Single Wearable Accelerometer-Based Human Activity Recognition via Kernel Discriminant Analysis and QPSO-KELM Classifier

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Human Activity Recognition from Accelerometer Data Using a Wearable Device

Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computation...

متن کامل

Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree

Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree

متن کامل

Contextual Constraints Based Kernel Discriminant Analysis for Face Recognition

In this paper, an improved subspace learning method using contextual constraints based linear discriminant analysis (CCLDA) is proposed for face recognition. The linear CCLDA approach does not consider the higher order nonlinear information in facial images. However, the wide face variations posed by some factors, such as viewpoint, illumination and expression, existing in non-linear subspaces ...

متن کامل

Face recognition using kernel scatter-difference-based discriminant analysis

There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analys...

متن کامل

Subspace Kernel Discriminant Analysis for Speech Recognition

Kernel Discriminant Analysis (KDA) has been successfully applied to many pattern recognition problems. KDA transforms the original problem into a space of dimension N where N is the number of training vectors. For speech recognition, N is usually prohibitively high increasing computational requirements beyond current computational capabilities. In this paper, we provide a formulation of a subsp...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE Access

سال: 2019

ISSN: 2169-3536

DOI: 10.1109/access.2019.2933852