Benchmarking classification techniques using the Opportunity human activity dataset
| Autor: | Sundara Tejaswi Digumarti, Alberto Calatroni, Gerhard Tröster, Daniel Roggen, José del R. Millán, Ricardo Chavarriaga, Hesam Sagha |
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| Rok vydání: | 2011 |
| Předmět: |
Computer science
business.industry Behavioural sciences Benchmarking Benchmark Machine learning computer.software_genre Activity recognition Body sensors networks Group tests Gesture recognition Benchmark (computing) Opportunity dataset Human activity recognition Data mining Artificial intelligence business computer |
| Zdroj: | SMC |
| DOI: | 10.1109/icsmc.2011.6083628 |
| Popis: | Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms on their own datasets using experimental setups specially conceived for that specific purpose. In this work, we introduce a versatile human activity dataset conceived to fill that void. We illustrate its use by presenting comparative results of different classification techniques, and discuss about several metrics that can be used to assess their performance. Being an initial benchmarking, we expect that the possibility to replicate and outperform the presented results will contribute to further advances in state-of-the-art methods. |
| Databáze: | OpenAIRE |
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