Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

Show full item record

Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/3243

Title: Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers
Author: Atrash, Amin ; Starner, Thad
Abstract: Most gesture recognition systems analyze gestures intended for communication (e.g. sign language) or for command (e.g. navigation in a virtual world). We attempt instead to recognize gestures made in the course of performing everyday work activities. Specifically, we examine activities in a wood shop, both in isolation as well as in the context of a simulated assembly task. We apply linear discriminant analysis (LDA) and hidden Markov model (HMM) techniques to features derived from body-worn accelerometers and microphones. The resulting system can successfully segment and identify most shop activities with zero false positives and 83.5% accuracy.
Type: Technical Report
URI: http://hdl.handle.net/1853/3243
Date: 2003
Relation: GVU Technical Report;GIT-GVU-03-32
Publisher: Georgia Institute of Technology
Subject: Gesture recognition
Accelerometers
HMMs
LDA
Context recognition
Wearable computers

All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved, unless otherwise specifically indicated on or in the materials.

Files in this item

Files Size Format View
03-32.pdf 1.081Mb PDF View/ Open

This item appears in the following Collection(s)

Show full item record