487 research outputs found
Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting
A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)
Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review
An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303—AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
A Survey on Multimedia Content Protection Mechanisms
Cloud computing has emerged to influence multimedia content providers like Disney to render their multimedia services. When content providers use the public cloud, there are chances to have pirated copies further leading to a loss in revenues. At the same time, technological advancements regarding content recording and hosting made it easy to duplicate genuine multimedia objects. This problem has increased with increased usage of a cloud platform for rendering multimedia content to users across the globe. Therefore it is essential to have mechanisms to detect video copy, discover copyright infringement of multimedia content and protect the interests of genuine content providers. It is a challenging and computationally expensive problem to be addressed considering the exponential growth of multimedia content over the internet. In this paper, we surveyed multimedia-content protection mechanisms which throw light on different kinds of multimedia, multimedia content modification methods, and techniques to protect intellectual property from abuse and copyright infringement. It also focuses on challenges involved in protecting multimedia content and the research gaps in the area of cloud-based multimedia content protection
Identification, synchronisation and composition of user-generated videos
Cotutela Universitat Politècnica de Catalunya i Queen Mary University of LondonThe increasing availability of smartphones is facilitating people to capture videos of their experience when attending events such as concerts, sports competitions and public rallies. Smartphones are equipped with inertial sensors which could be beneficial for event understanding. The captured User-Generated Videos (UGVs) are made available on media sharing websites. Searching and mining of UGVs of the same event are challenging due to inconsistent tags or incorrect timestamps. A UGV recorded from a fixed location contains monotonic content and unintentional camera motions, which may make it less interesting to playback. In this thesis, we propose the following identification, synchronisation and video composition frameworks for UGVs.
We propose a framework for the automatic identification and synchronisation of unedited multi-camera UGVs within a database. The proposed framework analyses the sound to match and cluster UGVs that capture the same spatio-temporal event, and estimate their relative time-shift to temporally align them. We design a novel descriptor derived from the pairwise matching of audio chroma features of UGVs. The descriptor facilitates the definition of a classification threshold for automatic query-by-example event identification. We contribute a database of 263 multi-camera UGVs of 48 real-world events. We evaluate the proposed framework on this database and compare it with state-of-the-art methods. Experimental results show the effectiveness of the proposed approach in the presence of audio degradations (channel noise, ambient noise, reverberations).
Moreover, we present an automatic audio and visual-based camera selection framework for composing uninterrupted recording from synchronised multi-camera UGVs of the same event. We design an automatic audio-based cut-point selection method that provides a common reference for audio and video segmentation. To filter low quality video segments, spatial and spatio-temporal assessments are computed. The framework combines segments of UGVs using a rank-based camera selection strategy by considering visual quality scores and view diversity. The proposed framework is validated on a dataset of 13 events (93~UGVs) through subjective tests and compared with state-of-the-art methods. Suitable cut-point selection, specific visual quality assessments and rank-based camera selection contribute to the superiority of the proposed framework over the existing methods. Finally, we contribute a method for Camera Motion Detection using Gyroscope for UGVs captured from smartphones and design a gyro-based quality score for video composition. The gyroscope measures the angular velocity of the smartphone that can be use for camera motion analysis. We evaluate the proposed camera motion detection method on a dataset of 24 multi-modal UGVs captured by us, and compare it with existing visual and inertial sensor-based methods. By designing a gyro-based score to quantify the goodness of the multi-camera UGVs, we develop a gyro-based video composition framework. A gyro-based score substitutes the spatial and spatio-temporal scores and reduces the computational complexity. We contribute a multi-modal dataset of 3 events (12~UGVs), which is used to validate the proposed gyro-based video composition framework.El incremento de la disponibilidad de teléfonos inteligentes o smartphones posibilita a la gente capturar videos de sus experiencias cuando asisten a eventos asà como como conciertos, competiciones