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- Version Reporting and Assessment Approaches for New and Updated Activity and Heart Rate MonitorsPublication . Collins, Tim; Woolley, Sandra; Oniani, Salome; Pires, Ivan; Garcia, Nuno M.; Ledger, Sean; Pandyan, AnandThis paper addresses the significant need for improvements in device version reporting and practice across the academic and technical activity monitoring literature, and it recommends assessments for new and updated consumer sensing devices. Reproducibility and data veracity are central to good scholarship, and particularly significant in clinical and health applications. Across the literature there is an absence of device version reporting and a failure to recognize that device validity is not maintained when firmware and software updates can, and do, change device performance and parameter estimation. In this paper, we propose the use of tractable methods to assess devices at their current version and provide an example empirical approach. Experimental results for heart rate and step count acquisitions during walking and everyday living activities from Garmin Vivosmart 3 (v4.10) wristband monitors are presented and analyzed, and the reliability issues of optically-acquired heart rates, especially during periods of activity, are demonstrated and discussed. In conclusion, the paper recommends the empirical assessment of new and updated activity monitors and improvements in device version reporting across the academic and technical literature.
- 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.
- Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selectionPublication . Pinho, André; Pombo, Nuno; Silva, Bruno M.C.; Bousson, K.; Garcia, Nuno M.A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.
- A importância dos indicadores biomédicos no funcionamento sexual em adultos portugueses saudáveisPublication . Teixeira, Paula; Pereira, Henrique; Monteiro, Samuel; Esgalhado, Graça; Afonso, Rosa Marina; Loureiro, Manuel; Ferrão, Delfina; Garcia, Nuno M.SexualidadeResumoIntroduc¸ão: Na presente investigac¸ão pretendeu-se analisar a influência de indicadores biomé-dicos, tais como o índice de massa corporal, glicémia, colesterol total, triglicerídeos, pressãoarterial sistólica e pressão arterial diastólica, no funcionamento sexual em adultos portugueses.Métodos: A amostra foi constituída por 225 indivíduos saudáveis (não fumadores, mulheres nãotomando pílula contracetiva), entre os 18 e os 89 anos (média = 41), 107 do sexo masculinoe 117 do feminino. Aplicaram-se um questionário sociodemográfico, a versão portuguesa doMassachusetts General Hospital Sexual Functioning Questionnaire (MGH-SFQ), e dispositivos demedida de indicadores biomédicos.Resultados: Dos participantes, 59,6% (n = 134) apresentaram níveis para o funcionamento sexualglobal abaixo dos valores esperados, os homens pontuaram melhor funcionamento sexual glo-bal quando comparados com as mulheres, assim como o grupo dos participantes mais novos.Destacam-se os níveis de correlac¸ão significativa entre o IMC (r = −0,253; p < 0,001), a gli-cémia (r = −0,230; p < 0,001), o colesterol total (r = −0,144; p < 0,05) e o funcionamentosexual. O modelo de regressão hierárquica permitiu demonstrar o efeito mediador das variá-veis biomédicas sobre o funcionamento sexual, explicou 31% (r2= 0,31; p < 0,001) da variânciatotal.
- Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A ReviewPublication . Marques, Gonçalo; Pitarma, R.; Garcia, Nuno M.; Pombo, NunoInternet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that o er an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.
- Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile DevicesPublication . Pires, Ivan; Garcia, Nuno M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, SusannaSensors 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 into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.
- Energy Harvesting Methods for Medical DevicesPublication . Gaspar, Pedro Dinis; Felizardo, Virginie; Garcia, Nuno M.The evolution of medical equipments and health care involve the miniaturization of devices related to that area. The biggest challenge to this goal is the reduction of battery and/or a search for alternative energy sources. In this chapter, we discuss some sources for energy harvesting, its main methods of conversion, and several applications developed for medical equipments. In order to provide some future prospects, the current state of technology and future trends are addressed, accessing some possible approaches in the development of medical equipments.
- 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.
- Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devicesPublication . Pires, Ivan; Garcia, Nuno M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, Maria Cristina CanavarroSeveral 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). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition.
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