When Else Did This Happen? Efficient Subsequence Representation and Matching for Wearable Activity Data

In long-term activity recognition, large sets of inertial sensor data need to be analyzed in which physical actions of the sensorpsilas wearer are captured non-stop for weeks to months. These massive time sequences often burden the processing, and especially any post-analysis of the data. We propose a method that approximates and matches accelerometer time series, that is fast on large data sets, well-suited to human acceleration data, and efficient to log on the sensors. Experiments show that approximation and matching are faster than traditional methods, while remaining competitive in recognition of motion patterns.