deportivas o mÃtines públicos. Los Videos Generados por Usuarios (UGVs) pueden estar disponibles en sitios web públicos especializados en compartir archivos. La búsqueda y la minerÃa de datos de los UGVs del mismo evento son un reto debido a que los etiquetajes son incoherentes o las marcas de tiempo erróneas. Por otra parte, un UGV grabado desde una ubicación fija, contiene información monótona y movimientos de cámara no intencionados haciendo menos interesante su reproducción. En esta tesis, se propone una identificación, sincronización y composición de tramas de vÃdeo para UGVs. Se ha propuesto un sistema para la identificación y sincronización automática de UGVs no editados provenientes de diferentes cámaras dentro de una base de datos. El sistema propuesto analiza el sonido con el fin de hacerlo coincidir e integrar UGVs que capturan el mismo evento en el espacio y en el tiempo, estimando sus respectivos desfases temporales y alinearlos en el tiempo. Se ha diseñado un nuevo descriptor a partir de la coincidencia por parejas de caracterÃsticas de la croma del audio de los UGVs. Este descriptor facilita la determinación de una clasificación por umbral para una identificación de eventos automática basada en búsqueda mediante ejemplo (en inglés, query by example). Se ha contribuido con una base de datos de 263 multi-cámaras UGVs de un total de 48 eventos reales. Se ha evaluado la trama propuesta en esta base de datos y se ha comparado con los métodos elaborados en el estado del arte. Los resultados experimentales muestran la efectividad del enfoque propuesto con la presencia alteraciones en el audio. Además, se ha presentado una selección automática de tramas en base a la reproducción de video y audio componiendo una grabación ininterrumpida de multi-cámaras UGVs sincronizadas en el mismo evento. También se ha diseñado un método de selección de puntos de corte automático basado en audio que proporciona una referencia común para la segmentación de audio y video. Con el fin de filtrar segmentos de videos de baja calidad, se han calculado algunas medidas espaciales y espacio-temporales. El sistema combina segmentos de UGVs empleando una estrategia de selección de cámaras basadas en la evaluación a través de un ranking considerando puntuaciones de calidad visuales y diversidad de visión. El sistema propuesto se ha validado con un conjunto de datos de 13 eventos (93 UGVs) a través de pruebas subjetivas y se han comparado con los métodos elaborados en el estado del arte. La selección de puntos de corte adecuados, evaluaciones de calidad visual especÃficas y la selección de cámara basada en ranking contribuyen en la mejorÃa de calidad del sistema propuesto respecto a otros métodos existentes. Finalmente, se ha realizado un método para la Detección de Movimiento de Cámara usando giróscopos para las UGVs capturadas desde smartphones y se ha diseñado un método de puntuación de calidad basada en el giro. El método de detección de movimiento de la cámara con una base de datos de 24 UGVs multi-modales y se ha comparado con los métodos actuales basados en visión y sistemas inerciales. A través del diseño de puntuación para cuantificar con el giróscopo cuán bien funcionan los sistemas de UGVs con multi-cámara, se ha desarrollado un sistema de composición de video basada en el movimiento del giroscopio. Este sistema basado en la puntuación a través del giróscopo sustituye a los sistemas de puntuaciones basados en parámetros espacio-temporales reduciendo la complejidad computacional. Además, se ha contribuido con un conjunto de datos de 3 eventos (12 UGVs), que se han empleado para validar los sistemas de composición de video basados en giróscopo.Postprint (published version
Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living
Following the recent advances in technology and the growing use of mobile devices such as
smartphones, several solutions may be developed to improve the quality of life of users in the
context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g.,
accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS)
receiver, which allow the acquisition of physical and physiological parameters for the
recognition of different Activities of Daily Living (ADL) and the environments in which they are
performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare
tasks, based on the types of skills that people usually learn in early childhood, including
feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping,
watching TV, working, listening to music, cooking, eating and others. On the context of AAL,
some individuals (henceforth called user or users) need particular assistance, either because
the user has some sort of impairment, or because the user is old, or simply because users
need/want to monitor their lifestyle. The research and development of systems that provide a
particular assistance to people is increasing in many areas of application. In particular, in the
future, the recognition of ADL will be an important element for the development of a personal
digital life coach, providing assistance to different types of users. To support the recognition
of ADL, the surrounding environments should be also recognized to increase the reliability of
these systems.
