Long-term Activities Segmentation using Viterbi Algorithm with a k-minimum-consecutive-states Constraint

摘要

In the last years, several works have made use of acceleration sensors to recognize simple physical activities like: walking, running, sleeping, falling, etc. Many of them rely on segmenting the data into fixed time windows and computing time domain and/or frequency domain features to train a classifier. Along-term activityis composed of a collection of simple activities and may last from a few minutes to several hours (e.g., shopping, exercising, working, etc.). Sincelong-term activitiesare more complex and their duration varies greatly, generating fixed length segments is not suitable. For this type of activities the segmentation should be done dynamically. In this work we propose the use of the Viterbi algorithm on a Hidden Markov Model with the addition of a k-minimum-consecutive-states constraint to perform thelong-term activityrecognition and segmentation from accelerometer data. This constraint allows the algorithm to perform a more informed search by incorporating prior knowledge about the minimum duration of eachlong-term activity. Our experiments showed good results for the activity recognition task and it was demonstrated that the accuracy was significantly increased by adding the k-minimum-consecutive-states constraint.