Human Motion Patterns Recognition based on RSS and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

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Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽

10.3390/sci2030060 ◽

2020 ◽

Vol 2 (3) ◽

pp. 60

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. Lockhart

Keyword(s):

Older Adults ◽

Support Vector Machines ◽

Daily Life ◽

Human Motion ◽

Function Parameter ◽

Support Vector ◽

Activities Of Daily Life ◽

Svm Algorithm ◽

Vector Machines ◽

Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

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Detecting human motion with support vector machines

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. ◽

10.1109/icpr.2004.1334092 ◽

2004 ◽

Cited By ~ 48

Author(s):

H. Sidenbladh

Keyword(s):

Support Vector Machines ◽

Human Motion ◽

Support Vector ◽

Vector Machines

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Automatic Detection of Dynamic and Static Activities of the Elderly using a Wearable Sensor and Support Vector Machines

Sci ◽

10.3390/sci2020038 ◽

2020 ◽

Vol 2 (2) ◽

pp. 38

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. E. Lockhart

Keyword(s):

Support Vector Machines ◽

Daily Life ◽

The Elderly ◽

Human Motion ◽

Function Parameter ◽

Support Vector ◽

Activities Of Daily Life ◽

Svm Algorithm ◽

Vector Machines ◽

Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the elderly is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the elderly. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying static and dynamic activities of daily life in the elderly.

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Human motion recognition using support vector machines

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2009.06.002 ◽

2009 ◽

Vol 113 (10) ◽

pp. 1064-1075 ◽

Cited By ~ 11

Author(s):

Dongwei Cao ◽

Osama T. Masoud ◽

Daniel Boley ◽

Nikolaos Papanikolopoulos

Keyword(s):

Support Vector Machines ◽

Human Motion ◽

Support Vector ◽

Motion Recognition ◽

Vector Machines ◽

Human Motion Recognition

Download Full-text

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽

10.3390/sci2030050 ◽

2020 ◽

Vol 2 (3) ◽

pp. 50

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. Lockhart

Keyword(s):

Older Adults ◽

Support Vector Machines ◽

Daily Life ◽

Human Motion ◽

Function Parameter ◽

Support Vector ◽

Activities Of Daily Life ◽

Svm Algorithm ◽

Vector Machines ◽

Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Download Full-text