scholarly journals Smart Phone Based Data Mining for Human Activity Recognition

2015 ◽  
Vol 46 ◽  
pp. 1181-1187 ◽  
Author(s):  
Girija Chetty ◽  
Matthew White ◽  
Farnaz Akther
Keyword(s):  
Data Mining ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Phone ◽  
Human Activity Recognition

Human Activity Recognition Through Wireless Body Sensor Networks (WBSN) Applying Data Mining Techniques

2021 ◽  
pp. 327-339
Author(s):  
Jimmy Alfonso Rocha ◽  
Gabriel Piñeres-Espitia ◽  
Shariq Aziz Butt ◽  
Emiro De-la-Hoz-Franco ◽  
Muhammad Imran Tariq ◽  
...  
Keyword(s):  
Data Mining ◽  
Sensor Networks ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Body Sensor Networks ◽  
Data Mining Techniques ◽  
Wireless Body Sensor Networks

scholarly journals Live Spoofing Detection for Automatic Human Activity Recognition Applications

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7339
Author(s):  
Viktor Dénes Huszár ◽  
Vamsi Kiran Adhikarla
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Smart Phone ◽  
Human Activity Recognition ◽  
Physical Activities ◽  
False Alarms ◽  
Surveillance Systems ◽  
Biometric Authentication ◽  
Lighting Conditions ◽  
Remote Server

Human Activity Recognition (HAR) has become increasingly crucial in several applications, ranging from motion-driven virtual games to automated video surveillance systems. In these applications, sensors such as smart phone cameras, web cameras or CCTV cameras are used for detecting and tracking physical activities of users. Inevitably, spoof detection in HAR is essential to prevent anomalies and false alarms. To this end, we propose a deep learning based approach that can be used to detect spoofing in various fields such as border control, institutional security and public safety by surveillance cameras. Specifically, in this work, we address the problem of detecting spoofing occurring from video replay attacks, which is more common in such applications. We present a new database containing several videos of users juggling a football, captured under different lighting conditions and using different display and capture devices. We train our models using this database and the proposed system is capable of running in parallel with the HAR algorithms in real-time. Our experimental results show that our approach precisely detects video replay spoofing attacks and generalizes well, even to other applications such as spoof detection in face biometric authentication. Results show that our approach is effective even under resizing and compression artifacts that are common in HAR applications using remote server connections.

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