@techreport{oai:ipsj.ixsq.nii.ac.jp:00239458,
 author = {Hongyin, Qiao and Hamada, Rizk and Takuya, Maekawa and Hongyin, Qiao and Hamada, Rizk and Takuya, Maekawa},
 issue = {3},
 month = {Sep},
 note = {Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks., Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks.},
 title = {Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation},
 year = {2024}
}