Title
Enhancing task classification in human-machine collaborative teleoperation systems by real-time evaluation of an agreement criterion
Abstract
Human-machine collaborative teleoperation systems were introduced to overcome limitations of state-of-the-art teleoperation systems by using a virtual assistant that supports the human operator in the execution of a task. Since assistances are highly task-dependent a correct classification of the currently performed task is paramount. In this paper, we present a novel approach for improving task classification for a human-machine collaborative teleoperation system. Starting from a classical HMM-based classifier implemented in our previous research, we introduce a method for correcting erroneous task classifications by evaluating an agreement criterion. This criterion is based on interactive forces and is used to distinguish between situations in which human and assistant agree/disagree in their execution of the task. Using disagreement as indicator for the activation of an unsuitable/suboptimal assistance, erroneous task classifications are identified and the original classification result is revised. The proposed approach shows significant improvements in task classification coming along with a comparable low implementation effort.
Year
DOI
Venue
2011
10.1109/WHC.2011.5945535
World Haptics Conference
Keywords
Field
DocType
man-machine systems,pattern classification,task analysis,telerobotics,HMM-based classifier,agreement criterion,erroneous task classification,human operator,human-machine collaborative teleoperation system,interactive forces,suboptimal assistance,unsuitable assistance,virtual assistant
Teleoperation,Computer vision,Human–machine system,Remote assistance,Human operator,Task analysis,Computer science,Artificial intelligence,Classifier (linguistics),Hidden Markov model,Telerobotics
Conference
ISBN
Citations 
PageRank 
978-1-4577-0297-6
6
0.45
References 
Authors
13
4
Name
Order
Citations
PageRank
Carolina Passenberg160.45
Nikolay Stefanov2443.88
Angelika Peer340640.39
Martin Buss41799159.02