Indoor Person Identification through Footstep Induced Structural Vibration

Abstract

Person identification is crucial in various smart building applications, including customer behavior analysis, patient monitoring, etc. Prior works on person identification mainly focused on access control related applications. They achieve identification by sensing certain biometrics with specific sensors. However, these methods and apparatuses can be intrusive and not scalable because of instrumentation and sensing limitations. In this paper, we introduce our indoor person identification system that utilizes footstep induced structural vibration. Because structural vibration can be measured without interrupting human activities, our system is suitable for many ubiquitous sensing applications. Our system senses floor vibration and detects the signal induced by footsteps. Then the system extracts features from the signals that represent characteristics of each person's gait pattern. With the extracted features, the system conducts hierarchical classification at an individual step level and then at a trace (i.e., collection of consecutive steps) level. Our system achieves over 83% identification accuracy on average. Furthermore, when the application requires different levels of accuracy, our system can adjust confidence level threshold to discard uncertain traces. For example, at a threshold that allows only most certain 50% traces for classification, the identification accuracy increases to 96.5%.