World is changing to ageing society. There are many facts revealing the proportion of the elders and the youngsters and most of them point out that the majority of world population in a next few decades is seniors. The number of people aged 65 or older is estimated to increase from 524 million in 2010 to nearly 1.5 billion in 2050 [1]. Several countries are confronting this situation. For example, in France, the number of seniors aged from 65 will grow from 11 million in 2012 to 18 million in 2050 [2]. Similar to other countries Thailand’s population is rapidly ageing. The population of Thai elders will rapidly increase from 9 million in 2010 to 20 million in 2050 [3].
Having more elders implies that we require more health care providers. Unfortunately, the number of health care personnel is limited and is clustered around the city. In Thailand, the potential support ratio indicating the number of working age adults available to support the elderly is expected to reduce by half in 2025 [3]. Many elders have to live alone or stay in nursing homes with insufficient caregivers and they are subjected to home accidents. One of the most dangerous and life-threatening emergencies for the elderly is fall. Fall can cause severe bone fractures, long-term suffering and eventually death. Each year, there are greater
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than 11 million people fall and it was reported in the United States that unintentional falls resulted in 56,423 hospitalizations and 7,946 related deaths in 2005 [4].
Therefore, this project targets to propose the novel framework to integrate fall detection and fall prevention in order to provide an efficient system to support elders and patients. The contribution is twofold. One is to propose a new high-efficient fall detection method by synchronizing data from a portable device and video surveillance. Another is to develop a new fall prevention system that could predict the fall risk based on personal record and moving patterns.
7. วัตถุประสงค์ของโครงการวิจัย
The purposes of this project are as follows. First it aims at applying Information and Communication Technology to support the healthcare sectors especially for the elderly and patients in two related parts: fall detection and fall prevention. The fall detection system targets to detect possible falls and alert caregivers to take an immediate action to minimize injuries and loss. Additionally, the fall prevention system acts as the preventive measure by recording personal information and walking patterns. Then the system analyzes the fall risk and suggests the elderly or patients to be more cautious as well as recommends their family members to pay more attentive care.
Second it focuses on discovering the new framework and knowledge on computer science including image processing, data mining and knowledge management that are applicable to healthcare domain. There are challenges on how to develop fast image processing technique to integrate with motion sensor data for detecting fall. Also the machine learning algorithms to cluster and classify data of users for predicting fall risk must be studied. The new discovery will be published as scientific publications of international standard.
Third due to the lack of open datasets of fall and gait, this project intends to collect a series of walking footage and simulated fall of volunteers to be compiled as a standard datasets for experimenting and published to researchers in healthcare.
Lastly the project aims at developing a real healthcare system that could be actually implemented. Mae Fah Luang University has a health science faculty and university hospital. We wish that the developed project will be partial adapted to the hospital and help improve the efficiency of healthcare services.
8. ขอบเขตของโครงการวิจัย
The subjects of the research will be around 50 volunteers including elders from local community for gait analysis and students of Mae Fah Luang University for fall experiments. The collection of data and tests will be performed in a safe environment equipped with video surveillance cameras and mobile devices. After completing the research project, there will be a workshop to train healthcare personnel to understand how to use the developed system.
9. การทบทวนวรรณกรรม ผลงานวิจัยต่างๆ ที่เกี่ยวข้อง
Realizing the inevitable ageing population and the danger of fall to the elderly, in-home assistive and surveillance system have become a hotspot recently. Numbers of automatic and
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real-time fall detections have been proposed with different techniques. Three existing fall detection approaches are observed: