Title :
Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors
Author :
Robards, Matthew W. ; Sunehag, Peter
Author_Institution :
RSISE at ANU, NICTA, Canberra, ACT, Australia
Abstract :
Subsequence clustering aims to find patterns that appear repeatedly in time series data. We introduce a novel subsequence clustering technique that we call semi-Markov kmeans clustering. The clustering results in ideal examples of the repeating patterns and in labeled segmentations that can be used as training data for sophisticated discriminative methods like max-margin semi-Markov models. We are applying the new clustering technique to activity recognition from body-worn sensors by showing how it can enable a system to learn from data that is only annotated by an ordered list of activity types that have been undertaken. This kind of annotation, unlike a detailed segmentation of the sensor data, is easily provided by a non-expert user. We show that we can achieve equally good results using only an ordered list of activity types for training as when using a full detailed labeled segmentation.
Keywords :
Markov processes; image segmentation; pattern clustering; sensors; time series; activity recognition; body worn sensors; detailed segmentation sensor data; full detailed labeled segmentation; non expert user; novel subsequence clustering technique; semi Markov kmeans clustering; sophisticated discriminative methods; time series data; Acceleration; Accelerometers; Australia; Data mining; Humans; Pattern recognition; Sensor systems; Text recognition; Training data; Wearable sensors; activity recognition; clustering; subsequence; time-series;
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2009.13