Using accelerometers to remotely and automatically characterize behavior in small animals
| Autor: | Rachel E. Walsh, Dwight Springthorpe, Taylor Berg-Kirkpatrick, Talisin T. Hammond |
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| Rok vydání: | 2015 |
| Předmět: |
0106 biological sciences
Physiology Computer science Aquatic Science Accelerometer 010603 evolutionary biology 01 natural sciences Machine Learning Automation Species Specificity Accelerometry Animals Computer vision Animal behavior Molecular Biology Ecology Evolution Behavior and Systematics Communication Data collection Behavior Animal business.industry 010604 marine biology & hydrobiology Direct observation Reproducibility of Results Sciuridae Markov Chains Behavioral data Insect Science Animal Science and Zoology Artificial intelligence Seasons business Coding (social sciences) |
| Zdroj: | The Journal of experimental biology. 219(Pt 11) |
| ISSN: | 1477-9145 |
| Popis: | Activity budgets in wild animals are challenging to measure via direct observation because data collection is time consuming and observer effects are potentially confounding. Although tri-axial accelerometers are increasingly employed for this purpose, their application in small-bodied animals has been limited by weight restrictions. Additionally, accelerometers engender novel complications, as a system is needed to reliably map acceleration to behaviors. In this study we describe newly-developed, tiny acceleration-logging devices (1.5-2.5 grams) and use them to characterize behavior in two chipmunk species. We collected paired accelerometer readings and behavioral observations from captive individuals. We then employed techniques from machine learning to develop an automatic system for coding accelerometer readings into behavioral categories. Finally, we deployed and recovered accelerometers from free-living, wild chipmunks. This is the first time to our knowledge that accelerometers have been used to generate behavioral data for small-bodied ( |
| Databáze: | OpenAIRE |
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