title: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¸Ð½Ð°Ð´Ð»ÐµÐ¶Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ðº Ð½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ¼ Ð² Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ Ð¿ÐµÑÐµÐ¼ÐµÑÐµÐ½Ð¸Ð¹ Ð¸ ÑÐ°Ð±Ð¾Ñ Ð² Ð¾Ð¿Ð°ÑÐ½ÑÑ Ð·Ð¾Ð½Ð°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Automatic identification of mobile tags holders in the industrial problem of monitoring movements and work in danger zones
Creators: name: ÐÑÐ´ÑÑÐ²ÑÐµÐ²Ð°, ÐÐ°ÑÐ¸Ð»Ð¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð ÑÐ´Ñ (Ð¼Ð°Ñ.) Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
deep learning

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°Â­ ÑÐ¸Ð¸ Ð¼ÐµÑÑÐ° ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð¹ Ð¼ÐµÑÐºÐ¸ Ð½Ð° ÑÐµÐ»Ð¾Ð²ÐµÐºÐµ Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼, Ð¿Ð¾ÑÑÑÐ¿Ð°ÑÑÐ¸Ð¼ Ñ Ð°ÐºÑÐµÐ»ÐµÑÐ¾Ð¼ÐµÑÑÐ° Ð¸ Ð³Ð¸ÑÐ¾ÑÐºÐ¾Ð¿Ð°. Ð¡ÐµÑÑ Ð¾Ð±ÑÑÐ°ÐµÑÑÑ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÑÑÑÐ¸Ñ ÑÐ¾Ð±Ð¾Ð¹ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ ÑÑÐ´Ñ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¾Ð¹ Ð´Ð»Ð¸Ð½Ñ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐµÐ¹ BiLSTM Ð¸ DeepConvLSTM. BiLSTM Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÑÐ¸ÑÑÐ²Ð°ÑÑ Ð´Ð¾Ð»Ð³Ð¾ÑÑÐ¾ÑÐ½ÑÐµ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¼ÐµÐ¶Ð´Ñ ÑÐ°Ð³Ð°Ð¼Ð¸ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð¸ Ð¸Ð·Ð²Ð»ÐµÐºÐ°ÑÑ Ð±Ð¾Ð»ÑÑÐµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð·Ð° ÑÑÐµÑ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð° Ð² Ð´Ð²ÑÑ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸ÑÑ. DeepConvLSTM Ð¸Ð¼ÐµÐµÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ ÑÐ»Ð¾Ð¸, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ðµ Ð¸Ð·Ð²Ð»ÐµÐºÐ°ÑÑ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð·Ð° ÑÑÐµÑ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²Ð·Ð°Ð¸Ð¼Ð¾ÑÐ²ÑÐ·ÐµÐ¹ Ð¼ÐµÐ¶Ð´Ñ ÐºÐ¾ÑÐ¾ÑÐºÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐ¼Ð¸ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÑÐ¼Ð¸ Ð²Ð½ÑÑÑÐ¸ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð°. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ, Ð¾ÑÐ½Ð¾ÑÑÑÐ¸ÑÑÑ Ðº Ð´Ð²ÑÐ¼ ÐºÐ»Ð°ÑÑÐ°Ð¼ Ð¿Ð¾ Ð¼ÐµÑÑÐ°Ð¼ ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐºÐ¸. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½Â­ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ Ð´Ð»Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ð¹ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÐµÐ³Ð¾ ÑÑÐµÐ´ÑÑÐ²Ð¾ Ð¸Ð½Ð´Ð¸Ð²Ð¸Ð´ÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð·Ð°ÑÐ¸ÑÑ Ñ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð¹ Ð¼ÐµÑÐºÐ¾Ð¹, Ð½Ð° ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼ÐµÑÑÐ° ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐºÐ¸. ÐÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð½Ðµ Ð·Ð°Ð²Ð¸ÑÐ¸Ñ Ð¾Ñ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð² Ð¸Ð½Ð´ÑÑÑÑÐ¸Ð°Ð»ÑÂ­Ð½Ð¾Ð¹ Ð¾Ð±ÑÑÐ°Ð½Ð¾Ð²ÐºÐ¸. ÐÐ°ÑÐµÑÑÐ²Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ»ÑÑÑÐ°ÐµÑÑÑ Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð¿Ð¾ ÐºÐ¾ÑÐ¾ÑÑÐ¼ ÑÐ°Ð·Ð»Ð¸ÑÐ¸Ð¼Ñ Ð´Ð°Ð½Ð½ÑÐµ Ñ Ð´Ð°ÑÑÐ¸ÐºÐ¾Ð², Ð½Ð°ÑÐ¾Ð´ÑÑÐ¸ÑÑÑ Ð² ÑÐ°Ð·Ð½ÑÑ Ð¼ÐµÑÑÐ°Ñ ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸Ñ.
description: This work is devoted to the construction of a neural network model for classifying mobile tag placement on a person based on data coming from an accelerometer and gyroscope. The network is trained on data representing time series of relatively short length. The model architecture uses recurrent neural networks for processing sequences like BiLSTM and DeepConvLSTM. BiLSTM allows to take into account long-term dependencies between time series steps and extract more information by processing the time series in two directions. DeepConvLSTM has convolutional layers that allow extracting additional information by analyzing the dependencies between short-term sequences within a time series. This paper presents the results of computational experiments on data belonging to two classes according to the placement of the tag. The experiments were conducted to investigate the effect of the actions of a person using a personal protective equipment with a mobile tag on the quality of the tag placement classification. The proposed method does not depend on the factors of the industrial situation. The quality of the model is improved by using synthetic features, by which data from sensors located in different placements are distinguishable.