Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People

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

doi:10.5220/0006817802690275

Cited by 26 publications

(37 citation statements)

References 20 publications

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“…In conclusion, the accuracy of the ADLs recognition depends on several variables, including the conditions for data acquisition, conditions for data processing, and the use of lightweight methods (local processing) or server-side processing [72]. As presented in [72], it may cause failures on the data acquisition, collect incorrect data, or claim the nonexistence of data in some instances, causing improper recognition of ADL. To avoid some effects of inaccurate data, we implemented data cleaning methods, and data imputation methods may be useful for reducing the impacts of unavailable data.…”

Section: Resultsmentioning

confidence: 99%

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

et al. 2020

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The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

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Section: Resultsmentioning

confidence: 99%

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

et al. 2020

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“…In this stage, we first acquire the dataset for which we have to extrapolate the missing samples. Thus, we used a publicly available dataset (Pires, 2018). The dataset (Pires, 2018) includes five daily living activities, i.e., walking, running, standing, moving upstairs, and moving downstairs.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The dataset (Pires, 2018) includes five daily living activities, i.e., walking, running, standing, moving upstairs, and moving downstairs. The dataset (Pires, 2018) is acquired using three motion sensors, i.e., accelerometer, gyroscope, and magnetometer. The five daily living activities included in the dataset (Pires, 2018) are performed by 25 subjects age ranging from (20-60) with sedentary and active lifestyles.…”

Section: Data Acquisitionmentioning

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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“…In addition to using inertial sensors (i.e., accelerometers and gyroscopes) embedded in mobile devices, acoustic sensors can augment and improve the activity and environment recognition [25,26]. However, these methods have several limitations that should be considered during the development of these systems, such as availability of the sensors, weather conditions, battery lifetime, limited power processing, and memory capabilities, among others [27,28].…”

Section: Introductionmentioning

confidence: 99%

Promotion of Healthy Nutrition and Physical Activity Lifestyles for Teenagers: A Systematic Literature Review of The Current Methodologies

Villasana

Pires

Sá

et al. 2020

JPM

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Amid obesity problems in the young population and apparent trends of spending a significant amount of time in a stationary position, promoting healthy nutrition and physical activities to teenagers is becoming increasingly important. It can rely on different methodologies, including a paper diary and mobile applications. However, the widespread use of mobile applications by teenagers suggests that they could be a more suitable tool for this purpose. This paper reviews the methodologies for promoting physical activities to healthy teenagers explored in different studies, excluding the analysis of different diseases. We found only nine studies working with teenagers and mobile applications to promote active lifestyles, including the focus on nutrition and physical activity. Studies report using different techniques to captivate the teenagers, including questionnaires and gamification techniques. We identified the common features used in different studies, which are: paper diary, diet diary, exercise diary, notifications, diet plan, physical activity registration, gamification, smoking cessation, pictures, game, and SMS, among others.

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Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People

Cited by 26 publications

References 20 publications

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

An Efficient Data Imputation Technique for Human Activity Recognition

Promotion of Healthy Nutrition and Physical Activity Lifestyles for Teenagers: A Systematic Literature Review of The Current Methodologies

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