Abstract
Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.
Highlights
Human behavior recognition has a rich and powerful role in many fields
Compared with behavior recognition methods based on video images, behavior recognition based on sensors has the characteristics of low cost, flexibility, and good portability. erefore, the research on human activity recognition based on wearable sensors has become a research hotspot in behavior recognition [1]
After the sensor signal is converted from the time domain to the frequency domain, the corresponding features are extracted from the frequency domain. e commonly used time-domain to frequencydomain conversion methods include Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT). e frequency-domain features are FFTcoefficient, DCTcoefficient, Power Spectral Density (PSD), and frequency-domain entropy (FDE) [20]
Summary
Human behavior recognition has a rich and powerful role in many fields. It can be used for health detection, fall detection, and medical assistance, and it plays an important role in human health. Erefore, the research on human activity recognition based on wearable sensors has become a research hotspot in behavior recognition [1]. E threshold analysis method is one of the main research methods in the field of human behavior recognition at home and abroad [4]. Different machine learning algorithms or threshold analysis methods are used to study the falling behavior of the elderly at home and abroad. Erefore, this paper proposes to take human movement energy expenditure a feature of fall behavior recognition. Erefore, this paper proposes a threshold analysis method to detect the occurrence of fall behavior from three aspects: human movement acceleration, human movement energy expenditure, and body tilt angle away from the vertical direction
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