An effective approach for human activity recognition on smartphone

Paul, Pinky; George, Thomas

doi:10.1109/icetech.2015.7275024

Cited by 68 publications

(42 citation statements)

References 3 publications

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“…Recently, several studies have been performed for the recognition of ADL using several sensors [7][8][9][10][11][12], proving the reliability of the use of Artificial Neural Networks (ANN) for the recognition of ADL. Due to the limitation of number of sensors available in the off-the-shelf mobile devices, based on the previous study The features used in the recognition of the ADL are presented in table 2, showing that the mean, standard deviation, maximum, minimum, energy, inter-quartile range, correlation coefficients, median, and variance are the most used features, with more relevance for mean, standard deviation, maximum, and minimum.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2017

Preprint

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Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors' data, reports an accuracy of 89.51%.[13] that uses only the accelerometer sensor, this study proposes the creation of two different methods for the recognition of ADL using different number of sensors in order to adapt the method according to the number of sensors available. Firstly, it proposes the fusion of the data acquired from the accelerometer, and the magnetometer sensors. Secondly, it proposes the fusion of the data acquired from the accelerometer, gyroscope, and magnetometer sensors. The ADL proposed for the recognition are running, walking, going upstairs, going downstairs, and standing, consisting this research on the analysis of the performance of three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning. A dataset used for this research is composed by the sensors' data acquired in several experiments with people aged between 16 and 60 years old, distinct lifestyles, and a mobile device in the front pocket of their pants, performing the proposed ADL. This research was conducted with the use of three Java libraries, such as Neuroph [14], Encog [15], and DeepLearning4j [16], and different datasets of features, in order to identify the best dataset of features and type of ANN for the recognition of ADL, verifying that the best accuracy for the recognition of ADL with the two different methods proposed was achieved with Deep Learning methods.This paragraph concludes the section 1, and this paper is organized as follows: Section 2 summarizes the literature review for the use of data fusion techniques with accelerometer, gyroscope, and magnetometer sensors; Section 3 presents the methods used on each stage of the architecture proposed. The results obtained are presented in the Section 4, presenting the discussion about these results in the Section 5. The conclusions of this study are presented in the Section 6. Related WorkData fusion techniques may be used with the data acquired from motion and magnetic sensors available in the off-the-shelf mobile devic...

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Section: Introductionmentioning

confidence: 99%

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2017

Preprint

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“…However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing.…”

mentioning

confidence: 99%

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

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

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This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%. Following the previous research studies [4][5][6], the recognition of the ADL is composed by several steps, such as the data acquisition, the data processing, composed by data cleaning, data imputation, and feature extraction, the data fusion, and the artificial intelligence methods for the concrete identification of the ADL. However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing. The datasets are composed with raw accelerometer data acquired by individual with ages ranged between 16 and 60 years old and different lifestyles, with a mobile device in the pocket. For the implementation of these types of ANN are used several datasets with different sets of features, identifying the best features to increase the accuracy of the recognition, and three Java libraries are used for the implementation of the different methods, such as Neuroph [13], Encog [14], and DeepLearning4j [15], achieving the best accuracy with Deep Learning methods.The remaining sections of this paper are organized as follows: Section 2 presents a brief literature review re...

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“…In [8] k-NN was used to recognize four activities ("Standing", "sitting", "walking" and "running"), collecting data with a smartphone, and using as features: mean, standard deviation, maximum value, and minimum value, obtaining 92% of accuracy, which is considered a low accuracy, taken into account the activities are considered simple ones. In [2], k-NN is used to recognize 13 activities, among them some complex activities as "dish wash", "sweep" and "vacuum clean".…”

Section: Human Activity Recognition (Har) For Manicdepressive and Agementioning

confidence: 99%

Development of remote health monitoring devices: towards IoT for manic-depressive, ageing and obese people

Coelho¹,

Vervloet²,

Gašpar³

et al. 2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

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This work presents the development of devices for remote monitoring of manic-depressive, elderly, and obese people to be used at IoT (Internet of Things) context. These people are monitored through a wearable device and an instrumented digital scale. For monitoring of manic-depressive and ageing people, several techniques were analyzed (Decision tree, kNearest Neighbor, Naive Bayes, and Artificial Neural Network) to allow human activity recognition. k-Nearest Neighbor got the best result (86% of accuracy) for four different trials in six different activities (walking, running, going upstairs, going downstairs, standing up, and sitting). For monitoring of obese people, an app was developed for a smartphone, which obtains remote weight measurements with 0.01 kg of accuracy.

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An effective approach for human activity recognition on smartphone

Cited by 68 publications

References 3 publications

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

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

Development of remote health monitoring devices: towards IoT for manic-depressive, ageing and obese people

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