کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6873276 1440632 2018 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A robust human activity recognition system using smartphone sensors and deep learning
ترجمه فارسی عنوان
یک سیستم تشخیص فعالانه انسان با استفاده از سنسورهای گوشی هوشمند و یادگیری عمیق
کلمات کلیدی
به رسمیت شناختن فعالیت سنسورها، گوشیهای هوشمند، شبکه اعتقادی درونی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
In last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human-computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual's degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 81, April 2018, Pages 307-313
نویسندگان
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