Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Pires, Ivan Miguel; García, Nuno M.; Flórez-Revuelta, Francisco

doi:10.1007/978-3-319-40114-0_14

Cited by 24 publications

(50 citation statements)

References 30 publications

(23 reference statements)

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“…The sensors that are available in the mobile devices, including accelerometer, gyroscope, and magnetometer sensors, allow the capture of data that can be used to the recognition of ADL [2]. This study focused on the architecture defined in [4][5][6], composed by several steps, such as data acquisition, data cleaning, feature extraction, data fusion and artificial intelligence methods. Based on the literature review, the proposed ADL for the recognition with motion and magnetic sensors are running, walking, going upstairs, going downstairs, and standing.…”

Section: Discussionmentioning

confidence: 99%

“…The number of training iterations may influence the results of the neural networks, defining the maximum number of 10 6 , 2x10 6 and 4x10 6 iterations, in order to identify the best number of training iterations with best results.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…After this research, the methods that should be implemented in the framework for the recognition of ADL defined in [4][5][6] is a function of the number of sensors available in the off-the-shelf mobile device. According to the results available in [13], if the mobile device has only the accelerometer sensor, the method that should be implemented is the Deep Neural Networks, verifying with this research the best methods for the use of the datasets defined in the sections 3.3.1 and 3.3.2.…”

Section: Artificial Intelligencementioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

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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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“…The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.These methods are included in the development of a framework for the recognition of ADL and their environments, proposed in [5][6][7], composed by several modules, such as data acquisition, data processing, data fusion, and artificial intelligence methods. However, the data processing is composed by some steps, such as data cleaning and feature extraction, and the data fusion and artificial intelligence techniques are applied at the same time for the achievement of the final purpose of the recognition of ADL and their environments.…”

mentioning

confidence: 99%

“…These methods are included in the development of a framework for the recognition of ADL and their environments, proposed in [5][6][7], composed by several modules, such as data acquisition, data processing, data fusion, and artificial intelligence methods. However, the data processing is composed by some steps, such as data cleaning and feature extraction, and the data fusion and artificial intelligence techniques are applied at the same time for the achievement of the final purpose of the recognition of ADL and their environments.…”

mentioning

confidence: 99%

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2019

Stat., optim. inf. comput.

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The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.These methods are included in the development of a framework for the recognition of ADL and their environments, proposed in [5][6][7], composed by several modules, such as data acquisition, data processing, data fusion, and artificial intelligence methods. However, the data processing is composed by some steps, such as data cleaning and feature extraction, and the data fusion and artificial intelligence techniques are applied at the same time for the achievement of the final purpose of the recognition of ADL and their environments. The advantages of recognition of the environments are not limited to the increasing of the number of ADL recognized, but it allows the framework to combine the environments with the ADL recognition returning different results, e.g., the user is walking on the street.The topic related to the recognition of the ADL has some studies available in the literature [8][9][10][11][12][13], but there are no studies that uses all sensors available on the off-the-shelf mobile devices, however the Artificial Neural Networks (ANN) is one of the most used methods in this topic. Based on our previous studies using motion and magnetic sensors for the development of the framework for the recognition of ADL and their environments [4,14], this study proposes the creation of several methods to adapt the framework to the number of sensors available in off-the-shelf mobile devices. Some methods using different combinations of sensors are presented in previous studies [4,14], such as the method using accelerometer data, the meth...

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A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

Pires

Marques

García

et al. 2020

Advances in Intelligent Systems and Computing

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Smart environments and mobile devices are two technologies that when combined may allow the recognition of Activities of Daily Living (ADL) and its environments. This paper focuses on the literature review of the existing machine learning methods for the recognition of ADL and its environments, by means of comparison jointly with a proposal of a novel taxonomy in this context. The sensors used for this purpose depends on the nature of the system and the ADL to recognize. The available in the mobile devices are mainly motion, magnetic and location sensors, but the sensors available in the smart environments may have different types. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment.

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Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Cited by 24 publications

References 30 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

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

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