Multilevel Transfer Learning for Complex Work Activity Recognition in Logistic Domain

Complex work activity recognition based on wearable sensors is crucial for streamlining work processes in industrial domains. Unlike basic activities such as walking or running, which involve simple repetitive motions, a complex work activity consists of discrete atomic actions such as an action of spreading a shipping label or cutting tape in a packaging task. In addition, the atomic actions sometimes involve characteristic short-term sensor data patterns. In addition, these actions can be performed in different orders by different workers to achieve similar outcomes, resulting in different long-term sensor data trends for different workers. Because multilayer networks for activity recognition may learn short-term features from shallow-level layers and long-term trends from deeper layers, we propose a new transfer learning method called multilevel knowledge transfer (MLKT), which performs level-wise source selection according to trend similarity across workers in different levels. For example, for training shallow layers, highly similar workers are selected for specific short motions (e.g., pasting a shipping label), and to train the deeper layers, workers with similar cadence are selected. This method also enables the adaptive thresholding of source data selection for each layer level during network training using the proposed adaptive level-wise discerning module.