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- Non-invasive measurement of results of timed-up and go test: preliminary results.Publication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Pombo, NunoWith the evolution of the time and together with the evolution of ubiquitous systems with high processing capacity, various forms of use and that allow the realisation of several studies and the joining of areas of knowledge quite heterogeneous as computer science and physiotherapy. The use of martphones, in conjunction with inertial sensors, represents not only an excellent opportunity for the development of support and research applications but also a way to create cheaper solutions close to people. In this study, we also propose that as an experimental form the use of this type of sensors to capture movements using the timed up and go test and with the objectives and to create tools that allow the detection of diseases related to the action in elderly individuals. This paper presents the results of the data capture with different perspectives and using various features such as the time of the test the time of getting up from the chair, sitting in the chair, reaction to sound signalling during the trial, time reversal and the time it takes for the individual to sit down.
- Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devicesPublication . Pires, Ivan M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, M.C.C.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). 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.
- Android library for recognition of activities of daily living: implementation considerations, challenges, and solutionsPublication . Pires, Ivan M.; Teixeira, M.C.C.; Pombo, Nuno; Garcia, Nuno M.; Flórez-Revuelta, Francisco; Spinsante, Susanna; Goleva, Rossitza; Zdravevski, EftimOff-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.
- Smartphone-based automatic measurement of the results of the timed-up and go testPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Pombo, Nuno; Spinsante, Susanna; Crisóstomo, RuteThe Timed-Up and Go test is a very used test in the physiotherapy area. For the measurement of the results of the test, we propose to use a smartphone with several embedded sensors, including accelerometer, magnetometer, gyroscope, a Bitalino device with the Electromyography (EMG) and Electrocardiography (ECG) sensors, and a second Bitalino device with a pressure sensor connected and positioned in the back of the chair. This architecture allows to capture several types of data from the sensors easily. In this paper, we present a structured method to implement the measurement of the different parameters involved in the Timed-up and Go test, for acquiring, processing and cleaning the collected measurements. This data will help in the classification of the test results initially, and later on to discover more complex patterns and related conditions, such as equilibrium changes, neurological pathologies, degenerative pathologies, lesions of lower limbs and chronic venous diseases.
- Functional App TestsPublication . Andrade, M.V.; Pires, Ivan M.; Crisóstomo, Rute; Garcia, Nuno M.Objetivos: Pretende-se desenvolver uma aplicação de internet e mobile do teste funcional Heel-rise Test, já existente na literatura, mas cuja realização é manual. Materiais e métodos: O acelerómetro, instrumento capaz de capturar dados de aceleração instantânea de um sujeito ou objeto, permite obter dados que, depois de processados, identificam atividades da vida diária utilizando por exemplo, o reconhecimento de padrões. Os dados capturados precisam ser pré – processados (por exemplo: exclusão de ruídos), analisados e classificados. Análise Estatística: Através de um algoritmo implementado numa aplicação móvel, os dados recolhidos, após processados, permitem verificar a validade do exercício executado recorrendo à identificação de padrões de acelerometria para a validação dos exercícios realizados no decorrer de testes funcionais. Resultados: Foi desenvolvido um protótipo de uma aplicação móvel para o teste funcional Heel-rise Test em que, está implementado o algoritmo, baseado na literatura e atividades experimentais prévias que, ainda assim, necessita de uma exaustiva validação com diferentes populações (idades, estilo de vida, condições específicas) para ser válida. Conclusões: O protótipo de aplicação do Heel-rise Test mostrou estabilidade e o algoritmo resultou. No futuro, esta aplicação requer avaliação da validade e fiabilidade de forma a poder ser usadas por fisioterapeutas, outros profissionais e público em geral de forma a avaliar a funcionalidade dos indivíduos associada à componente física de força muscular do músculo tricípite sural.
- Recognition of activities of daily living and environments using acoustic sensors embedded on mobile devicesPublication . Pires, Ivan M.; Marques, Gonçalo Santos; Garcia, Nuno M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, M.C.C.; Zdravevski, EftimThe identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.