Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model

Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision owns the high robustness and accuracy for detection. However, executing CNN model on the cloud possible incurs a privacy disclosure problem during data transmission, and the high computation overhead makes difficult to execute the model on edge-end IoT devices with a well real-time performance. In this paper, we realize a skeleton-based abnormal behavior detection, and propose a secure partitioned CNN model (SP-CNN) to extract human skeleton keypoints and achieve safely collaborative computing by deploying different CNN model layers on the cloud and the IoT device. Because, the data outputted from the IoT device are processed by the several CNN layers instead of transmitting the sensitive video data, objectively it reduces the risk of privacy disclosure. Moreover, we also design an encryption method based on channel state information (CSI) to guarantee the sensitive data security. At last, we apply SP-CNN in abnormal behavior detection to evaluate its effectiveness. The experiment results illustrate that the efficiency of the abnormal behavior detection based on SP-CNN is at least 33. 2% higher than the state-of-the-art methods, and its detection accuracy arrives to 97. 54%.

Authors

• Jiefan Qiu
• Xinlei Yan
• Wei Wang
• Wei Wei
• Kai Fang

Keywords

• Computational modeling
• Convolutional neural networks
• Skeleton
• Feature extraction
• Encryption
• Internet of Things
• Channel state information
• Neural Network
• Network Model
• Convolutional Neural Network
• Artificial Neural Network
• Abnormal Behavior
• Convolutional Neural Network Model
• Detection Of Abnormalities
• Detection Accuracy
• Data Transmission
• Real-time Performance
• Video Data
• Computational Overhead
• Internet Of Things Devices
• Risk Disclosure
• Encryption Method
• Computation Time
• Feature Maps
• Cloud Computing
• K-nearest Neighbor
• Long Short-term Memory
• Local Devices
• Transmission Latency
• Image Encryption
• Front Layer
• Network Throughput
• Encryption Key
• Encrypted Data
• Convolution Operation
• Partition Model
• Communication Overhead
• Abnormal behavior detection
• partitioned CNN model
• privacy protection
• skeleton keypoints
• Humans
• Neural Networks, Computer
• Privacy
• Computer Security