Adaptive transfer learning framework for dense prediction of human activity recognition

• 作 者: Zhang Zhao Zhang Yong Teng Yinglei Guo Da Deng Haiqin
• 机 构: School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876
• 出 处: 中国邮电高校学报（英文版）
• 发表日期: 2019年5期
• 关键词: transfer learning human activity recognition dense prediction global domain adap

Human activity recognition (HAR) for dense prediction is proven to be of good performance,but it relies on labeling every point in time series with the high cost.In addition,the performance of HAR model will show significant degradation when tested on the sensor data with different distribution from the training data,where the training data and the test data are usually collected from different sensor locations or sensor users.Therefore,the adaptive transfer learning framework for dense prediction of HAR is introduced to implement cross-domain transfer,where the proposed multi-level unsupervised domain adaptation (MLUDA) approach combines the global domain adaptation and the specific task adaptation to adapt the source and target domain in multiple levels.The multiconnected global domain adaptation architecture is proposed for the first time,which can adapt the output layer of the encoder and the decoder in dense prediction model.After this,the specific task adaptation is proposed to ensure alignment of each class centroid in source domain and target domain by introducing the cosine distance loss and the moving average method.Experiments on three public HAR datasets demonstrate that the proposed MLUDA improves the prediction accuracy of target data by 20％ compared to the source domain pre-trained model and it is more effective than the other three deep transfer learning methods with an improvement of 10％ to 18％ in accuracy.