title: A wearable system for asymmetric contactless human sensing.
abstract: A wearable sensor capable of detecting the presence of humans within a front-facing 90-degree sector of varying radius is demonstrated herein. The system offers extensive applicability across a variety of scenarios where detecting the parameters of human interaction, including separation distance and duration, is of value. Sensing is accomplished using an ultrasonic distance and passive infrared sensor. This design improves upon previous approaches presented in the literature by eliminating privacy concerns associated with audio and video capture, and also relaxing the requirement that both interacting individuals be in possession of dedicated hardware. A KNN classifier is developed using data obtained from a designed indoor experiment intended to demonstrate system robustness across geometries consistent with those observed in the target application. Employing a set of only three features, an overall accuracy rate of 94.2% is realized for detecting human interactions occurring within a 90-degree sector of three-foot radius.
authors.forename: Henry
authors.initials: H
authors.lastname: Griffith
authors.lastname: Yan Shi
authors.forename: Subir
authors.initials: S
authors.lastname: Biswas
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D058998:Remote Sensing Technology / Q000295:instrumentation* / Q000379:methods*