Neural Network-Based Multiplicatively Gait Feature Eradication and Detection

This research proposes an innovative method based on machine learnings for extracting and identifying gait features from multiple sources. The method aims to enhance the accuracy of gait identification by minimizing interferences caused by complex backgrounds and shelters, thereby capturing more precise information that reflects the walking characteristics of moving individuals. The technical approach involves the acquisition of gait data using a video recorder and a pyroelectric IR sensor. The image source information obtained from the video recorder is utilized to extract skeleton feature variables and Radon difference peak characteristic variables. In addition, the pyroelectric IR source information is transformed from a voltage signal to frequency domain characteristic variables. These variables are then merged after undergoing dimension reduction and signal processing. Finally, a backpropagation neural network is employed as the classifier to perform classified identification based on the merged characteristics, and the identification accuracy is evaluated. The primary application of this method is in the field of identification

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)