TY - JOUR

T1 - Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps

AU - Shah, S.Y.

AU - Ahmad, J.

AU - Masood, F.

AU - Shah, S.Y.

AU - Pervaiz, H.

AU - Taylor, W.

AU - Imran, M.A.

AU - Abbasi, Q.H.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the 'unstable incapacity'. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.

AB - The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the 'unstable incapacity'. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.

KW - human activity

KW - machine learning

KW - patient monitoring

KW - Wandering behavior

KW - wireless sensing

KW - Brain

KW - Classification (of information)

KW - Convolutional neural networks

KW - Decision trees

KW - Deep learning

KW - Image analysis

KW - Learning systems

KW - Long short-term memory

KW - Nearest neighbor search

KW - Potable water

KW - Privacy by design

KW - Radar

KW - Support vector machines

KW - Activities of daily living (ADLs)

KW - Additive effects

KW - Data augmentation

KW - Data processing techniques

KW - Dementia patients

KW - K-nearest neighbours

KW - Machine learning techniques

KW - Privacy preserving

KW - Learning algorithms

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 - 1530-437X

IS - 3

ER -