A benchmark dataset to evaluate sensor displacement in activity recognition
| Autor: | Oresti Banos, Ignacio Rojas, Mate Attila Toth, Héctor Pomares, Oliver Amft, Miguel Damas |
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| Předmět: |
Inertial frame of reference
business.industry Computer science Work (physics) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION computer.software_genre Displacement (vector) Activity recognition Acceleration Position (vector) Benchmark (computing) Key (cryptography) Computer vision Artificial intelligence Data mining business computer |
| Zdroj: | Scopus-Elsevier UbiComp |
| Popis: | This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future. |
| Databáze: | OpenAIRE |
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