Smartphone Naïve Bayes Human Activity Recognition Using Personalized Datasets

2020 ◽  
Vol 24 (5) ◽  
pp. 685-702
Author(s):  
Moses L. Gadebe ◽  
◽  
Okuthe P. Kogeda ◽  
Sunday O. Ojo
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Human Activity Recognition ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Vector Machines

Recognizing human activity in real time with a limited dataset is possible on a resource-constrained device. However, most classification algorithms such as Support Vector Machines, C4.5, and K Nearest Neighbor require a large dataset to accurately predict human activities. In this paper, we present a novel real-time human activity recognition model based on Gaussian Naïve Bayes (GNB) algorithm using a personalized JavaScript object notation dataset extracted from the publicly available Physical Activity Monitoring for Aging People dataset and University of Southern California Human Activity dataset. With the proposed method, the personalized JSON training dataset is extracted and compressed into a 12×8 multi-dimensional array of the time-domain features extracted using a signal magnitude vector and tilt angles from tri-axial accelerometer sensor data. The algorithm is implemented on the Android platform using the Cordova cross-platform framework with HTML5 and JavaScript. Leave-one-activity-out cross validation is implemented as a testTrainer() function, the results of which are presented using a confusion matrix. The testTrainer() function leaves category K as the testing subset and the remaining K-1 as the training dataset to validate the proposed GNB algorithm. The proposed model is inexpensive in terms of memory and computational power owing to the use of a compressed small training dataset. Each K category was repeated five times and the algorithm consistently produced the same result for each test. The result of the simulation using the tilted angle features shows overall precision, recall, F-measure, and accuracy rates of 90%, 99.6%, 94.18%, and 89.51% respectively, in comparison to rates of 36.9%, 75%, 42%, and 36.9% when the signal magnitude vector features were used. The results of the simulations confirmed and proved that when using the tilt angle dataset, the GNB algorithm is superior to Support Vector Machines, C4.5, and K Nearest Neighbor algorithms.

scholarly journals A DINÂMICA DO USO E OCUPAÇÃO DA TERRA NO LAGO POPÓO (BOLÍVIA) ENTRE 1985 E 2017 UTILIZANDO CLASSIFICAÇÃO VOLTADA AO OBJETO EM DADOS LANDSAT

2020 ◽  
Vol 38 (4) ◽  
pp. 1073-1082
Author(s):  
Florença das Graças MOURA ◽  
Álvaro Xavier FERREIRA ◽  
Tati ALMEIDA ◽  
Jérémie GARNIER ◽  
Rejane Ennes CICERELLI ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Landsat 8 ◽  
K Nearest Neighbor ◽  
Vector Machines

O lago Poópo é o segundo maior lago da Bolívia e atualmente vem passando por uma forte crise hídrica que alguns autores associam diretamente a mudança de ocupação da terra. Neste trabalho foi realizada a classificação do uso e ocupação do solo na sub-bacia P6 do lago entre os anos de 1985 e 2017. Foi analisado o desempenho dos classificadores SVM (Support Vector Machines), KNN (K-Nearest Neighbor) e MaxVer (Máxima Verossimilhança). A classificação que obteve melhor acurácia foi a gerada pelo classificador SVM, em que o valor do índice Kappa foi de 82,28% e 83,7% para as imagens Landat-5 e Landsat-8, respectivamente, e a exatidão global foi de 92% para ambas as imagens. A partir das classificações geradas foi verificado que as maiores alterações se deram nas classes de vegetação nativa, agricultura e área úmida. A perda de área úmida na sub-bacia vem ocorrendo desde 1995, 15 anos antes do aumento da atividade agrícola, que começou a partir de 2010. Assim, diversos são os fatores que podem estar contribuindo com essa redução acelerada dos corpos de água, como variações climáticas locais e as atividades antrópicas que interferem no ciclo hidrológico de forma regional.

scholarly journals Human Activity Recognition from the Acceleration Data of a Wearable Device. Which Features Are More Relevant by Activities?

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1242 ◽  
Author(s):  
Macarena Espinilla ◽  
Javier Medina ◽  
Alberto Salguero ◽  
Naomi Irvine ◽  
Mark Donnelly ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Human Activity Recognition ◽  
Classification Algorithm ◽  
Wearable Device ◽  
K Nearest Neighbor ◽  
Multiple Features ◽  
Acceleration Data

Data driven approaches for human activity recognition learn from pre-existent large-scale datasets to generate a classification algorithm that can recognize target activities. Typically, several activities are represented within such datasets, characterized by multiple features that are computed from sensor devices. Often, some features are found to be more relevant to particular activities, which can lead to the classification algorithm providing less accuracy in detecting the activity where such features are not so relevant. This work presents an experimentation for human activity recognition with features derived from the acceleration data of a wearable device. Specifically, this work analyzes which features are most relevant for each activity and furthermore investigates which classifier provides the best accuracy with those features. The results obtained indicate that the best classifier is the k-nearest neighbor and furthermore, confirms that there do exist redundant features that generally introduce noise into the classification, leading to decreased accuracy.

Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

2019 ◽  
Vol 10 (2) ◽  
pp. 34-47 ◽  
Author(s):  
Bagavathi Lakshmi ◽  
S.Parthasarathy
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Action Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Activity Recognition ◽  
Machine Learning Algorithms ◽  
Support Vector

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

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