Using ambient intelligence for physiological monitoring

Curtis, Dorothy; Bailey, Jacob; Pino, Esteban J.; Stair, Thomas O.; Vinterbo, Staal A.; Waterman, Jason; Shih, Eugene; Guttag, John V.; Greenes, Robert A.; Ohno-Machado, Lucila

doi:10.3233/ais-2009-0018

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…A pervasive neural network algorithm was utilized by Kerdegari et al on smart phones to monitor older individuals' activities, and identified the occurrence of falls [15]. Ambient intelligence was used to gather information from an environment to assess patients' vital signs and locations in the waiting area of a hospital emergency department by Curtis et al [16]. In addition, the SVM classifier was used to analyze the minimum foot clearance owing to aging by Begg et al [17].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Soangra

Lockhart

2020

Sci

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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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Soangra

Lockhart

2020

Sci

View full text Add to dashboard Cite

show abstract

“…Kerdegari et al utilized a pervasive neural network algorithm on smart phones to monitor elderly individuals' activities, and identified the occurrence of falls [15]. Curtis et al used ambient intelligence to gather information from an environment to assess patients' vital signs and locations in the waiting area of a hospital emergency department [16]. Finally, Begg et al used the SVM classifier to analyze the minimum foot clearance owing to aging [17].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Soangra²,

Lockhart³

2020

Sci

View full text Add to dashboard Cite

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.

show abstract

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

Zhang¹,

Soangra²,

Lockhart³

2020

Sci

View full text Add to dashboard Cite

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.

show abstract

Using ambient intelligence for physiological monitoring

Cited by 5 publications

References 16 publications

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

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

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

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

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