Human-sitting-pose detection using data classification and dimensionality reduction

Nunez-Godoy, Santiago; Alvear-Puertas, Vanessa; Realpe-Godoy, Staling; Pujota-Cuascota, Edwin; Farinango-Endara, Henry; Navarrete-Insuasti, Ivan; Vaca-Chapi, Franklin; Rosero-Montalvo, Paúl D.; Peluffo, Diego

doi:10.1109/etcm.2016.7750822

Cited by 13 publications

(2 citation statements)

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“…In the principal program, we call the file to read and compare each matrix's point with new data acquired for the accelerometer sensor and determinate the shorter distance and predict the position with the matrix label. The picture 8 shows a pseudocode that describes the K-NN function implemented [19]. …”

Section: Discussionmentioning

confidence: 99%

Human Sit Down Position Detection Using Data Classification and Dimensionality Reduction

Rosero-Montalvo¹,

Jaramillo²,

Flores³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Section: Discussionmentioning

confidence: 99%

Human Sit Down Position Detection Using Data Classification and Dimensionality Reduction

Rosero-Montalvo¹,

Jaramillo²,

Flores³

et al. 2017

Adv. sci. technol. eng. syst. j.

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“…These new features will be more expressive than the original ones, thus allowing a better analysis of the data. This is the approach followed by proposals such as one of Nu ñez-Godoy et al [35], who employ Feature Extraction's Principal Component Analysis (PCA) algorithm to reduce the high-dimensionality of sensor-gathered data to detect patterns in human-sitting-poses. The application of these techniques helps improve the results yielded by the initial prediction models, using the original feature set, while reducing the computational cost.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile

Rojo

Pinho²,

Fonseca³

et al. 2023

IEEE Trans. Emerg. Topics Comput.

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Healthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue-reducing these parameters to a smaller set with the most "determinant" information. However, existing proposals usually focus on classification problems-aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses. However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead. Proposals using Feature Selection here are very limited. This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults. Data from 829 functional profiles assessments in 49 residential homes were used in this study. The number of features was reduced from 31 to 25-with a correlation between inputs and outputs of 0.99 according to the R 2 score and a Mean Square Error (MSE) of 0.11-or to 14 features-with a correlation of 0.98 and MSE of 5.73.

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Intelligence in Embedded Systems: Overview and Applications

Rosero-Montalvo

Batista

Jaramillo

et al. 2018

Proceedings of the Future Technologies Conference (FTC) 2018

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Human-sitting-pose detection using data classification and dimensionality reduction

Cited by 13 publications

References 10 publications

Human Sit Down Position Detection Using Data Classification and Dimensionality Reduction

Human Sit Down Position Detection Using Data Classification and Dimensionality Reduction

Analyzing the Performance of Feature Selection on Regression Problems: A Case Study on Older Adults’ Functional Profile

Intelligence in Embedded Systems: Overview and Applications

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