Posture recognition using sensing blocks

Yulong Xi; Seoungjae Cho; Kyhyun Um; Kyungeun Cho

doi:10.1007/978-981-10-0281-6_35

Posture recognition using sensing blocks

Yulong Xi
, Seoungjae Cho
, Kyhyun Um
, Kyungeun Cho

Department of Computer Science and Artificial Intelligence

Dongguk University

Research output: Contribution to journal › Article › peer-review

Abstract

Posture recognition has been investigated in a variety of fields including medicine, HCI, and video games. In particular, it is important to improve the generality and recognition speed of posture recognition to improve the user experience in diverse kinds of user environments. This paper proposes a posture recognition algorithm with high generality and recognition speed. The algorithm is able to recognize a variety of postures, regardless of the number of joints recognized in human skeleton data. Furthermore, experimental results show that the method can quickly process a large quantity of data and recognize 22 postures in real time.

Original language	English
Pages (from-to)	243-247
Number of pages	5
Journal	Lecture Notes in Electrical Engineering
Volume	373
DOIs	https://doi.org/10.1007/978-981-10-0281-6_35
State	Published - 2015

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Human-computer interface
Pattern recognition
Posture recognition
Support vector machine

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10.1007/978-981-10-0281-6_35

Cite this

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title = "Posture recognition using sensing blocks",

abstract = "Posture recognition has been investigated in a variety of fields including medicine, HCI, and video games. In particular, it is important to improve the generality and recognition speed of posture recognition to improve the user experience in diverse kinds of user environments. This paper proposes a posture recognition algorithm with high generality and recognition speed. The algorithm is able to recognize a variety of postures, regardless of the number of joints recognized in human skeleton data. Furthermore, experimental results show that the method can quickly process a large quantity of data and recognize 22 postures in real time.",

keywords = "Human-computer interface, Pattern recognition, Posture recognition, Support vector machine",

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AU - Xi, Yulong

AU - Cho, Seoungjae

AU - Um, Kyhyun

AU - Cho, Kyungeun

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PY - 2015

Y1 - 2015

N2 - Posture recognition has been investigated in a variety of fields including medicine, HCI, and video games. In particular, it is important to improve the generality and recognition speed of posture recognition to improve the user experience in diverse kinds of user environments. This paper proposes a posture recognition algorithm with high generality and recognition speed. The algorithm is able to recognize a variety of postures, regardless of the number of joints recognized in human skeleton data. Furthermore, experimental results show that the method can quickly process a large quantity of data and recognize 22 postures in real time.

AB - Posture recognition has been investigated in a variety of fields including medicine, HCI, and video games. In particular, it is important to improve the generality and recognition speed of posture recognition to improve the user experience in diverse kinds of user environments. This paper proposes a posture recognition algorithm with high generality and recognition speed. The algorithm is able to recognize a variety of postures, regardless of the number of joints recognized in human skeleton data. Furthermore, experimental results show that the method can quickly process a large quantity of data and recognize 22 postures in real time.

KW - Human-computer interface

KW - Pattern recognition

KW - Posture recognition

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/84951271378

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SP - 243

EP - 247

JO - Lecture Notes in Electrical Engineering

JF - Lecture Notes in Electrical Engineering

ER -

Posture recognition using sensing blocks

Abstract

UN SDGs

Keywords

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