Object

Title: Time domain feature extraction and SVM processing for activity recognition using smartphone signals

Journal or Publication Title:

Математические вопросы кибернетики и вычислительной техники=Կիբեռնետիկայի և հաշվողական տեխնիկայի մաթեմատիկական հարցեր=Mathematical problems of computer science

Date of publication:

2013

Volume:

40

ISSN:

0131-4645

Additional Information:

click here to follow the link

Other title:

Շարժման ճանաչումը բջջային հեռախոսի ազդանշաններից ժամանակային հատկանիշների առանձնացման և SVM մեթոդի կիրառման միջոցով; Распознавание движений с помощью сигналов датчиков мобильных устройств путем выделения временных характеристических значений и применением метода опорных векторов

Coverage:

44-54

Abstract:

Automatic classification of human movement is a feature that is desired for a multitude of applications and mobile phone technology continuously evolves and incorporates more and more sensors to enable advanced applications. Combining these two concepts we can deal with “an activity recognition via smartphone sensors” problem where sensors of these devices play a core role when we deal with personalized activity tracking systems. In this paper we give an overview of the recent work in the field of activity recognition from mobile devices that can be attached to different parts of the body (pocket, wrist, and forearm). We focus on the technique of feature extraction from raw acceleration signal sequences of smartphone (mean, standard deviation, minimal and maximal signal values, correlation, median crossing). Further processing of these data allowed to classify the activity performed by the user. The core classification stage of the current approach was based on the method of “learning with the teacher” where the features of signal sequences were analyzed using the support vector machines (SVM) learning method.

Publisher:

Изд-во НАН РА

Date created:

2013-09-10

Format:

pdf

Identifier:

oai:arar.sci.am:258790

Location of original object:

ՀՀ ԳԱԱ Հիմնարար գիտական գրադարան

Object collections:

Last modified:

Dec 8, 2023

In our library since:

Jul 24, 2020

Number of object content hits:

20

All available object's versions:

https://arar.sci.am/publication/281904

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