An investigation into smartphone based weakly supervised activity recognition systems
| Autor: | Tom Lunney, Kevin Curran, William Duffy, Daniel Kelly |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Data collection
Computer Networks and Communications business.industry Computer science Feature vector 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Random forest Activity recognition Support vector machine Annotation Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software Information Systems |
| Zdroj: | Pervasive and Mobile Computing. 56:45-56 |
| ISSN: | 1574-1192 |
| DOI: | 10.1016/j.pmcj.2019.03.005 |
| Popis: | With smart-devices becoming increasingly more commonplace, methods of capturing an individual’s activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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