Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Object-based activity recognition with heterogeneous sensors on wrist
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Transferring knowledge of activity recognition across sensor networks
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
On the use of brain decoded signals for online user adaptive gesture recognition systems
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
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This paper proposes an activity recognition method that models an end user's activities without using any labeled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user's physical characteristics such as height and gender to find and select appropriate training data obtained from other users in advance. Then, we model the end user's activities by using the selected labeled sensor data. Therefore, our method does not require the end user to collect and label her training sensor data. In this paper, we propose and test two methods for finding appropriate training data by using information about the end user's physical characteristics. Moreover, our recognition method improves the recognition performance without the need for any effort by the end user because the method automatically adapts the activity models to the end user when it recognizes her unlabeled sensor data. We confirmed the effectiveness of our method by using 100 h of sensor data obtained from 40 participants.