Human Identification via Gait Recognition Using Accelerometer Gyro Forces

Many applications, both cooperative and hostile, require the ability to identify humans. This paper proposes a method that exploits the biometric property of human gait to identify people. A single sensor composed of an accelerometer and gyroscope is used to record gait characteristics. Using a Direct Form II Transpose, gait cycles are extracted and compressed to their characteristic features. Using simple classifiers on these feature vectors, this approach achieves 95% accuracy in classifying gait cycles to individuals. This model-driven approach uniquely exploits the physics of human gait through the use of gyroscope forces and proves its viability for smart environment applications.