Hand Gesture Recognition for Doors with Neural Network

Abstract

In this paper we propose a hand gesture recognition system for door opening. Because the usage of door knobs and the way of opening doors are similar worldwide, people will naturally do similar actions without special promise when opening the door. When a user wears a smart watch, it is possible to perform movements more natural than the movement at the situation with holding a smartphone in hand. We used an accelerometer embedded in a smart watch to collect hand gesture data, which opens each of three types of door, hinged, slide, and shutter. We preprocessed the raw data with two steps. We trimmed the data and normalized trimmed data using akima spline for multi-layer perceptron (MLP). Also, we used MLP to classify the preprocessed hand gesture data in our system.