Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living

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 g...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 41; no. 3; pp. 569 - 573
Main Authors Zhu, Chun, Sheng, Weihua
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Accuracy
Algorithms
Artificial neural networks
Assisted living
Cybernetics
Gesture recognition
Hidden Markov models
hidden Markov models (HMNs)
Human
human-robot interaction (HRI)
Humans
Mathematical models
Multisensor fusion
Neural networks
Recognition
Robots
Sensors
Wearable
wearable computing
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2010.2093883