Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost

Aziz, Firman; Usman, Syahrul; Jeffry, Jeffry

doi:10.30865/mib.v5i4.3222

Cited by 3 publications

(3 citation statements)

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“…The results of this research indicate that machine learning algorithms, including both the decision tree and support vector machine, can effectively predict physical activity behavior among adolescents. This finding aligns with previous research, which has shown that machine learning methods perform exceptionally well in studying physical activity (Aziz et al, 2021). Several earlier studies that predicted physical activity using machine learning, specifically the support vector machine model (Cheng et al, 2021;Chong et al, 2021;Vanstrum et al, 2023;Wang, 2022;Zhou et al, 2019), also reported that the support vector machine model demonstrates high accuracy in predicting physical activity.…”

Section: Discussionsupporting

confidence: 90%

A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents

Mahendra,

Jajat,

Damayanti

et al. 2023

Ind. Jo. Spo. Man.

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The ongoing evolution of technology has had both positive and negative effects on modern society. On the positive side, it has significantly improved the ease with which various activities can be performed. However, it has also had a negative impact by reducing physical activity. This reduction in physical activity, in turn, increases the risk of chronic diseases that contribute to global mortality rates. This research aims to assess the effectiveness of machine learning in predicting the physical activity levels of adolescents. The study utilizes data from accelerometers, specifically the ActiGraph GT3X. The research methodology employs a semi-supervised machine learning approach, using the support vector machine and decision tree algorithms to make these predictions. The sample comprises 61 adolescents (males = 17, female = 44), including high school students and university students aged 18-21, from the West Java region. The results from the machine learning model using the decision tree algorithm indicated a model accuracy of 97.50% in predicting physical activity levels. In contrast, the accuracy obtained from the performance analysis using the confusion matrix for the support vector machine model was 92.5%. Based on these accuracy levels, the decision tree algorithm outperforms the support vector machine algorithm's accuracy. Further analyses involving different models are necessary to determine which algorithm offers the highest level of accuracy.

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Section: Discussionsupporting

confidence: 90%

A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents

Mahendra,

Jajat,

Damayanti

et al. 2023

Ind. Jo. Spo. Man.

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“…However, this research is merely limited to two classes. Research [15]- [17] proposed a classification of human physical activity using accelerometer sensors, gyroscopes, and gravity sensors. The ensemble technique was employed by combining logistic regression as initial classification and gradient boost to correct misclassified logistic regression [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

Wungo

Mardewi²,

Aziz

et al. 2023

Ilk. J. Ilm.

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Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones. The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.

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“…Berdasarkan hasil yang diperoleh, ensemble SVM dengan Stacking memberikan peningkatan kinerja ± 1%. Penelitian [19], [20] menggunakan…”

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Penerapan Algoritma Multiclass Ensemble Support Vector Machine dengan Fungsi Kernel untuk Klasifikasi Human Activity

Aziz¹,

Usman²,

Jeffry³

et al. 2022

j. inform. terpadu

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Human Activity Recognition adalah teknologi yang memperkenalkan gerakan tubuh manusia menggunakan accelerometer, giroskop, global positioning system, dan kamera. Awal munculnya metode support vector machine digunakan untuk mengklasifikasi 2 kelas, sehingga diperlukan pengembangan untuk mengatasi permasalahan multikelas dan banyaknya dataset yang berskala besar mengakibatkan kinerja menjadi tidak optimal. Tujuan kertas ini adalah menerapkan metode ensemble Support Vector Machine dalam mengklasifikasikan gerakan berjalan, berlari, dan naik tangga berdasarkan sensor accelerometer dan gyroscope pada smartphone. Serta melihat kinerja metode ensemble Support Vector Machine ketika menggunakan kernel linear dan RBF. Hasil akurasi Support Vector Machine kernel linear sebesar 79.66% dan mengalami peningkatan sebesar 88.01% setelah menggunakan ensemble. Sedangkan akurasi untuk Support Vector Machine kernel RBF sebesar 79.51 dan mengalami peningkatan sebesar 88.04% setelah menggunakan ensemble.

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Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost

Cited by 3 publications

References 0 publications

A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents

A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents

Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

Penerapan Algoritma Multiclass Ensemble Support Vector Machine dengan Fungsi Kernel untuk Klasifikasi Human Activity

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