Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition

In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (13), p.2899
Main Authors: Heo, Kuk Ho, Jeong, Seol Young, Kang, Soon Ju
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biometric recognition systems
Biometrics
Computer Security
Dementia
Fault diagnosis
Feature recognition
Humans
Internet of Things
IoT Smart Home
Methods
Neural networks
Older people
Preprocessing
Pressure distribution
Principal components analysis
Radio frequency identification
Real time
real-time identification
Sensors
Smart buildings
user and behavior recognition
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Summary:In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19132899