TY - GEN
T1 - Indoor Positioning and Prediction in Smart Elderly Care
T2 - 20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020
AU - Liu, Yufei
AU - Fang, Xuqi
AU - Lu, Fengyuan
AU - Chen, Xuxin
AU - Huang, Xinli
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The indoor positioning and prediction technologies can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone, with the aim to predict and prevent the occurrence of emergency or health risks. In this paper, we design and implement an indoor positioning and prediction system, and apply it to smart elderly care. The system consists of two parts: hardware module and software module. The hardware module is responsible for sensing and uploading the location information of the elderly living in the room, while the software module is responsible for receiving and processing the sensing data, modeling and predicting the indoor position of the elderly based on the sensing data, so as to help find the abnormal patterns of the elderly. We also introduce a positioning prediction model that is suitable for processing IoT sensing data. With the model, one can have a more accurate and comprehensive understanding of the regularity and periodicity of the positioning time series for the elderly. To demonstrate the effectiveness and performance gains of our solution, we deploy the system in real world and setup experiments based on the indoor trajectory data collected by sensors. Extensive experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy.
AB - The indoor positioning and prediction technologies can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone, with the aim to predict and prevent the occurrence of emergency or health risks. In this paper, we design and implement an indoor positioning and prediction system, and apply it to smart elderly care. The system consists of two parts: hardware module and software module. The hardware module is responsible for sensing and uploading the location information of the elderly living in the room, while the software module is responsible for receiving and processing the sensing data, modeling and predicting the indoor position of the elderly based on the sensing data, so as to help find the abnormal patterns of the elderly. We also introduce a positioning prediction model that is suitable for processing IoT sensing data. With the model, one can have a more accurate and comprehensive understanding of the regularity and periodicity of the positioning time series for the elderly. To demonstrate the effectiveness and performance gains of our solution, we deploy the system in real world and setup experiments based on the indoor trajectory data collected by sensors. Extensive experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy.
KW - Indoor positioning and prediction
KW - Internet of Things
KW - Positioning prediction model
KW - Smart elderly-caring
KW - System
UR - https://www.scopus.com/pages/publications/85092714335
U2 - 10.1007/978-3-030-60248-2_36
DO - 10.1007/978-3-030-60248-2_36
M3 - 会议稿件
AN - SCOPUS:85092714335
SN - 9783030602475
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 537
EP - 548
BT - Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings
A2 - Qiu, Meikang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 October 2020 through 4 October 2020
ER -
