Activity recognition based on accelerometer sensor using combinational classifiers

Abstract

In recent years, people nowadays easily to contact each other by using smartphone. Most of the smartphone now embedded with inertial sensors such accelerometer, gyroscope, magnetic sensors, GPS and vision sensors. Furthermore, various researchers now dealing with this kind of sensors to recognize human activities incorporate with machine learning algorithm not only in the field of medical diagnosis, forecasting, security and for better live being as well. Activity recognition using various smartphone sensors can be considered as a one of the crucial tasks that needs to be studied. In this paper, we proposed various combination classifiers models consists of J48, Multi-layer Perceptron and Logistic Regression to capture the smoothest activity with higher frequency of the result using vote algorithm. The aim of this study is to evaluate the performance of recognition the six activities using ensemble approach. Publicly accelerometer dataset obtained from Wireless Sensor Data Mining (WISDM) lab has been used in this study. The result of classification was validated using 10fold cross validation algorithm in order to make sure all the experiments perform well.

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

“…They implemented the J48 decision tree, bagging, decision table, and Naïve Bayes methods, reporting an accuracy of 94% [38].In [39], the authors implemented a decision tree classifier with several features, such as mean, median, maximum, minimum, RMS, standard deviation, median deviation, interquartile range, energy, entropy, skewness, and kurtosis of the accelerometer data, for the recognition of running, walking, standing, sitting, and laying activities with a reported accuracy of 99.5%.In [40], the authors implemented a SVM method with several features, such as RMS, variance, correlation and energy of the accelerometer data, for the recognition of walking, running, cycling, and hopping with a reported average accuracy of 97.69%.The authors of [41] implemented an ANN with mean, standard deviation, and percentiles of the magnitude of the accelerometer data as features, with a reported accuracy of 92% in the recognition of standing, walking, running, going up stairs, going down stairs, and running.In addition, the authors of [42] implemented an ANN with some features, such as mean, maximum, minimum, difference between maximum and minimum, standard deviation, RMS, Parseval's Energy, correlation between axis, kurtosis, skewness, ratio of the maximum and minimum values in the FFT, difference between the maximum and minimum values in the FFT, median of peaks, median of troughs, number of peaks, number of troughs, average distance between two consecutive peaks, average distance between two consecutive troughs, and ratio of the average values of peaks and troughs based on a window of the accelerometer data. The activities recognized by the method are resting, walking, cycling, jogging, running, and driving [42], reporting an accuracy between 57.53% to 97.58%.The authors of [43] implemented a SVN method with mean, minimum, maximum, standard deviation, energy, mean absolute deviation, binned distribution, and percentiles of the magnitude of acceleration as features, with a reported overall accuracy of 94.3% in the recognition of running, staying, walking, going up stairs, and going down stairs.In [44], a method that combines the J48 decision tree, MLP, and Likelihood Ratio (LR) models was implemented and it uses the accelerometer data for the extraction of the minimum, maximum, mean, standard deviation, and zero crossing rate for each axis, and the correlation between axis for the application in the model, in order to recognize the going down stairs, jogging, sitting, standing, going up stairs, and walking activities with a reported accuracy of 97%.The SVM method is also implemented with accelerometer and Global Positioning System (GPS) data for the recognition of walking, standing, and running activities, but the accelerometer features used are minimum, maximum, mean, standard deviation, correlation, and median crossing [68], reporting an accuracy of 97.51%.Another study that uses SVM method makes use of accelerometer, gyroscope, and barometer sensors for the identification of walking, going up stairs, going down stairs, standing, going elevator up, and going elevator down, extraction the mean, mean of 1 st half, mean of 2 nd half, difference of means, slope, variance, standard deviation, RMS, and Signal Magnitude Area [69], reporting an accuracy between 87.45% and 99.25%.The SVM method is also used with the accelerometer, the gyroscope, the barometer, and the GPS sensors...…”

Deep learning for human activity recognition: A resource efficient implementation on low-power devices

“…To further improve recognition accuracy, some researchers have demonstrated that ensemble classification methods, which combine multiple learning algorithms together, can achieve better outcomes in some cases. Catal et al [7] and Zainudin et al [8], for example, combine decision trees, multilayer perceptron, and logistic regression for HAR. Their results show that ensemble learning can obtain significant improvements for activity recognition when compared to what each learning algorithm can achieve individually with shallow features.…”

“…Features, such as mean [1], Fourier transforms [2], and symbols [3], are typically extracted from segments of data and then trained using classification methods [4]- [8]. However, these methods are still limited to the specific classification tasks that they were designed for.…”

Feature Selection Optimization using Hybrid Relief-f with Self-adaptive Differential Evolution

“…In any classification problem, feature selection method must be validating using ML classifier model. This process to ensure the selected features could optimize the classification performance [7]. In theoretically, feature selection can be grouped into two main categories; filter methods and wrapper methods [2], [8]- [10].…”