Human Detection and Monitoring System Using Multi Sensor Data Fusion

Walke, S. M.; Marne, Shubham; Langhe, Omkar; Kulkarni, Ashwin; Mahajan, Sandip

doi:10.1109/iccons.2018.8662979

Cited by 1 publication

(1 citation statement)

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“…Many of the works developed in the area of environmental intelligence are based on the concept of recognition and classification of human activities, through which it is possible to detect the type of activity performed by a person or group of people and offer a specific service or solution. is area of recognition and classification of human activities has had a huge boom in recent years, and there have been many works developed [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

García-Domínguez

Galván-Tejada

Zanella-Calzada

et al. 2020

Mobile Information Systems

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In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.

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