Tan, Q.; Qin, Y.; Tang, R.; Wu, S.; Cao, J. A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity. Sensors2023, 23, 9613.
Tan, Q.; Qin, Y.; Tang, R.; Wu, S.; Cao, J. A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity. Sensors 2023, 23, 9613.
Tan, Q.; Qin, Y.; Tang, R.; Wu, S.; Cao, J. A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity. Sensors2023, 23, 9613.
Tan, Q.; Qin, Y.; Tang, R.; Wu, S.; Cao, J. A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity. Sensors 2023, 23, 9613.
Abstract
This study presents a research plan that utilizes data obtained from wearable devices to
identify human activities and gain insights into human behavior. We developed a model capable of
classifying activities similar to human behavior and evaluated the effectiveness and generalization
capabilities of this model. The data underwent initial preprocessing, including standardization and
normalization. Additionally, recognizing the inherent similarities between human activity behaviors,
we introduced a multi-layer classifier model. The first layer is a random forest model based on
stepwise regression, which may encounter reduced accuracy for similar activities. The second layer
employs a Support Vector Machine (SVM) model based on Kernel Fisher Discriminant Analysis
(KFDA). KFDA is used to reduce the dimensionality of data points with potential confusion,
followed by SVM for classification. The model was experimentally evaluated and applied to four
benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results
demonstrate that our approach achieved recognition accuracies of 99.71%, 98.71%, 99.12%, and
97.6% on these datasets, indicating excellent recognition performance. Furthermore, to assess the
model's generalization ability, we performed K-fold cross-validation on the random forest model
and utilized ROC curves for the SVM classifier. The results indicate that our multi-layer classifier
model exhibits robust generalization capabilities.
Keywords
body-worn sensors; multi layer classifier; random forest; kernel fisher discriminant analysis; SVM; stepwise regression
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
