Practical Design and Implementation of Animal Movements Tracking System for Neuroscience Trials
Memarian Sorkhabi, M.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSThe nervous system functions of an animal are predominantly reflected in the behaviour and the movement, therefore the movement-related data and measuring behavior quantitatively are crucial for behavioural analyses. The animal movement is traditionally recorded, and human observers follow the animal behaviours; if they recognize a certain behaviour pattern, they will note it manually, which may suffer from observer fatigue or drift. ObjectiveAutomating behavioural observations with computer-vision algorithms are becoming essential equipment to the brain function characterization in neuroscience trials. In this study, the proposed tracking module is eligible to measure the locomotor behaviour (such as speed, distance, turning) over longer time periods that the operator is unable to precisely evaluate. For this aim, a novel animal cage is designed and implemented to track the animal movement. The frames received from the camera are analyzed by the 2D bior 3.7 Wavelet transform and SURF feature points. ResultsImplemented video tracking device can report the location, duration, speed, frequency and latency of each behavior of an animal. Validation tests were conducted on the auditory stimulation trial and the magnetic stimulation treatment of hemi-Parkinsonian rats. Conclusion/ SignificanceThe proposed toolkit can provide qualitative and quantitative data on animal behaviour in an automated fashion, and precisely summarize an animals movement at an arbitrary time and allows operators to analyse movement patterns without requiring to check full records for every experiment.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multi- Stage Feature Selection (MSFS) Algorithm for UWB- Based Early Breast Cancer Size Prediction 98%
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 97%
- A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of Leptospirosis 97%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 96%
- A Robust Spike Sorting Method based on the Joint Optimization of Linear Discrimination Analysis and Density Peaks 94%
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 94%
Similar papers in this journal
- Notable sequence homology of the ORF10 protein introspects the architecture of SARS-COV-2 90%
- Proximal relationships of moonlighting Proteins in Escherichia coli: a mathematical genomic perspective 90%
- Gene Expression Profiling and Physiological Adaptations of Pearl Spot (Etroplus suratensis) under Varying Salinity Conditions 89%
Similar papers in this journal
- Detection of Static, Dynamic, and No Tactile Friction Based on Non-linear dynamics of EEG Signals: A Preliminary Study 97%
- Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators 93%
- Modelling, Analysis, and Optimization of Three-Dimensional Restricted Visual Field Metric-Free Swarms 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.