mHealth Sensors and Applications for Personal Aid

Sousa, Paula; Sabugueiro, Daniel; Felizardo, Virginie; Couto, Rafael Aredes; Pires, Ivan Miguel; García, Nuno M.

doi:10.1007/978-3-319-12817-7_12

Cited by 37 publications

(26 citation statements)

References 9 publications

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“…Due to their widespread integration with the users' lifestyles, mobile devices can support personal activities anywhere at any time. In addition, these devices integrate numerous sensors that allow signal acquisition related to different aspects of medical or assisted living purposes in different environments [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Research on the Classification and Applicability of the Mobile Health Applications

Pires

Marques

García

et al. 2020

JPM

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Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.

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Section: Introductionmentioning

confidence: 99%

A Research on the Classification and Applicability of the Mobile Health Applications

Pires

Marques

García

et al. 2020

JPM

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“…Furthermore, it can be useful to support sports activities and stimulate the practice of physical activity in teenagers [4]. The development of these systems is included in the research of Ambient Assisted Living (AAL) systems and Enhanced Living Environments (ELE) [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

et al. 2020

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The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.

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“…The recent advancements of the technology (Cardinale and Varley, 2017;Kim et al, 2016;Ni Scanaill et al, 2011;Patel et al, 2012;Sousa et al, 2015) and the presence of sensors in the commonly used off-the-shelf mobile devices (Shahriyar et al, 2010;Stankevich et al, 2012;Steele, 2011;Tian et al, 2019;Ventola, 2014) allow the development of solutions for the identification of the human daily living activities in order to monitor its lifestyles, e.g., the creation of a Personal Digital Life Coach (Garcia, 2016). However, the accuracy of these systems and their resilience of fails is essential for the recognition of activities in different environments (Dimitrievski et al, 2016a;Pires et al, 2017Pires et al, , 2018aZdravevski et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Imputation Technique for Human Activity Recognition

Pires

Hussain²,

García

et al. 2020

Preprint

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Abstract The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.

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mHealth Sensors and Applications for Personal Aid

Cited by 37 publications

References 9 publications

A Research on the Classification and Applicability of the Mobile Health Applications

A Research on the Classification and Applicability of the Mobile Health Applications

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

An Efficient Data Imputation Technique for Human Activity Recognition

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