Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. We address this need for both a benchmark dataset as well as the smart annotation methods used to create it in our new paper Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables authored by Christine Martindale, Sebastijan Sprager and Bjoern Eskofier.
The open access article published in Sensors can be found here: https://www.mdpi.com/1424-8220/19/8/1820
Dataset is publicly available here: https://www.mad.tf.fau.de/research/activitynet/benchmark-cyclic-activity-recognition-database-using-wearables/