The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a data set with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available.
The dataset consists of 12 different task-driven activities, 10 of which are cyclic.
These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions.
Each participant wore 5 synchronized inertial measurementunits (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video.
We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms.
We are located close to Ascot Train Station in the Business Park.
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Unit E6, Ascot Business Park, Lyndhurst Rd, Ascot, Berkshire, Sl5 9FE
+44 1344 623 883