High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

Abstract

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

“…The biggest bene t of LFDA is that it can obtain good between-class separation in the feature subspace while preserving the within-class local structure [16] . It also integrates the advantages of both Fisher's linear discriminant analysis (LDA) and locality-preserving projections (LPP) while bypassing the requirement for a Gaussian distribution [17][18][19][20] . Figure 4 (a) and (b) are the before and after projection transformation feature distribution of one testing sample.…”

“…In this study, we were able to develop an alternative method named local Fisher's discriminant analysis (LFDA) that integrates the advantages of both Fisher's linear discriminant analysis (LDA) and locality-preserving projections (LPP), meanwhile bypassing the limitation of gaussian distribution. [12][13][14][15]…”

Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

“…Projection transformation is an advanced method for acquiring the maximum reduced bypassing the requirement for a Gaussian distribution [16][17][18][19] . Figure 4…”

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

“…We first compare our method’s HAR performance with the following state of the art approaches: using the lower body OPPORTUNITY dataset, we compare our approach to deep convolutional LSTM networks proposed by Ordónez and Roggen [ 28 ]; using the UCI HAR dataset, we compare our approach to four other approaches, i.e., one using SVM [ 10 ] (UCI HAR dataset owner), one using a two-dimensional activity image-based DCNN (DCNN+) [ 12 ], one using a fast Fourier transform-based DCNN (FFT+Convnet) [ 14 ], and one using a Three-Stage Continuous HMM (TSCHMM) [ 13 ]. We refer to our 1D CNN model with test data sharpening as CNN+Sharpen in the results section.…”

“…As HAR research matured, several benchmark human activity datasets [ 7 , 8 , 9 , 10 , 11 ] became publicly available, allowing straightforward comparison of different activity recognition methods. Recently, many state of the art approaches employ deep a Convolutional Neural Network (CNN) over other machine learning techniques, and these approaches, for example, exhibit high activity recognition accuracy that exceed 95% [ 12 , 13 , 14 ] on the benchmark Human Activity Recognition Using Smartphones Data Set (UCI HAR dataset) [ 10 ] that contain six activities. As deep learning approaches simultaneously learn both the suitable representations (i.e., features) and activity classifier from data, less attention was given to the explicit feature processing for HAR.…”