Efficient 2D viewpoint combination for human action recognition

Saghafi, Behrouz; Rajan, Dinesh; Li, Wanqing

doi:10.1007/s10044-016-0537-z

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…For evaluations, we compare our results with several baseline methods. Baseline classifiers under comparison include support vector machine (SVM), SparseSVM43, sparse feature selection followed by a SVM (SFS + SVM) and a multiple kernel learning (MKL) framework for SVM37, in which one kernel is learned for each modality and feature type (GM, WM and SBR, as described in Section) and classification is learned over these kernels. To further evaluate the effect of the feature selection ( regularization), we run the same objective in (6) with conventional regularization on the vector α , denoted as ‘Proposed -reg’.…”

Section: Resultsmentioning

confidence: 99%

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Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Adeli

Saghafi

et al. 2017

Sci Rep

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Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

“…This is similar to multiple kernel learning frameworks3637, while building each kernel only on a single feature. Applying the newly-introduced kernel k on all training samples, we would have K = k ( X , X ).…”

Section: Kernel-based Feature Selection and Max-margin Classificationmentioning

confidence: 99%

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Adeli

Saghafi

et al. 2017

Sci Rep

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“…Tracking Indoor environments equipped with smart sensors and gadgets have allowed us to capture human motion for the better development of health-care systems [1]. In a smart home environment [2], detection of human motion has become easy, especially due to the abundance and ubiquity of sensors in gadgets and smart phones that allow real time data from such sensors [3].…”

Section: Introductionmentioning

confidence: 99%

A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System

Jalal

Quaid

Kim

2019

J. Electr. Eng. Technol.

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In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fitness industry but it also holds a prominent place in ambient smart home to monitor resident's life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offices.

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