The main focus of this Thesis is the research on methods for the fusion and classification of the
data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL
in almost real-time, taking into account the large diversity of the capabilities and
characteristics of the mobile devices available in the market. In order to achieve this objective,
this Thesis started with the review of the existing methods and technologies to define the
architecture and modules of the method for the identification of ADL. With this review and
based on the knowledge acquired about the sensors available in off-the-shelf mobile devices,
a set of tasks that may be reliably identified was defined as a basis for the remaining research
and development to be carried out in this Thesis. This review also identified the main stages
for the development of a new method for the identification of the ADL using the sensors
available in off-the-shelf mobile devices; these stages are data acquisition, data processing,
data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One
of the challenges is related to the different types of data acquired from the different sensors,
but other challenges were found, including the presence of environmental noise, the positioning
of the mobile device during the daily activities, the limited capabilities of the mobile devices
and others. Based on the acquired data, the processing was performed, implementing data
cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of
the research their implementation does not have influence in the results of the identification
of the ADL and environments, as the features are extracted from a set of data acquired during
a defined time interval and there are no missing values during this stage. The joint selection of
the set of usable sensors and the identifiable set of tasks will then allow the development of a
framework that, considering multi-sensor data fusion technologies and context awareness, in
coordination with other information available from the user context, such as his/her agenda
and the time of the day, will allow to establish a profile of the tasks that the user performs in
a regular activity day. The classification method and the algorithm for the fusion of the features
for the recognition of ADL and its environments needs to be deployed in a machine with some
computational power, while the mobile device that will use the created framework, can
perform the identification of the ADL using a much less computational power. Based on the
results reported in the literature, the method chosen for the recognition of the ADL is composed
by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron
(MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural
Networks (DNN).
Data acquisition can be performed with standard methods. After the acquisition, the data must
be processed at the data processing stage, which includes data cleaning and feature extraction
methods. The data cleaning method used for motion and magnetic sensors is the low pass filter,
in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform
(FFT) was applied to extract the different frequencies. When the data is clean, several features
are then extracted based on the types of sensors used, including the mean, standard deviation,
variance, maximum value, minimum value and median of raw data acquired from the motion
and magnetic sensors; the mean, standard deviation, variance and median of the maximum
peaks calculated with the raw data acquired from the motion and magnetic sensors; the five
greatest distances between the maximum peaks calculated with the raw data acquired from
the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel-
Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw
data acquired from the microphone data; and the distance travelled calculated with the data
acquired from the GPS receiver. After the extraction of the features, these will be grouped in
different datasets for the application of the ANN methods and to discover the method and
dataset that reports better results. The classification stage was incrementally developed,
starting with the identification of the most common ADL (i.e., walking, running, going upstairs,
going downstairs and standing activities) with motion and magnetic sensors. Next, the
environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen,
living room, hall, street and library. After the environments are recognized, and based on the
different sets of sensors commonly available in the mobile devices, the data acquired from the
motion and magnetic sensors were combined with the recognized environment in order to
differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled,
extracted from the GPS receiver data, allowing also to recognize the driving activity.
After the implementation of the three classification methods with different numbers of
iterations, datasets and remaining configurations in a machine with high processing
capabilities, the reported results proved that the best method for the recognition of the most
common ADL and activities without motion is the DNN method, but the best method for the
recognition of environments is the FNN method with Backpropagation. Depending on the
number of sensors used, this implementation reports a mean accuracy between 85.89% and
89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of
environments, and equals to 100% for the recognition of activities without motion, reporting
an overall accuracy between 85.89% and 92.00%.
The last stage of this research work was the implementation of the structured framework for
the mobile devices, verifying that the FNN method requires a high processing power for the
recognition of environments and the results reported with the mobile application are lower
than the results reported with the machine with high processing capabilities used. Thus, the
DNN method was also implemented for the recognition of the environments with the mobile
devices. Finally, the results reported with the mobile devices show an accuracy between 86.39%
and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition
of environments, and equal to 100% for the recognition of activities without motion, reporting
an overall accuracy between 58.02% and 89.15%.
