A speech-based convolutional neural network for human body posture classification

Abstract

In recent years, computing and sensing advances have helped to develop efficient human posture classification systems, which assist in creating health systems that contribute to enhancing elder’s and disable’s life quality in context-aware and ambient assistive living applications. Other applications of body posture classification include the simulation of human bodies into virtual characters, security applications such as kidnapping and the body position of the kidnapper or victim, which can provide useful information to the negotiator or the rescue team, and other sports applications.

Keywords

• classification of body movements
• health system
• body position detection
• mel-frequency cepstral coefficient
• correlation-based feature selection
• artificial hydrocarbon networks
• and convolutional neural network