Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry

摘要

This paper investigates unsupervised pattern recognition approaches to segment raw accelerometer signals into a sequence of events, for activity monitoring in a free-living environment. Ambulatory devices, such as accelerometers, have made it possible to