Compared with the literature, the results returned by the implemented framework show only
a residual improvement. However, the results reported in this research work comprehend the
identification of more ADL than the ones described in other studies. The improvement in the
recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum
number of ADL and environments previously recognized was 13, while the number of ADL and
environments recognized with the framework resulting from this research is 16. In conclusion,
the framework developed has a mean improvement of 2.93% in the accuracy of the recognition
for a larger number of ADL and environments than previously reported.
In the future, the achievements reported by this PhD research may be considered as a start
point of the development of a personal digital life coach, but the number of ADL and
environments recognized by the framework should be increased and the experiments should be
performed with different types of devices (i.e., smartphones and smartwatches), and the data
imputation and other machine learning methods should be explored in order to attempt to
increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por
exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade
de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted
Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro,
giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS),
que permitem a aquisição de vários parâmetros fÃsicos e fisiológicos para o reconhecimento de
diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um
conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos
de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem
alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir
escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No
contexto de AVA, alguns indivÃduos (comumente chamados de utilizadores) precisam de
assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é
idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de
vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência
particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o
reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente
pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de
pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se
desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas.
O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos
dados adquiridos a partir dos sensores disponÃveis nos dispositivos móveis, para o
reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade
das caracterÃsticas dos dispositivos móveis disponÃveis no mercado. Para atingir este objetivo,
esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a
arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da
literatura e com base no conhecimento adquirido sobre os sensores disponÃveis nos dispositivos
móveis disponÃveis no mercado, um conjunto de tarefas que podem ser identificadas foi
definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os
principais conceitos para o desenvolvimento do novo método de identificação das AVD,
utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de
dados, imputação de dados, extração de caracterÃsticas, fusão de dados e extração de
resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado
aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram
encontrados, sendo os mais relevantes o ruÃdo ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis.
As diferentes caracterÃsticas das pessoas podem igualmente influenciar a criação dos métodos,
escolhendo pessoas com diferentes estilos de vida e caracterÃsticas fÃsicas para a aquisição e
identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos,
realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a
extração de caracterÃsticas, para iniciar a criação do novo método para o reconhecimento das
AVD. Os métodos de imputação de dados foram excluÃdos da implementação, pois não iriam
influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são
utilizadas as caracterÃsticas extraÃdas de um conjunto de dados adquiridos durante um intervalo
de tempo definido.
A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o
desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados
adquiridos com múltiplos sensores em coordenação com outras informações relativas ao
contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de
tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o
método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes
Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural
Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a
criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a
implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para
testes adicionais.
Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando
dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos
comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de
processamento de dados, que inclui os métodos de correção de dados e de extração de
caracterÃsticas. O método de correção de dados utilizado para sensores de movimento e
magnéticos é o filtro passa-baixo de modo a reduzir o ruÃdo, mas para os dados acústicos, a
Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências.
Após a correção dos dados, as diferentes caracterÃsticas foram extraÃdas com base nos tipos de
sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mÃnimo e
mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio
padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos
sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos
calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio
padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas
com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os
dados adquiridos pelo recetor de GPS. Após a extração das caracterÃsticas, as mesmas são agrupadas em diferentes conjuntos de dados
para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de
caracterÃsticas que reporta melhores resultados. O módulo de classificação de dados foi
incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores
magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em
seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala
de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes
reconhecidos e os restantes sensores disponÃveis nos dispositivos móveis, os dados adquiridos
dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para
diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número
de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraÃda
a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir.
Após a implementação dos três métodos de classificação com diferentes números de iterações,
conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os
resultados relatados provaram que o melhor método para o reconhecimento das atividades
comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o
reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número
de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51%
para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes,
e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão
global entre 85,89% e 92,00%.
A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando
que o método FNN requer um alto poder de processamento para o reconhecimento de
ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados
reportados com a máquina com alta capacidade de processamento utilizada no
desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o
reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados
com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o
reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual
a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral
entre 58,02% e 89,15%.
Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram
uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais
estudos disponÃveis na literatura. A melhoria no reconhecimento das AVD com base na média
das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos
estudos disponÃveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos
com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93%
na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto
de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e
ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser
repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os
métodos de imputação e outros métodos de classificação de dados devem ser explorados de
modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e
ambientes
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