Abstract
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.
Highlights
Abstract —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’
The raw channel state information (CSI) data received using off-the-shelf NIC was connected through wireless medium to a Wi-Fi router operating at 2.4 GHz is discussed in detailed as follows.The data packets were obtained with multiple orthogonal frequency subcarriers from the Internet Control Messages Protocol (ICMP) data stream
This article presented a novel system for monitoring of wandering behaviour in dementia patients and reported abnormal events exploiting using low-cost small wireless devices operating at 2.4 GHz
Summary
A LZHEIMER disease (AD) is the most common by numerous cognitive deficiencies such as (temporary) loss symptoms experienced by dementia patients that are of memory, decline in physical behaviour and slowing down. A sensor-based system enables the dementia patients to live safely These sensors include movement detection systems, non-invasive fall detectors and vital sign monitoring. These device can record the patients ADLs along their daily routine habits [5] and identity behavioral abnormalities. The conventional data set access schemes enable authorized people to decrypt particular subject’s sensitive healthcare record, it adversely affects the first-response treatment as the life of a certain patient is threatened due to the onsite care-provider not allowed to access healthcare historical data In this context, we propose a novel, privacy-preserving, non-invasive intelligent healthcare system for wandering behaviour in dementia patient. Modified Logistic and Dynamic Newton Leipnik maps for scalogram encryption
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