Action Recognition with PIR Sensor Array and Bidirectional Long Short-term Memory Neural Network

The spatio-temporal sequence of human body movements provides important information about daily action patterns. This article presents a pyroelectric infrared (PIR) sensor array for detecting human motion features and develops a bidirectional long short-term memory (LSTM) neural network for sequence...

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Bibliographic Details
Published inIEEE ... International Conference on Cloud Computing and Intelligence Systems pp. 284 - 288
Main Authors Liu, Tong, Liang, Jianchu, Wan, Kai, Liu, Jun
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.08.2023
Subjects
Action recognition
Bidirectional long short-term memory
Distributed databases
Feature extraction
Force
Neural networks
Pyroelectric infrared sensors
Sensors
Sociology
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Summary:The spatio-temporal sequence of human body movements provides important information about daily action patterns. This article presents a pyroelectric infrared (PIR) sensor array for detecting human motion features and develops a bidirectional long short-term memory (LSTM) neural network for sequence recognition. A PIR sensor array with five direction-sensitive modules is proposed to detect the changing infrared field induced by the human head, upper limb, and lower limb. The low-dimensional output of the sensor array is directly fed to a multi-layer bidirectional LSTM-based neural network for sequential dependency learning. A PIR data set of seven daily actions is generated by four subjects imitating the predefined movements. Experimental results demonstrate that the presented approach achieves high accuracy in action recognition.
ISSN:2376-595X
DOI:10.1109/CCIS59572.2023.10263049