scholarly journals SIPEC: the deep-learning Swiss knife for behavioral data analysis

2020 ◽  
Author(s):  
Markus Marks ◽  
Jin Qiuhan ◽  
Oliver Sturman ◽  
Lukas von Ziegler ◽  
Sepp Kollmorgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Animal Behavior ◽  
Home Cage ◽  
Complex Environments ◽  
Or Groups ◽  
Multiple Behaviors ◽  
Freely Moving ◽  
Using Data

ABSTRACTAnalysing the behavior of individuals or groups of animals in complex environments is an important, yet difficult computer vision task. Here we present a novel deep learning architecture for classifying animal behavior and demonstrate how this end-to-end approach can significantly outperform pose estimation-based approaches, whilst requiring no intervention after minimal training. Our behavioral classifier is embedded in a first-of-its-kind pipeline (SIPEC) which performs segmentation, identification, pose-estimation and classification of behavior all automatically. SIPEC successfully recognizes multiple behaviors of freely moving mice as well as socially interacting nonhuman primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

scholarly journals Deep Learning Based Classification of Radar Spectral Maps

2021 ◽  
pp. 99-104
Author(s):  
Tong Lin ◽  
◽  
Xin Chen ◽  
Xiao Tang ◽  
Ling He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Region Of Interest ◽  
Radar Target ◽  
Automatic Annotation ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Using Data ◽  
Map Data

This paper discusses the use of deep convolutional neural networks for radar target classification. In this paper, three parts of the work are carried out: firstly, effective data enhancement methods are used to augment the dataset and address unbalanced datasets. Second, using deep learning techniques, we explore an effective framework for classifying and identifying targets based on radar spectral map data. By using data enhancement and the framework, we achieved an overall classification accuracy of 0.946. In the end, we researched the automatic annotation of image ROI (region of interest). By adjusting the model, we obtained a 93% accuracy in automatic labeling and classification of targets for both car and cyclist categories.

scholarly journals DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jens F. Schweihoff ◽  
Matvey Loshakov ◽  
Irina Pavlova ◽  
Laura Kück ◽  
Laura A. Ewell ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Animal Behavior ◽  
Closed Loop ◽  
Behavioral Experiments ◽  
Olfactory Conditioning ◽  
Spatiotemporal Resolution ◽  
Neuronal Ensembles ◽  
Freely Moving ◽  
Millisecond Range

AbstractIn general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate behavior with neuronal activity it is often necessary to detect and react online to behavioral expressions. Here we present DeepLabStream, a versatile closed-loop tool providing real-time pose estimation to deliver posture dependent stimulations. DeepLabStream has a temporal resolution in the millisecond range, can utilize different input, as well as output devices and can be tailored to multiple experimental designs. We employ DeepLabStream to semi-autonomously run a second-order olfactory conditioning task with freely moving mice and optogenetically label neuronal ensembles active during specific head directions.

scholarly journals Yoga Pose Perfection using Deep Learning

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Satyam Goyal ◽  
Animesh Jain
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pose Estimation ◽  
Health Monitoring ◽  
Modern Society ◽  
Public Security ◽  
Pose Detection ◽  
Daunting Task ◽  
Human Pose

Abstract Even with lots of attention and work in the computer vision and artificial intelligence field, human body pose detection is still a daunting task. The application of human pose detection is wide-ranging from health monitoring to public security. This paper focuses on the application in yoga, an art that has been performed for over a millennium. In modern society yoga has become a common method of exercise and there-in arises a demand for instructions on how to do yoga properly. Doing certain yoga postures improperly may lead to injuries and fatigue and hence the presence of a trainer becomes important. As many people don’t have the resources to have a yoga instructor or guide, artificial intelligence can act as a substitute and advise people on their poses. Currently, the research surrounding pose estimation for yoga mainly discusses the classification of yogic poses. In this work, we propose a method, using the Tensorflow MoveNet Thunder model, that allows real-time pose estimation to detect the error in a person's pose, thereby allowing them to correct it.

scholarly journals A Unified Deep-Learning Model for Classifying the Cross-Country Skiing Techniques Using Wearable Gyroscope Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3819 ◽  
Author(s):  
Jihyeok Jang ◽  
Ankit Ankit ◽  
Jinhyeok Kim ◽  
Young Jae Jang ◽  
Hye Young Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wearable Sensors ◽  
Learning Model ◽  
Natural Course ◽  
Cross Country ◽  
Sensor Configuration ◽  
Using Data ◽  
Leave One Out ◽  
Deep Learning Model

The automatic classification of cross-country (XC) skiing techniques using data from wearable sensors has the potential to provide insights for optimizing the performance of professional skiers. In this paper, we propose a unified deep learning model for classifying eight techniques used in classical and skating styles XC-skiing and optimize this model for the number of gyroscope sensors by analyzing the results for five different configurations of sensors. We collected data of four professional skiers on outdoor flat and natural courses. The model is first trained over the flat course data of two skiers and tested over the flat and natural course data of a third skier in a leave-one-out fashion, resulting in a mean accuracy of ~80% over three combinations. Secondly, the model is trained over the flat course data of three skiers and tested over flat course and natural course data of one new skier, resulting in a mean accuracy of 87.2% and 95.1% respectively, using the optimal sensor configuration (five gyroscope sensors: both hands, both feet, and the pelvis). High classification accuracy obtained using both approaches indicates that this deep learning model has the potential to be deployed for real-time classification of skiing techniques by professional skiers and coaches.

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