Neuronal Network-Based Multiplicatively Gait Feature Eradication and Detection

Abstract

The accuracy of gait detection is affected by external influences such as intricate backdrop and coverings. To address this issue, this research presents an artificial neuronal network-based multiplicatively gait feature eradication and detection procedure. The proposed procedure uses a imaging device and a pyroelectric infrared sensor to separately acquire gait data. The skeletal attribute factor, peak variation attribute factor from the imaging device, and frequency spectrum attribute factor from the pyroelectric infrared sensor are extracted and combined for dimensionality reduction and signal processing. Finally, the merged attributes are classified and identified using a BP neuronal network as the classifier. The proposed procedure is evaluated for its detection accuracy.