scholarly journals From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 184 ◽  
Author(s):  
Ivan Pires ◽  
Nuno Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
State Of The Art ◽  
The State ◽  
Sensor Data ◽  
Battery Life ◽  
Sensor Data Fusion ◽  
Processing Power

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

scholarly journals Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 766 ◽  
Author(s):  
Hemant Ghayvat ◽  
Muhammad Awais ◽  
Sharnil Pandya ◽  
Hao Ren ◽  
Saeed Akbarzadeh ◽  
...  
Keyword(s):  
Data Fusion ◽  
Daily Living ◽  
Research Work ◽  
Well Being ◽  
Sensor Data ◽  
Single Type ◽  
Health Improvement ◽  
Activity Of Daily Living ◽  
Ground Truth Data ◽  
Sensor Data Fusion

Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

scholarly journals Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Mobile Devices’ Sensors

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Fusion ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Automatic Identification ◽  
Internet Connection ◽  
Processing Data

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

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

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Pattern Recognition Techniques ◽  
Artificial Intelligence Methods ◽  
Processing Power

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 based on 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 model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.

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

2019 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Pattern Recognition Techniques ◽  
Artificial Intelligence Methods ◽  
Processing Power

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 based on 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 model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.

scholarly journals Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices

2018 ◽  
Vol 47 ◽  
pp. 78-93 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante ◽  
...  
Keyword(s):  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Magnetic Sensors

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

2019 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Pattern Recognition Techniques ◽  
Artificial Intelligence Methods ◽  
Processing Power

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 based on 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 model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.

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

2017 ◽  
Author(s):  
Ivan Miguel Serrano Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Magnetic Sensors ◽  
Pattern Recognition Techniques ◽  
Processing Data ◽  
Artificial Intelligence Methods

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%.

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