CSDL Bài trích Báo - Tạp chí
chủ đề: Cybernetics
1 Human gait analysis using hybrid convolutional neural networks / Khang Nguyen, Viet V. Nguyen, Nga T. Mai, An H. Nguyen, Anh V. Nguyen // .- 2023 .- Tập 39 - Số 2 .- P. 125-142 .- 005
This paper analyzes the combination of IMU sensors and electromyography sensors (EMG) to improve the identification accuracy of human movements. We propose the hybrid convolutional neural network (CNN) and long short-term memory neural network (LSTM) for the human gait analysis problem to achieve an accuracy of 0.9418, better than other models including pure CNN models. By using CNN's image classification advancements, we analyze multivariate time series sensor signals by using a sliding window to transform sensor data into image representation and principal component analysis (PCA) to reduce the data dimensionality. To tackle the dataset imbalance issue, we re-weight our model loss by the inverse effective number of samples in each class. We use the human gait HuGaDB dataset with unique characteristics, for gait analysis.
2 Two-phase combined model to improve the accuracy of indoor location fingerprinting / Van Hieu Vu, Binh Ngo Van, Tung Hoang Do Thanh // Tin học & Điều khiển học .- 2022 .- P. 377-403 .- 621
In this paper, present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduction method. Specifically, our research is focused on the combination of different regression and classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extra Tree Regressor (extraTree), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR) and Linear Regression (LiR) to create a new data set and models that can be used in the training phase. These proposed models are tested on the UJIIndoorLoc 1 dataset. Our experimental results show a prediction accuracy of 98.73% by floor, and an estimated accuracy of 99.62% and 99.52% respectively by longitude and latitude. When compared with the results of the models in which we use independent algorithms, and of other researches that have different models using the same algorithms and on the same dataset, most of our results are better.