Topic modeling of behavioral modes using sensor data

The field of movement ecology is experiencing a period of rapid growth in availability of data. As the volume rises, traditional methods are giving way to machine learning and data science, which are playing an increasingly large part in turning these data into science-driving insights. One rich and interesting source is the biologger. These small electronic wearable devices are attached to animals free to roam in their natural habitats and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of accelerometer data is for supervised learning of behavioral modes. However, we need unsupervised analysis tools as well, in order to overcome the inherent difficulties of obtaining a labeled dataset, which in some cases either is infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here, we present a matrix factorization-based topic model method for accelerometer bursts, derived using a linear mixture property of patch features. Our method is validated via comparison with a labeled dataset and is further compared to standard clustering algorithms.