Technology and the Elderly

Elderly and Technology

With the increasing growth of the elderly population, the focus of early prevention and real-time monitoring has become one of the essential services of elderly healthcare. When an older person falls, the hip joint is prone to damage when he or she halls, and most injuries can lead to later complications. In the past, researchers discovered that wearable devices could help with fall detection; however, the devices are mostly placed on the wrist or waist, but the wrist has a lower accuracy rate as it has to compass for multiple changes in the accelerometer as well as the compass within the device. With the research that we are developing, we are introducing many detection and service mechanisms involving an autonomous presence at the patient’s home as well as fall detection using an artificial neural network to understand and predict falls. This is expected to be 100% accurate and in real-time with not only preventive measures but immediate detection.

Introduction

Population aging is rapidly progressing worldwide. The number of older adults living alone is increasing as well.
A decrease in the number of elderlies living with their
children and an increase in the number of those living alone
after the death of their spouse are the leading causes of the
phenomena. Especially in Korea, the suicide rate of
the elderly has tripled in the last ten years. According to
the Korea National Statistical Office, the number of the
elderly aged 65 or older living alone is expected to increase
from 1.19 million in 2012 to 3.34 million in 2035.
In this regard, the medical expenses for the elderly are
rapidly increasing. According to the “Annual Health Insur-
ance Statistics for 2016″ by the National Health Insurance
Corporation and the Health Insurance Review and Assess-
me Service, the medical expenses for senior citizens aged
65 and older have risen by 13.5% over the previous year
and doubled from 2009. The rate of increase in elderly
medical care expenditure is 8 % in 2012, 9% in 2013,
10.4% in 2014, and 11.4% in 2015.
Population aging is rapidly progressing worldwide. The
the number of older adults living alone is increasing as well.
A decrease in the number of elderlies living with their
children and an increase in the number of those living alone
after the death of their spouse are the leading causes of the
phenomena. Especially in Korea, the suicide rate of
the elderly has tripled in the last ten years. According to
the Korea National Statistical Office, the number of the
elderly aged 65 or older living alone is expected to increase
from 1.19 million in 2012 to 3.34 million in 2035.
In this regard, the medical expenses for the elderly are
rapidly increasing. According to the “Annual Health Insur-
ance Statistics for 2016″ by the National Health Insurance
Corporation and the Health Insurance Review and Assess-
me Service, the medical expenses for senior citizens aged
65 and older have risen by 13.5%over the previous year
and doubled from 2009. The rate of increase in elderly
medical care expenditure is eight % in 2012, 9% in 2013,
10.4% in 2014, and 11.4% in 2015.
Population aging is rapidly progressing worldwide. The
the number of older adults living alone is increasing as well.
A decrease in the number of elderlies living with their
children and an increase in the number of those living alone
after the death of their spouse are the leading causes of the
phenomena. Especially in Korea, the suicide rate of
the elderly has tripled in the last ten years. According to
the Korea National Statistical Office, the number of the
elderly aged 65 or older living alone is expected to increase
from 1.19 million in 2012 to 3.34 million in 2035.
In this regard, the medical expenses for the elderly are
rapidly increasing. According to the “Annual Health Insur-
ance Statistics for 2016″ by the National Health Insurance
Corporation and the Health Insurance Review and Assess-
me Service, the medical expenses for senior citizens aged
65 and older have risen by 13.5%over the previous year
and doubled from 2009. The rate of increase in elderly
medical care expenditure is eight % in 2012, 9% in 2013,
10.4% in 2014, and 11.4% in 2015.

Population aging is rapidly progressing worldwide. The number of older adults living alone is increasing as well. A decrease in the number of elderlies residing with their children and an increase in the number of those living alone after the death of their spouse are the leading causes of the phenomena. Especially in Korea, the suicide rate of the elderly has tripled in the last ten years. According to the Korea National Statistical Office, the number of elderly aged 65 or older living alone is expected to increase from 1.19 million in 2012 to 3.34 million in 2035. In this regard, the medical expenses for the elderly are rapidly growing. According to the “Annual Health Insurance Statistics for 2016” by the National Health Insurance Corporation and the Health Insurance Review and Assessment Service, the medical expenses for senior citizens age 65 and older have risen by 13.5% over the previous year and doubled from 2009. The rate of increase in elderly medical care expenditure is 8% in 2012, 9% in 2013,10.4% in 2014, and 11.4% in 2015 Gellersen, H.-W., & Beigl, M. (2000).

Artificial Neural networks

A neural network is a type of machine learning which models itself after the human brain, creating an artificial neural network that, via an algorithm, allows the computer to learn by incorporating new data. While there are plenty of artificial intelligence algorithms these days, neural networks can perform what has been termed deep learning. While the basic unit of the brain is the neuron, the essential building block of an artificial neural network is a perceptron which accomplishes simple signal processing, and these are then connected into an extensive mesh network The MIT Press. (2018).

The computer with the neural network is taught to do a task by having it analyze training examples, which have been previously labeled in advance. A typical example of an assignment for a neural network using deep learning is an object recognition task, where the neural network is presented with a large number of objects of a particular type, such as a cat, or a street sign, and the computer, by analyzing the recurring patterns in the presented images, learns to categorize new photos. Unlike other algorithms, neural networks with their deep learning cannot be programmed directly for the task. Instead, they have the requirement, just like a child’s developing brain, that they need to learn the information. The learning strategies go by three methods:

  • Supervised learning: This learning strategy is the simplest, as there is a labeled dataset, which the computer goes through, and the algorithm gets modified until it can process the dataset to get the desired result.
  • Unsupervised learning: This strategy gets used in cases where there is no labeled dataset available to learn from. The neural network analyzes the dataset, and then a cost function tells the neural network how far off of target it was. The neural network then adjusts to increase the accuracy of the algorithm.
  • Reinforced learning: In this algorithm, the neural network is reinforced for positive results, and punished for a negative effect, forcing the neural network to learn over time.

A fall detection mechanism development

In this research, we will be using the Azure Cloud and the pre-built person detection and pre-built fall detection models to determine fall detection and prevention methods. These models will be the base of our in house developed model to assess fall detection with a service robot for failover data. This method will not require any devices attached to the person but using joint mapping to determine if movements are subject to fractures or misalignment. With this method, we can then decide if the user is about to fall and send the robot to either capture the fall and call emergency personnel or inform the user that the task may cause a fall again. This will only be viable in a radius view of the azure camera. If the user is way form, eth Azure Kinect, the user, would then be monitored by the robot TEMI and information will be transferred to the Azure cloud to determine a potential fall.

References

Gellersen, H.-W., & Beigl, M. (2000). Ambient Telepresence: Colleague Awareness in Smart Environments. Managing Interactions in Smart Environments, 80–88.

The MIT Press. (2018). Applying Cognitive Science to Education. Retrieved from https://mitpress.mit.edu/books/applying-cognitive-science-education

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