Paper Info
Title
User Routine Model Using a Cloud-Connected Social Robot
Abstract
In this manuscript we propose a distributed classifier to perform inference on a person daily behaviour routine, based on multi-modal input data. The model is implemented on a social robot and allows to efficiently fuse locally perceived information with data classified remotely on a cloud. Unlike the dominant multi-class approaches, where each class is classified separately, the multi-label scheme estimates all classes simultaneously from the available input instances. This method enables a robot to capture user typical behaviour and provides a simple scheme of regulation that allows the identification of abnormal situations. We propose to solve our problem in two steps based on the principles of Binary Relevance and Label Power-set: (1) a label classification is used to filter input instances into independent labels, (2) the algorithm will map the labels into an hyper-label space, where each hyper-label represents the behaviour which maximizes input instance correlations. Results show the proposed multi-label model to achieve a highly accurate comprehension of the user behaviour even within more demanding test scenarios. As for the regulatory experiments, initial results show that the proposed behaviour model allows to identify unexpected events, that can be used to trigger care giver interventions.
Year
DOI
Venue
2016
10.1109/CloudNet.2016.34
2016 5th IEEE International Conference on Cloud Networking (Cloudnet)
Keywords
Field
DocType
multi-label classification,service robots,robot perception,multi-modal interface,human robot interaction
Data modeling,Social robot,Inference,Computer science,Scenario testing,Artificial intelligence,Robot,Classifier (linguistics),Machine learning,Encoding (memory),Cloud computing
Conference
ISSN
ISBN
Citations 
2374-3239
978-1-5090-5094-9
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Luís Santos111014.58
Jorge Dias217533.83