An improved BiLSTM model for daily activity recognition
Xingchen Li, Yang Feng, Dingyi Li, Yaqing Liu · 2023
Approaches and models of intelligent control have been developed to improve quality of daily life. Smart home solution has been designed to provide convenience for residents by recognizing their daily activities. In order to improve the performance of daily activity recognition, here we proposed an improved BiLSTM-Attention based daily activity recognition method which introduced dimension upgrading. Firstly, we used the feature extraction algorithm to obtain the relevant feature vectors. Secondly the feature vectors were transferred to the Bi-directional Long Short-Term Memory (BiLSTM) model for learning the time-series relationship between features and daily activity recognition. Thirdly, the BiLSTM model and attention mechanism were combined to improve the discrimination capability of daily activity features. Finally, the input data of the BiLSTM-Attention model was upgraded to improve the performance of daily activity recognition. The the F1-score of BiLSTM-Attention model was improved over the benchmark method by 5% using the public dataset CASAS_Cairo, and by 19% after the data dimension upgrading. On the public dataset CASAS_Aruba, the F1-score was improved 6% by using BiLSTM-Attention model and improved 11% by using additionally data dimension upgrading.