Bidirectional LSTM Based Sequence Classification Approach for Human Presence Detection Using Channel State Information

In the recent years, deep learning techniques have received a great attention in a wide range of practical problems, including the event detection, localization, classification, and prediction. In this paper, we present a real-time bidirectional long-short term memory (BiLSTM) based human presence sensing using the WiFi channel state information (CSI) feature for non-intrusive occupancy detection systems in indoor environments by selecting best hyper-parameters. In this study, we created real-time Wi-Fi enabled Raspberry Pi computing platform to collect CSI datasets for training and testing purposes by considering different room sizes, fixed objects and different activities. On the wide variety of CSI datasets, evaluation results showed that the proposed magnitude CSI-BiLSTM based human presence detection (HPD) method had an average sensitivity (SE) of 92.23 %, specificity (SP) of 91.18 % and accuracy (ACC) of 91.68% for the 20% test CSI segments taken from the same training dataset and had an average SE of 85.71 %, SP of 91.59% and ACC of 88.67% on the unseen CSI dataset collected under different room conditions. In real-time Raspberry Pi testing environment, the proposed method had an average SE of 99.35%, SP of 94.39 % and ACC of 97.00 %. The proposed magnitude CSI-BiLSTM based HPD method had an average computational time of 1.37 ms with a model size of 509.67 kB.