Danilo

Danilo

Dec 08, 2023

Group 6 Copy 787
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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
  • 3. Vehkaoja, Antti; Somppi, Sanni; Törnqvist, Heini; Valldeoriola Cardó, Anna; Kumpulainen, Pekka; Väätäjä, Heli; Majaranta, Päivi; Surakka, Veikko; Kujala, Miiamaaria; Vainio, Outi (2021), “Movement Sensor Dataset for Dog Behavior Classification”, Mendeley Data, V1, doi: 10.17632/vxhx934tbn.1
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    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.

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    A computer science project funded by 5 people