Rights statement: ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 3.8 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Privacy-preserving wandering behaviour sensing in dementia patients using modified logistic and dynamic Newton Leipnik maps
AU - Shah, Syed Aziz
AU - Ahmad, Jawad
AU - masood, Fawad
AU - Shah, Syed Yaseen
AU - Pervaiz, Haris
AU - Taylor, William
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer H.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Wandering behaviour is a neurological disorder experienced by elderly people with cognitive impairment, affecting normal brain functioning of the patients. In extreme cases, the patients either completely or partially loses control to perform activities of daily livings.This paper presents privacy-preserving, easily deployable wandering behaviour detection system in dementia patients by identifying their day to day activities and detecting wandering behaviour when they leave area of interest. In this context, we have used low-cost small wireless devices such as off-the-shelf Wi-Fi router and network interface card operating at 2.4 GHz. The Wi-Fi data collected is converted into scalograms using continous wavelet transform. An unsupervised deep learning algorithmnamely Autoencoder is used to classify scalgorams to identify critical event such as wandering behaviour. Modified Logistic and Dynamic Newton Leipnik Maps are used to encrypt the health record of patients to preserve the privacy. Theproposed system provided high classification of accuracy 94.2%. Furthermore, the proposed system is robust in terms of security and has been tested on a number of parameters such as correlation coefficient (0.0001), number of pixel changerate (99.9%), unified average change intensity (33.2), energy (0.01) and contrast (10).
AB - Wandering behaviour is a neurological disorder experienced by elderly people with cognitive impairment, affecting normal brain functioning of the patients. In extreme cases, the patients either completely or partially loses control to perform activities of daily livings.This paper presents privacy-preserving, easily deployable wandering behaviour detection system in dementia patients by identifying their day to day activities and detecting wandering behaviour when they leave area of interest. In this context, we have used low-cost small wireless devices such as off-the-shelf Wi-Fi router and network interface card operating at 2.4 GHz. The Wi-Fi data collected is converted into scalograms using continous wavelet transform. An unsupervised deep learning algorithmnamely Autoencoder is used to classify scalgorams to identify critical event such as wandering behaviour. Modified Logistic and Dynamic Newton Leipnik Maps are used to encrypt the health record of patients to preserve the privacy. Theproposed system provided high classification of accuracy 94.2%. Furthermore, the proposed system is robust in terms of security and has been tested on a number of parameters such as correlation coefficient (0.0001), number of pixel changerate (99.9%), unified average change intensity (33.2), energy (0.01) and contrast (10).
U2 - 10.1109/JSEN.2020.3022564
DO - 10.1109/JSEN.2020.3022564
M3 - Journal article
VL - 21
SP - 3669
EP - 3679
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1558-1748
IS - 3
ER -