Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer

Sensors

卷:22

Woo-Hyeong Cho¹  Sangmin Lee¹  Yeon-Wook Kim¹  Kyu-Sung Kim² 

[1] Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea;

[2] Department of Otorhinolaryngology, Inha University Hospital, Incheon 22332, Korea;

关键词: inertial measurement unit;    human activity recognition;    data augmentation;    conformer;    transformer;    convolutional neural network;   

DOI  :  10.3390/s22103932

来源: DOAJ

【 摘 要 】

Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of the transformer-based HAR model. The transformer model has a multi-head self-attention structure that can extract temporal dependency well, similar to the recurrent neural network (RNN) series while having higher computational efficiency than the RNN series. However, recent HAR studies have shown good performance by combining an RNN-series and convolutional neural network (CNN) model. Therefore, the performance of the transformer-based HAR study can be improved by adding a CNN layer that extracts local features well. The model that improved these points is the conformer-based-model model. To evaluate the proposed model, WISDM, UCI-HAR, and PAMAP2 datasets were used. A synthetic minority oversampling technique was used for the data augmentation algorithm to improve the dataset. From the experiment, the conformer-based HAR model showed better performance than baseline models: the transformer-based-model and the 1D-CNN HAR models. Moreover, the performance of the proposed algorithm was superior to that of algorithms proposed in recent similar studies which do not use RNN-series.

【 授权许可】

Unknown