L.N. Anishchenko – Ph.D. (Eng.), Associate Professor, Senior Research Scientist, Biomedical Engineering  Department, Research Section of scientific and educational complex “Fundamental Sciences”, 

Bauman Moscow State Technical University

E-mail: anishchenko@rslab.ru

Problem statement. The need to solve the problem of detecting falls for the elderly is caused by the well-known phenomenon of global population aging, which is the result of the life expectancy increasing and the birth rate decreasing. Every year, the population over 60 years old increases by 3%. According to the UN estimates in 2030, the number of people over 60 can reach 1.4 billions. Older people are subject to negative physiological changes that can increase the risk of injuries, including those caused by falls. About 13% of all deaths of older people are the result of a lack of help after a fall episode. 

The aim of the work – the development of effective methods for detecting falls is an up-to-date medical and engineering problem. Results. We currently have wearable fall sensors on the market, and the scientific community is actively working on the development and improvement of contactless systems that use optical and non-optical sensors for automatic fall detection. However, there are still unresolved issues related primarily to the high level of false alarms, privacy violation, the high cost of the device. The bioradiolocation method is free from a number of the listed drawbacks, however, due to the relatively high cost and complexity of the hardware, the use of this method in practice has been doubting so far. The paper describes an experimental bioradar model for contactless detection of human falls and a classification algorithm based on the application of wavelet analysis and deep learning, which together make it possible to create an inexpensive portable bio-radar for remote fall detection. The architecture of the deep neural network GoogLeNet, previously trained for image recognition, was used to detect falls in the work. The time-frequency representation of the bio-radar signal, representing the absolute values of the wavelet transform coefficients, was used as input data for this network. The Morlet wavelet was used as the base wavelet. The network architecture of GoogLeNet has been adapted to solve the problem of detecting falls. Data processing was performed using the MATLAB 2019a package. The classification accuracy using the proposed algorithm for the test dataset was estimated to be 98.96%. The limitation of this work is that the experimental data used to train the classifier were obtained only for 5 subjects and for relatively small  (1–2 m) distances between the radar and the object.

Practical significance. In the course of further research, it is planned to expand the experimental dataset taking into account various surroundings and distances from the radar to a biological object.

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