Real-Time Analysis of Youth Emotion Based on Python Language and Smart Sensor Network

Adolescents’ emotional changes will have a huge impact on themselves; perhaps, they do not understand themselves. However, according to research, many behaviors of adolescents are often accompanied by emotional changes, and the occurrence of these changes will also bring about their unconsciousness....

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Veröffentlicht in:Mobile information systems Jg. 2022; S. 1 - 15
1. Verfasser: Yan, Qilin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Hindawi 23.04.2022
John Wiley & Sons, Inc
Schlagworte:
Adolescents
Algorithms
Application programming interface
Computer architecture
Data processing
Data transmission
Emotional factors
Emotions
Hardware
Neural networks
Programming languages
Real time
Recognition
Smart buildings
Smart sensors
Software
Training
Unconsciousness
User interface
Wireless networks
Wireless sensor networks
Young adults
ISSN:1574-017X, 1875-905X
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Zusammenfassung:Adolescents’ emotional changes will have a huge impact on themselves; perhaps, they do not understand themselves. However, according to research, many behaviors of adolescents are often accompanied by emotional changes, and the occurrence of these changes will also bring about their unconsciousness. This article first introduces the research background, significance, and development status of smart home sensors and young people’s emotions at home and abroad. This article then gives a detailed introduction to the Python language, intelligent sensor networks, and real-time analysis of youth emotions. In the introduction, it mainly explains the design of the intelligent sensor network system and introduces the system architecture and software and hardware design of the wireless sensor network in detail. In the hardware part, it mainly gives a brief overview of information collection, data transmission, and data processing. In the software part, the embedded software design of three types of network nodes and the control center software design based on Python are given. Finally, the neural network algorithm is used to realize the real-time analysis of young people’s emotions, and the recognition rate of multiple algorithms and the data situation of multiple emotional factors are tested at the same time. The results show that the highest recognition rate of 58.4% can be achieved on the validation set of the HAPPEI database after preprocessing, which is higher than the recognition result obtained by directly training the network using the training set of the HAPPEI database.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1574-017X
1875-905X
DOI:10.1155/2022/8635787