Dec 08, 2023
790
0
2
References
- 1. Kumpulainen, P., Cardó, A. V., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., & Vehkaoja, A. (2021). Dog behaviour classification with movement sensors placed on the harness and the collar. Applied Animal Behaviour Science, 241, 105393. https://doi.org/10.1016/j.applanim.2021.105393
- 2. Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., & Vainio, O. (2022). Description of movement sensor dataset for dog behavior classification. Data in Brief, 40, 107822. https://doi.org/10.1016/j.dib.2022.107822
Deirdre and lakejain like this. Please wait...
About This Project
Edge machine learning refers to the process of running embedded ML models on site using devices capable of collecting, processing, and recognizing patterns within collections of raw data. This project seeks to train one of such devices (Nicla SenseME) with dog data from the Earth Species' Bio-logger Ethogram Benchmark (BEBE). The board will be used as a smart dog collar, with its ML inferences controlling haptics vibrations in a bracelet wore by a person.
Browse Other Projects on Experiment
Related Projects
Generalizable Computational Pipeline for Engineering Ultrastable Variants of Carbonic Anhydrase
AI-driven protein design is undergoing a transformation, driven by recent breakthroughs such as AlphaFold...
Designing ultrastable carbonic anhydrase with deep generative models and high-throughput assays
To minimize the impact of CO2 emissions on life on earth, we need technologies for carbon capture exceeding...
Understanding seal behavior with artificial intelligence
Seals are important indicators for the health of our oceans, but it is often difficult to monitor their...
