Detection of Moving Direction using PIR Sensors and Deep Learning Algorithm

Jiyoung Woo; Jaeseok Yun

doi:https://doi.org/10.9708/jksci.2019.24.03.011

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2019, 24(3), pp.11-17
DOI :https://doi.org/10.9708/jksci.2019.24.03.011
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Jiyoung Woo ¹ , Jaeseok Yun ²

¹ 순천향대학교
² 순천향대학교

ABSTRACT

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by 45° increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users’ moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.
KEYWORDS

Passive infrared , movement direction detection , deep learning , machine learning , convolutional neural network
REFERENCES (18)

[journal] L. Atzori / 2010 / The Internet of Things : A survey / Computer Networks 54 (15) : 2787~2805

[journal] X. Wu / 2014 / Data Mining with Big Data / IEEE Transactions on Knowledge and Data Engineering 26 (1) : 97~107

[journal] F. Pedregosa / 2011 / Scikit-learn: Machine Learning in Python / Journal of Machine Learning Research 12 : 2825~2830

[confproc] G. D. Abowd / 1999 / Towards a Better Understanding of Context and Context-Awareness / Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing (HUC 1999) : 304~307

[other] W. Lee / 1994 / Method and Apparatus for Detecting Direction and Speed using PIR sensor / U.S. Patent No. 5,291,020

[journal] P. Zappi / 2010 / Tracking Motion Direction and Distance With PIR Sensors / IEEE Sensors Journal 10 (9) : 1486~1494

[journal] J. Yun / 2014 / Detecting Direction of Movement using PIR Sensors / IEEE Sensors Journal 14 (5) : 1482~1489

[journal] A. Krizhevsky / 2012 / ImageNet Classification with Deep Convolutional Neural Networks / Advances in Neural Information Processing Systems 25 : 1097~1105

[journal] G. Hinton / 2012 / Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups / IEEE Signal Processing Magazine 29 (6) : 82~97

[confproc] J. Ngiam / 2011 / Multimodal Deep Learning / Proceedings of the 28th International Conference on Machine Learning (ICML '11) : 689~696

[confproc] D. Castro / 2015 / Predicting Daily Activities From Egocentric Images Using Deep Learning / Proceedings of the 18th International Symposium on Wearable Computers (ISWC '15) : 75~82

[journal] K. Sundararajan / 2018 / Deep Learning for Biometrics: A Survey / ACM Computing Surveys 51 (3)

[journal] Q. Zhang / 2017 / HeartID : A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications / IEEE Access 5 : 1805~11816

[journal] R. T. Schirrmeister / 2017 / Deep learning with convolutional neural networks for EEG decoding and visualization / Human Brain Mapping 38 (11) : 5391~5420

[confproc] L. Everson / 2018 / BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG / IEEE International Symposium on Circuits and Systems (ISCAS '18) : 1~5

[journal] 홍다솔 / 2017 / Efficient Swimmer Detection Algorithm using CNN-based SVM / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 22 (12) : 79~85

[journal] 백영태 / 2017 / Intelligent missing persons index system Implementation based on the OpenCV image processing and TensorFlow Deep-running Image Processing / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 22 (1) : 15~21

[journal] Y. LeCun / 2015 / Deep Learning / Nature 38 (7533) : 436~444
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