Elderly Behavior Prediction Using a Deep Learning Model in Smart Homes

Elderly Behavior Prediction Using a Deep Learning Model in Smart Homes

Sridevi U. K., Sophia Sudhir, Shanthi Palaniappan
DOI: 10.4018/978-1-7998-2101-4.ch008
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The goal of a smart home is to keep track of the behaviors of the older adults with disabilities within the home, and then anticipate their activity to help with other actions. Elderly and disabled people have problems with their daily lives, while most other people are unaware of their difficulties. Helping the elderly to live independently allows them to lead their daily lives in a better manner. The implementation of analytics and machine learning algorithms leads to a predictive approach to health care services. In this chapter, a learning model in a smart home concept focuses on making it possible for the elderly to remain safe and comfortable at home. The transformative home security device learning architecture of the smart home platform is a valuable solution to studying mobility patterns at home, with the ability to identify behavioral changes related to issues of wellbeing. A predictive learning system can effectively recognize and identify the behavior of the elderly. A learning model, a recurrent neural network (RNN) is proposed to evaluate the people's activity. The focus of the present study is to forecast the deterioration in mental function and give warnings for the benefit of seniors.
Chapter Preview
Top

Introduction

The elderly who live independently are the main issue and the control of daily living practices helps assess their ability to care for them and ensure better living life. In the case of elderly patients, there is always a need for constant monitoring and rapid diagnosis. Daily living tasks, also known as physical daily activity of living (ADL) include essential skills normally required to manage their daily activities. These responsible abilities are learned at the start of life and are generally retained in contrast to higher-level tasks in the face of deteriorating cognitive functioning. Specific ADLs are typically classified separately from Daily Living Instrumental Activities (IADLs), which include more specific task depends on the cognitive ability of the brain. IADL task is prone to early neuro degeneration in comparison to regular activity is often an important driver of core ADL skills. IADL deficiency shows slight cognitive decline and Alzheimer’s can indeed develop, although simple ADL changes often do not occur until later stage of dementia. Regular Living Ability Evaluation Instrumental Training helps to assess independent living. The assessment of ADL, IADL, relies on psychological variables and forecasting helps to assess the independent living. Successful longevity, autonomy and on-site aging capability include sustaining and preserving the physical, mental and social well-being of individuals. ADLs are used to describe a situation that may or may not be critical to the capacity of a person to live comfortably. The cognitive impairment has different impact on ADL and IADL activity. The low cognitive persons will have difficulty in performing the ADL activities. As the old people segment grows, the method to identify the cognitive decline is essential. Smart homes are capable of tracking and supporting people by monitoring their activities. The health care services can be improved by proving better living environment.

ADLs could include the dressing, getting up in the morning, drug administration, self-clean, and their related activities. A common concern amongst older people, especially loved ones becomes a responsibility on everyone else. Changes in activity will estimate further depressive symptoms with important implications for probable treatment arrangements. Rising reliance can imply the need of a care conversation among doctors, the patient and their family to explain the wishes of the patient. The alert message is needed if they are unable to meet their basic needs. Elderly people need to retain the potential for both day-to-day living and day-to-day living practices. In recent decades, medical advancements and technology, education and healthcare welfare, combined with increased knowledge of nutrition have paved the way for a significant increase in global lifespan. The monitoring devices in the home technology system is capable of tracking people’s well-being and everyday behaviors and smart alert system enhance their standard of living.

Developing easy and inexpensive healthcare solution is essential to meet the increasing need for medical services for the old people. Elderly people may need medical care regularly, immediately, which otherwise could have fatal consequences. Continuous monitoring of older people's medical parameters and activities may avoid such emergencies conditions. Smart homes will allow older people to stay in safer home rather than expensive and restricted health facilities. Medical care professionals can also track older people’s general health status in real time and even provide support and advice from remote services. Miniaturized and low-cost monitoring systems, embedded systems and wireless networking technology have been introduced. Remote health monitoring allows inoffensive, pervasive and real activities of person. Able to monitor everyday living habits and recognizing shifts in past trends is important to assess an elderly individual’s ability to live habits and recognizing shifts in past trends is important to assess an elderly individual’s ability to live in freedom in their environment and identify potential important situations earlier on.

Complete Chapter List

Search this Book:
Reset