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

Sci ◽  
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.

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

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 62
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.

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

Sci ◽  
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.

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

Sci ◽  
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.

scholarly journals On the Sparseness and Generalization Capability of Least Squares Support Vector Machines

2015 ◽  
Vol 3 (3) ◽  
pp. 279-288 ◽  
Author(s):  
Aijun Yan ◽  
Xiaoqian Huang ◽  
Hongshan Shao
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Principal Component ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Function Parameter ◽  
Support Vector ◽  
Generalization Capability ◽  
Penalty Factor ◽  
Vector Machines

AbstractCompared with standard support vector machines (SVM), sparseness is lost in the modeling process of least squares support vector machines (LS-SVM), causing limited generalization capability. An improved method using quadratic renyi-entropy pruning is presented to deal with the above problems. First, a kernel principal component analysis (KPCA) is used to denoise the training data. Next, the authors use the genetic algorithm to estimate and optimize the kernel function parameter and penalty factor. Then, pick the subset that has the largest quadratic entropy to train and prune, and repeat this process until the cumulative error rate reaches the condition requirement. Finally, comparing experiments on the data classification and regression indicates that the proposed method is effective and may improve the sparseness and the generalization capability of LS-SVM model.

scholarly journals Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4681 ◽  
Author(s):  
Hao Yan ◽  
Hongbo Wang ◽  
Luige Vladareanu ◽  
Musong Lin ◽  
Victor Vladareanu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Task Difficulty ◽  
Interaction Force ◽  
Support Vector ◽  
Rehabilitation Robot ◽  
Training Task ◽  
K Value ◽  
Svm Algorithm ◽  
Vector Machines ◽  
And Training

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients’ participation in training. By establishing the static equation of the mechanical leg, the man–machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers’ motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm.

Human Motion Patterns Recognition based on RSS and Support Vector Machines

2020 ◽  
Author(s):  
Sameer Ahmad Bhat ◽  
Abolfazl Mehbodniya ◽  
Ahmed Elsayed Alwakeel ◽  
Julian Webber ◽  
Khalid Al-Begain
Keyword(s):  
Support Vector Machines ◽  
Human Motion ◽  
Support Vector ◽  
Motion Patterns ◽  
Vector Machines

An Indoor Localization of WiFi Based on Support Vector Machines

2014 ◽  
Vol 926-930 ◽  
pp. 2438-2441 ◽  
Author(s):  
Feng Yu ◽  
Ming Hua Jiang ◽  
Jing Liang ◽  
Xiao Qin ◽  
Ming Hu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Basis Function ◽  
Indoor Localization ◽  
Signal Strength ◽  
Kernel Functions ◽  
Support Vector ◽  
Localization Accuracy ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Wifi Signal

The recent growing interest for indoor localization-based services has created a need for more accurate and real-time indoor localization solutions. Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquity. In this paper, we utilize the information of the signal strength received from the surrounding access points (APs) to determine the user localization. The propose algorithm based on support vector machines (SVM) algorithm, and comparing with three kernel functions, radial basis function (RBF) performs best of all. Experimental results indicate that the proposed algorithm leads to improvement on localization accuracy.

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