Human fall detection method based on triaxial acceleration

Abstract

The invention discloses a human fall detection method based on triaxial acceleration. The method includes acquiring an acceleration set composed of all accelerations in two seconds before and all accelerations in a current second; determining an acceleration sequence for indicating suspected fall according to the acceleration set; acquiring a feature vector of the acceleration sequence for indicating the suspected fall according to the acceleration set; calculating a fall decision value according to the feature vector; judging whether a human body falls or not according to the fall decision value; and judging whether the fell human body is able to self-rescue or not according to an acceleration which is acquired at any acquisition time in alarm judgment time following all the currently acquired accelerations. The method is simple to implement, easy to calculate and high in detection accuracy.

Description

technical field [0001] The invention relates to a human body fall detection technology, in particular to a human body fall detection method based on triaxial acceleration. Background technique [0002] Falling is one of the main causes of accidental injuries to people in daily life. The condition tends to be more serious when the faller is an older person. According to the survey of the willingness of the elderly to provide for the elderly by the civil affairs department, more than 90% of the elderly still hope to provide for the elderly at home, and the traditional model of children taking care of the elderly is now difficult to cope with the growing demand for the elderly, which has resulted in a large number of elderly living alone. Without the care of relatives, the elderly living alone often cannot get timely and effective assistance when they fall accidentally. [0003] In daily life, there are two main reasons for falls: one is accidental falls or slips during actio...

AI Technical Summary

Problems solved by technology

[0005] In the fall detection technology based on wearable sensors, the most widely used fall detection methods are mainly the method of judging the threshold value and the method of using pattern recognition. The former is simple and effective, but the recognition accuracy rate is difficult to reach the standard of practical application; The latter is relatively good in terms of correctness, but the detection process is too complicated to be suitable for the operation of single-chip microcomputers, and there is still room for improvement in terms of correctness

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