Wi-Fi based non-invasive detection of indoor wandering using LSTM model

Wandering is a significant indicator in the clinical diagnosis of dementia and other related diseases for elders. Reliable monitoring of long-term continuous movement in indoor setting for detection of wandering movement is challenging because most elders are prone to forget to carry or wear sensors that collect motion information daily due to their declining memory. Wi-Fi as an emerging sensing modality has been widely used to monitor human indoor movement in a non-invasive manner. In order to continuously monitor individuals’ indoor motion and reliably identify wandering movement in a non-invasive manner, in this work, we develop a LSTM-based deep classification method that is able to differentiate the wandering-caused Wi-Fi signal change from the others. Specifically, we first use the off-the-shelf Wi-Fi devices to capture a resident’s indoor motion information, enabling to collect a group of Wi-Fi signal streams, which will be split into variable-size segments. Second, the deep network LSTM is adopted to develop wandering detection method that is able to classify every variable-size segment of Wi-Fi signals into categories according to the well-known wandering spatiotemporal patterns. Last, experimental evaluation conducted on a group of real-world Wi-Fi signal streams shows that our proposed LSTM-based detection method is workable and effective to identify indoor wandering behavior, obtaining an average value of 0.9286, 0.9618, 0.9634 and 0.9619 for accuracy, precision, recall and F-1 score, respectively.