Context cells: towards lifelong learning in activity recognition systems

Authors:
Alberto Calatroni;Claudia Villalonga;Daniel Roggen;Gerhard Tröster
Affiliations:
Wearable Computing Laboratory, ETH Zürich, Switzerland;Wearable Computing Laboratory, ETH Zürich, Switzerland and SAP Research, CEC, Zürich, Switzerland;Wearable Computing Laboratory, ETH Zürich, Switzerland;Wearable Computing Laboratory, ETH Zürich, Switzerland
Venue:
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Year:
2009

Citing 10
Cited 1

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning From Noisy Examples

Machine Learning
Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition

ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
Activity-Aware Computing for Healthcare

IEEE Pervasive Computing
Wearable Activity Tracking in Car Manufacturing

IEEE Pervasive Computing
SMASH: a distributed sensing and processing garment for the classification of upper body postures

BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
A long-term evaluation of sensing modalities for activity recognition

UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
The computer for the 21st Century

IEEE Pervasive Computing
Learn++: an incremental learning algorithm for supervised neuralnetworks

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel incremental principal component analysis and its application for face recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Titan: an enabling framework for activity-aware "pervasive apps" in opportunistic personal area networks

EURASIP Journal on Wireless Communications and Networking - Special issue on towards the connected body: advances in body communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

A robust activity and context-recognition system must be capable of operating over a long period of time, exploiting new sources of information as they become available and evolving in an autonomous manner, coping with user variability and changes in the number and type of available sensors. In particular, wearable and ambient nodes should be trained lifelong, as new context instances naturally arise, and the labeling of the instances should be carried out ideally with no user intervention. In this paper we show by means of an experiment and simulations that we can indeed achieve lifelong learning and automatic labeling by using Context Cells, an architecture capable of sensing, learning, classifying data and exchanging information.