A Novel Method for Cross-Subject Human Activity Recognition with Wearable Sensors  

作　　者：Qi Zhang Feng Jiang Xun Wang Jinnan Duan Xiulai Wang Ningling Ma Yutao Zhang Qi Zhang;Feng Jiang;Xun Wang;Jinnan Duan;Xiulai Wang;Ningling Ma;Yutao Zhang(Faculty of Computing, Harbin Institute of Technology, Harbin, China;School of Future Technology, Nanjing University of Information Science & Technology, Nanjing, China;School of Medicine and Health, Harbin Institute of Technology, Harbin, China;Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China)

机构地区：[1]Faculty of Computing, Harbin Institute of Technology, Harbin, China [2]School of Future Technology, Nanjing University of Information Science & Technology, Nanjing, China [3]School of Medicine and Health, Harbin Institute of Technology, Harbin, China [4]Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China

出　　处：《Journal of Sensor Technology》2024年第2期17-34,共18页传感技术（英文）

摘　　要：Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.

关 键 词：Human Activity Recognition Cross-Subject Adaptation Semi-Supervised Learning Wearable Sensors 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]