Most falls are caused by a combination of risk factors. The more risk factors a person has, the greater their chances of falling. Healthcare providers can help cut down a person’s risk by reducing the fall risk factors listed above.

· About one third of the elder population over the age of 65 falls each year, and the risk of falls increases proportionately with age. At 80 years, over half of seniors fall annually. · As alarming as they are, these documented statistics fall short of the actual number since many incidents are unreported by seniors and unrecognized by family members or caregivers. · Frequent falling. Those who fall are two to three times more likely to fall again. · About half (53%) of the older adults who are discharged for fall-related hip fractures will experience another fall with in six months.

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Every year, several thousands of elderly people experience with falling accident. Falling is then a main problem about healthiness of elders. This paper tries to find out a simple algorithm to detect a fall. With less calculation, the device can quickly distinguish between a fall and a normal activity of daily living (ADL). As the smartphone technology is currently in very advance, it includes several sensors to come along. The sensors building in the smartphone are very useful in every field of measurements even in medical engineering. The tri-axial accelerometer is one sensor available on the smartphone and one application is to use for fall detection. From the study, the simple algorithm can be applied for fall detection by observing any change of x-, y-, or z-acceleration 10g within time limited obtaining from ADLs in terms of lying down. The advantages of using the smartphone as a fall detector are that it can alarm or call out for help. It is also getting cheap, widely used, and comfortable to use or mount.

Fall perception for elderly care: A fall detection algorithm in Smart Wristlet mHealth system Z. Li ;mHealth Lab., Peking Univ., Beijing, China ; A. Huang ; W. Xu ; W. Hu Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level. 8.Fall detection algorithm in energy efficient multistate sensor system G. Korats ;Ventspils Univ. Coll., Ventspils, Latvia ; J. Hofmanis ; A. Skorodumovs ; E. Avots Health issues for elderly people may lead to different injuries obtained during simple activities of daily living (ADL). Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. Many fall detection systems are proposed but only recently such health care systems became available. Nevertheless sensor design, accuracy as well as energy consumption efficiency can be improved. In this paper we present a single 3-axial accelerometer energy-efficient sensor system. Power saving is achieved by selective event processing triggered by fall detection procedure. The results in our simulations show 100% accuracy when the threshold parameters are chosen correctly. Estimated energy consumption seems to extend battery life significantly.


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