@inproceedings{6e4500f1265143a1886960b77fa43664,
title = "A real-time sleeping position recognition system using IMU sensor motion data",
abstract = "The emergence of wearable miniature inertial measurement unit (IMU) sensors is a powerful enabler for lying motion data extraction. Consumer wearable sleep devices with inertial measurement capability are in the market with some having limited functions such as automatic sleep detection, awakening, determination of sleep position changes and sleep efficiency. In this study, an IMU sensor is used for capturing 3D motion data. A spectrogram based algorithm for feature extraction from the motion data is proposed and implemented. Using the generated spectrogram based features, the long term short memory (LSTM) recurrent neural network (RNN) model is used for recognition of sleeping positions. The test results show that an accuracy of 99.09% can be achieved in a supervised learning mode. A real-time feature extraction and recognition system is developed to implement the proposed algorithm.",
author = "Eyobu, {Odongo Steven} and Kim, {Young Woo} and Daewoong Cha and Han, {Dong Seog}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 ; Conference date: 12-01-2018 Through 14-01-2018",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/ICCE.2018.8326209",
language = "English",
series = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--2",
editor = "Mohanty, {Saraju P.} and Peter Corcoran and Hai Li and Anirban Sengupta and Jong-Hyouk Lee",
booktitle = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
address = "United States",
}