Wearable Sensor-Based Biometric Gait Classification Algorithm Using WEKA

• DOI: 10.6109/jicce.2016.14.1.045
• Publication Date: 2016-04-05T04:40:03Z
• Authors: Ik-Hyun Youn; Kwanghee Won; Jong-Hoon Youn; Jeremy Scheffler
• Keywords: Identification; Statistical classification

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques mitigate the natural variations in gait among different subjects. We incorporated several algorithms into this using data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us apply various sets of understand whether each algorithm can capture certain distinctive features. First, we defined 24 features by analyzing three-axis acceleration data, and then selectively used them distinguishing subjects 10 years age or younger from those aged 20 40. also applied voting scheme improve accuracy classification. proposed system was about 81% on average.