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- Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device DataPublication . Ferreira, José M.; Pires, Ivan; Marques, Gonçalo; Garcia, Nuno M.; Zdravevski, Eftim; Lameski, Petre; Flórez-Revuelta, Francisco; Spinsante, SusannaUsing the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.
- Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy ProposalPublication . Villasana, María Vanessa; Pires, Ivan; Sá, Juliana; Garcia, Nuno M.; Zdravevski, Eftim; Chorbev, Ivan; Lameski, Petre; Flórez-Revuelta, FranciscoBackground: Mobile applications can be used for the monitoring of lifestyles and physical activity. It can be installed in commodity mobile devices, which are currently used by different types of people in their daily activities worlwide . Objective: This paper reviews and categorizes the mobile applications related to diet, nutrition, health, physical activity and education, showing the analysis of 73 mobile applications available on Google Play Store with the extraction of the different features. Methods: The mobile applications were analyzed in relation to each proposed category and their features, starting with the definition of the search keywords used in the Google Play Store. Each mobile application was installed on a smartphone, and validated whether it was researched in scientific studies. Finally, all mobile applications and features were categorized. Results: These mobile applications were clustered into four groups, including diet and nutrition, health, physical activity and education. The features of mobile applications were also categorized into six groups, including diet, anthropometric parameters, social, physical activity, medical parameters and vital parameters. The most available features of the mobile applications are weight, height, age, gender, goals, calories needed calculation, diet diary, food database with calories, calories burned and calorie intake. Conclusion: With this review, it was concluded that most mobile applications available in the market are related to diet, and they are important for different types of people. A promising idea for future work is to evaluate the acceptance by young people of such mobile applications.
- Activities of Daily Living and Environment Recognition Using Mobile DevicesPublication . Ferreira, José M.; Pires, Ivan; Marques, Gonçalo; Garcia, Nuno M.; Zdravevski, Eftim; Lameski, Petre; Flórez-Revuelta, Francisco; Spinsante, Susanna; Xu, LinaThe 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.