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

作     者：Qiang LIN Yusheng HAO Caihong LIU Qiang LIN;Yusheng HAO;Caihong LIU

作者机构：School of mathematics and computer scienceNorthwest Minzu UniversityLanzhou 730030China Key Laboratory of Streaming Data Computing and ApplicationNorthwest Minzu UniversityLanzhou 730124China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2021年第15卷第6期

页      面：109-119页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was funded by the Fundamental Research Funds for the Central Universities(31920210013) the National Natural Science Foundation of China(Grant No.61562075) the Natural Science Foundation of Gansu Province(20JR5RA511,1506RJZA269) the Gansu Provincial First-class Discipline Program of Northwest Minzu University(11080305) the Program for Innovative Research Team of SEAC(98) 

主　　题：wandering detection assisting living Wi-Fi signal deep learning LSTM 

摘      要：Wandering is a significant indicator in the clinical diagnosis of dementia and other related diseases for *** 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 ***-Fi as an emerging sensing modality has been widely used to monitor human indoor movement in a noninvasive *** order to continuously monitor individuals’indoor motion and reliably identify wandering movement in a non-invasive manner,in this work,we develop a LSTMbased deep classification method that is able to differentiate the wandering-causedWi-Fi signal change from the ***,we first use the off-the-shelf Wi-Fi devices to capture a resident’s indoor motion information,enabling to collect a group ofWi-Fi signal streams,which will be split into variablesize ***,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 ***,experimental evaluation conducted on a group of realworld Wi-Fi signal streams shows that our proposed LSTMbased 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.