LifeMinder: A Wearable Healthcare Support System Using User's Context
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Multi-graph Based Semi-supervised Learning for Activity Recognition
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
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In the area of activity recognition, many systems using accelerometers have been proposed. Common method for activity recognition requires raw data labeled with ground truth to learn the model. To obtain ground truth, a wearer records his/her activities during data logging through video camera or handwritten memo. However, referring a video takes long time and taking a memo interrupts natural activity. We propose a labeling method for activity recognition using an execution sequence of activities. The execution sequence includes activities in performed order, does not include time stamps, and is made based on his/her memory. Our proposed method partitions and classifies unlabeled data into segments and clusters, and assigns a cluster to each segment, then assign labels according to the best-matching assignment of clusters with the user-recorded activities. The proposed method gave a precision of 0.812 for data including seven kinds of activities. We also confirmed that recognition accuracy with training data labeled with our proposal gave a recall of 0.871, which is equivalent to that with ground truth.