Keywords:
Lightweight deep learning model
Human activity recognition
Temporal convolutional network
Dilated convolution
Multiscale feature extraction
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
ShareSaveShareSaveImproving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSave
Establishment of the Lower Boundary of the Sola of Weakly Developed Soils that Occur in Oklahoma
PREAI
ShareSave
View more