Body Location Independent Activity Monitoring

Figueira, Carina; Matias, Ricardo; Gambôa, Hugo

doi:10.5220/0005699601900197

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…Aplicações em e-Health que, como o presente trabalho também fazem uso de acelerômetros, estão igualmente se tornando comuns. Dentre elas, há abordagens de rastreadores de atividades físicas em exercícios (Anjum and Ilyas, 2013;Kwapisz et al, 2011;Aguiar et al, 2014;Figueira et al, 2016), que buscam identificar o tipo de atividade física que um usuário está praticando.…”

Section: Trabalhos Correlatosunclassified

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Utilizando Acelerômetros de Smartphones para Prevenção de Acidentes de Trânsito

Silva

Zaghetto

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This paper presents a method based on Artificial Neural Networks that uses signals captured from conventional cell phone accelerometers to identify user's displacement patterns. The patterns are classified according to the location of the device (pocket, hand or console) and the way the user moves (using a car or walking). Such method enables the identification of risky situations that can lead to accidents, such as driving or walking using a smartphone. Tests show that the proposed method is able to identify one of 10 possible transport modes with an average hit rate of 95%. Resumo: Este artigo apresenta um método baseado em Redes Neurais Artificiais que, utilizando sinais capturados a partir de acelerômetros de telefones celulares convencionais,é capaz de identificar padrões de deslocamento do usuário definidos pela localização do dispositivo (no bolso, na mão ou no console) e pela forma como o usuário se desloca (utilizando um carro ou a pé). Tal método possibilita a identificação de situações de risco que podem culminar em acidentes, como dirigir ou andar utilizando um smartphone. Os testes mostram que o método propostoé capaz de identificar uma dentre 10 possíveis classes de transporte com uma taxa de acerto médio de 95%.

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Section: Trabalhos Correlatosunclassified

“…Também há aplicações focadas na evolução de pacientes idosos em fisioterapia. Em Figueira et al (2016), foram utilizados um acelerômetro, com taxa de aquisição de 30 Hz, e um barômetro, com taxa de aquisição de 5 Hz. Os dados são separados em janelas de 5 segundos e, então, são extraídas 29 características de cada janela.…”

Section: Trabalhos Correlatosunclassified

Utilizando Acelerômetros de Smartphones para Prevenção de Acidentes de Trânsito

Silva

Zaghetto

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…A natural step of authentication for widely used handheld and wearable devices is the utilization of accelerometer fingerprinting [126,127]. For instance, each smartphone holder could be verified based on the gait pattern by continuously monitoring the accelerometer data that is almost impossible to fake by another individual [128].…”

Section: Behavior Detectionmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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“…In the work of Fen Miao et al [15], the time-domain features, including mean, standard deviation, median, skewness, kurtosis, and Inter-quartile-range are extracted from the resultant acceleration data to study the six daily activities using Decision Tree, Naive Bayes and an improved SVM. In the work of Carina Figueira et al [16], the activities of standing, sitting, walking, running, ascending stairs and descending stairs are studied. They have extracted mixed-domain features, including root mean square, median absolute deviation, standard deviation, spectral roll on, mean power spectrum, max power spectrum from the data.…”

Section: Related Workmentioning

confidence: 99%

“…The same mixed-domain feature set as reported in the work of Carina Figueira et al [16] are extracted, which includes Root Mean Square(RMS), Median Absolute Deviation(MAD), Standard Deviation, Spectral Roll On, Mean Power Spectrum, