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

被引：146
|
作者：
Zhu, Chun [1 ]
Sheng, Weihua [1 ]
机构：
[1] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2011年 / 41卷 / 03期
基金：
美国国家科学基金会;
关键词：
Assisted living; hidden Markov models (HMNs); human-robot interaction (HRI); wearable computing; SYSTEM; ALGORITHM; MODEL;
D O I：
10.1109/TSMCA.2010.2093883
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：569 / 573
页数：5
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