Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
Volume 41, Issue 3, Pages 569-573Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCA.2010.2093883
Keywords
Assisted living; hidden Markov models (HMNs); human-robot interaction (HRI); wearable computing
Funding
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0916864, 0923238] Funding Source: National Science Foundation
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In this paper, we address natural human-robot interaction (HRI) in a smart assisted living (SAIL) system for the elderly and the disabled. Two common HRI problems are studied: hand gesture recognition and daily activity recognition. For hand gesture recognition, we implemented a neural network for gesture spotting and a hierarchical hidden Markov model for context-based recognition. For daily activity recognition, a multisensor fusion scheme is developed to process motion data collected from the foot and the waist of a human subject. Experiments using a prototype wearable sensor system show the effectiveness and accuracy of our algorithms.
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