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

Author

Zhu, Chun ; Sheng, Weihua

Author_Institution

Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA

Volume

41

Issue

3

fYear

2011

fDate

5/1/2011 12:00:00 AM

Firstpage

569

Lastpage

573

Abstract

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.

Keywords

gesture recognition; handicapped aids; hidden Markov models; human-robot interaction; mobile robots; neural nets; sensor fusion; service robots; wearable computers; context-based recognition; daily activity recognition; hidden Markov model; human robot interaction; multisensor fusion; neural network; robot assisted living; smart assisted living system; wearable sensor-based hand gesture recognition; Accuracy; Artificial neural networks; Gesture recognition; Hidden Markov models; Humans; Robots; Sensors; Assisted living; hidden Markov models (HMNs); human-robot interaction (HRI); wearable computing;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Publisher

ieee

ISSN

1083-4427

Type

jour

DOI

10.1109/TSMCA.2010.2093883

Filename

5682421