Detection of foraging behavior from accelerometer data using U-Net type convolutional networks

01/06/2021
by   Manh Cuong Ngô, et al.
0

Narwhal is one of the most mysterious marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of this species, where behavioral information can be collected from instrumented individuals. This includes accelerometer data, diving and acoustic data as well as GPS positioning. An essential element in understanding the ecological role of toothed whales is to characterize their feeding behavior and estimate the amount of food consumption. Buzzes are sounds emitted by toothed whales that are related directly to the foraging behaviors. It is therefore of interest to measure or estimate the rate of buzzing to estimate prey intake. The main goal of this paper is to find a way to detect prey capture attempts directly from accelerometer data, and thus be able to estimate food consumption without the need for the more demanding acoustic data. We develop 3 automated buzz detection methods based on accelerometer and depth data solely. We use a dataset from 5 narwhals instrumented in East Greenland in 2018 to train, validate and test a logistic regression model and the machine learning algorithms random forest and deep learning, using the buzzes detected from acoustic data as the ground truth. The deep learning algorithm performed best among the tested methods. We conclude that reliable buzz detectors can be derived from high-frequency-sampling, back-mounted accelerometer tags, thus providing an alternative tool for studies of foraging ecology of marine mammals in their natural environments. We also compare buzz detection with certain movement patterns, such as sudden changes in acceleration (jerks), found in other marine mammal species for estimating prey capture. We find that narwhals do not seem to make big jerks when foraging and conclude that their hunting patterns in that respect differ from other marine mammals.

READ FULL TEXT

page 5

page 9

page 12

page 13

page 17

page 18

page 19

research
08/14/2019

Predicting Eating Events in Free Living Individuals -- A Technical Report

This technical report records the experiments of applying multiple machi...
research
02/27/2018

Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks

Due to the growing concern of chronic diseases and other health problems...
research
02/03/2022

GMM Clustering for In-depth Food Accessibility Pattern Exploration and Prediction Model of Food Demand Behavior

Understanding the dynamics of food banks' demand from food insecurity is...
research
09/22/2021

A deep neural network for multi-species fish detection using multiple acoustic cameras

Underwater acoustic cameras are high potential devices for many applicat...
research
03/18/2020

An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients

Regular monitoring of nutrient intake in hospitalised patients plays a c...

Please sign up or login with your details

Forgot password? Click here to reset