상세정보
학술지
Sensor-data augmentation for human activity recognition with time-warping and data masking
- 발행일
- 202106
- 출처
- Multimedia Tools and Applications, v.80 no.14, pp.20991-21009
- ISSN
- 1380-7501
- 출판사
- Springer
- 협약과제
- 20HS1300, 신체기능의 이상이나 저하를 극복하기 위한 휴먼 청각 및 근력 증강 원천 기술 개발, 신형철
- 초록
- Human activity recognition (HAR) using an accelerometer can provide valuable information for understanding user context. Therefore, several studies have been conducted using deep learning to increase the recognition rate of activity classification. However, the existing dataset that is publicly available for HAR tasks contains limited data. Previous works have applied data augmentation methods that simply transform the entire accelerometer-signal dataset. However, the label of the augmented signal cannot be easily recognized by humans, and the augmentation methods cannot ensure that the label of the signal is preserved. Therefore, we propose a novel data augmentation method that reflects the characteristics of the sensor signal and can preserve the label of the augmented signal by generating partially occluded data of the accelerometer signals. To generate the augmented data, we apply time-warping, which deforms the time-series data in the time direction. We handle jittering effects and subsequently apply data masking to drop out a part of the input signals. We compare the performance of the proposed augmentation method with that of conventional methods by using two public datasets and an activity recognition model based on convolutional neural networks. The experimental results show that the proposed augmentation method improves the recognition rate of the activity classification model, regardless of the dataset. Additionally, the proposed method shows superior performance over conventional methods on the two datasets.
- KSP 제안 키워드
- Accelerometer signal, Augmentation method, Classification models, Conventional methods, Convolution neural network(CNN), Data Augmentation, Data masking, Drop-out, Human activity recognition(HAR), Limited data, Public Datasets