Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Lee, Jae-Neung; Byeon, Yeong-Hyeon; Kwak, Keun-Chang

doi:10.3390/mi9080411

Cited by 8 publications

(10 citation statements)

References 26 publications

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“…Automated HoAR has been widely studied with the use of different kinds of sensors such as accelerometers (Kamminga et al, 2019a;Lee et al, 2018), magnetometers (Gutierrez-Galan et al, 2018Lee et al, 2016), and gyroscopes (Mao et al, 2021;Braganca et al, 2020). The sensors can be attached to horses in various locations such as the horse's neck (Mao et al, 2021;Kamminga et al, 2019a), leg (Eerdekens et al, 2021(Eerdekens et al, , 2020, and wrist (Casella et al, 2020) for collecting observations at a particular time point.…”

Section: Related Workmentioning

confidence: 99%

Classifying horse activities with big data using machine learning

Birant

TEPE>

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Using big data-assisted machine learning methods in animal science has received increasing attention in recent years since they extract useful insights from large-scale animal datasets. Especially, animal activity recognition is the task of identifying the actions performed by animals and can provide rich insight into their health, welfare, reproduction, survival, foraging, and interaction with humans/other animals. This paper aims to propose a new solution for this purpose by building a machine learning model that classifies the actions of horses based on big sensor data. Unlike the previous studies, our study is original in that it compares the accuracies of per-subject (personalized) and cross-subject (generalized) models. It is the first study that especially compares different ensemble learning algorithms for horse activity recognition in terms of classification accuracy, including bagging trees, extremely randomized trees, random forest, extreme gradient boosting, light gradient boosting, gradient boosting, and categorical boosting. The purpose of the study is to classify five horse activities: walking, standing, grazing, galloping, and trotting. The experimental results showed that our solution achieved very good performance (94.62%) on average on a real-world dataset. Furthermore, the results also showed that our method outperformed the state-of-the-art methods on the same dataset.

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Section: Related Workmentioning

confidence: 99%

Classifying horse activities with big data using machine learning

Birant

TEPE>

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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“…But Parallel AIRS has multiple processors, so more than one processor can perform their task-parallel. AIRS 1 and AIRS 2 algorithm have the nine steps [10][11][12][13] Step 4: Collect the memory cells from each processor and the memory cells are merged and back to the root (initial stage). Speed up is achieved without any loss of accuracy in the classification.…”

Section: Literature Surveymentioning

confidence: 99%

Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

Kamalakannan¹,

Anandharaj²

2021

IJACSA

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Parkinson's disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson's are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson's disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System -Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithms and proved its significance on PD classification.

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“…To increase performance, many studies have used ensemble deep models at various applications. Lee [32] designed an ensemble stacked auto-encoder based on sum and product for classifying horse gaits using wavelet packets from motion data of the rider. Maguolo [33] studied an ensemble of convolutional neural networks trained with different activation functions using sum rule to improve the performance in smallor medium-sized biomedical datasets.…”

Section: Introductionmentioning

confidence: 99%

Posture Recognition Using Ensemble Deep Models under Various Home Environments

et al. 2020

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This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself. 15 of 26 preprocessed posture images for the testing was 500 for standing normal, 497 for standing bent, 500 for sitting sofa, 500 for sitting chair, 497 for sitting floor, 498 for sitting squat, 489 for lying face, 489 for lying back, 482 for lying side, 481 for lying crouched, and 2001 for other images. The total number of images for the training, validation, and testing was 44,874, 7913, and 6934, respectively. detector [39], and the segmented person images were filtered manually. The number of preprocessed posture images for the training was 3910 for standing normal, 3802 for standing bent, 3899 for sitting sofa, 3899 for sitting chair, 3896 for sitting floor, 3859 for sitting squat, 3752 for lying face, 3777 for lying back, 3773 for lying side, 3505 for lying crouched, and 6802 for other images. The number of preprocessed posture images for the validation was 689 for standing normal, 670 for standing bent, 688 for sitting sofa, 687 for sitting chair, 687 for sitting floor, 681 for sitting squat, 662 for lying face, 666 for lying back, 665 for lying side, 618 for lying crouched, and 1200 for other images. The number of preprocessed posture images for the testing was 500 for standing normal, 497 for standing bent, 500 for sitting sofa, 500 for sitting chair, 497 for sitting floor, 498 for sitting squat, 489 for lying face, 489 for lying back, 482 for lying side, 481 for lying crouched, and 2001 for other images. The total number of images for the training, validation, and testing was 44,874, 7913, and 6934, respectively.

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Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Cited by 8 publications

References 26 publications

Classifying horse activities with big data using machine learning

Classifying horse activities with big data using machine learning

Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

Posture Recognition Using Ensemble Deep Models under Various Home Environments

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