Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets

Kang，Hua1; Huang，Qianyi2,3; Zhang，Qian1

2022-09-07

10.1145/3550285

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies   Impact Factor & Quartile

2474-9567

Human activity recognition (HAR) plays an important role in a wide range of applications, such as health monitoring and gaming. Inertial sensors attached to body segments constitute a critical sensing system for HAR. Diverse inertial sensor datasets for HAR have been released with the intention of attracting collective efforts and saving the data collection burden. However, these datasets are heterogeneous in terms of subjects and sensor positions. The coupling of these two factors makes it hard to generalize the model to a new application scenario, where there are unseen subjects and new sensor position combinations. In this paper, we design a framework to combine heterogeneous data to learn a general representation for HAR, so that it can work for new applications. We propose an Augmented Adversarial Learning framework for HAR (AALH) to learn generalizable representations to deal with diverse combinations of sensor positions and subject discrepancies. We train an adversarial neural network to map various sensor sets' data into a common latent representation space which is domain-invariant and class-discriminative. We enrich the latent representation space by a hybrid missing strategy and complement each subject domain with a multi-domain mixup method, and they significantly improve model generalization. Experiment results on two HAR datasets demonstrate that the proposed method significantly outperforms previous methods on unseen subjects and new sensor position combinations.

Adversarial learning augmentation domain mixup generalization human activity recognition

ESCI

English

Others

National Natural Science Foundation of China[62002150];

Computer Science ; Engineering ; Telecommunications

Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000887938100027

ASSOC COMPUTING MACHINERY

2-s2.0-85139175193

Scopus

1.The Hong Kong University of Science and Technology,Hong Kong,Hong Kong
2.Southern University of Science and Technology,Shenzhen,China
3.Peng Cheng Laboratory,Shenzhen,China

Kang，Hua,Huang，Qianyi,Zhang，Qian. Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,2022,6(3).

Kang，Hua,Huang，Qianyi,&Zhang，Qian.(2022).Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,6(3).

Kang，Hua,et al."Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets".Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6.3(2022).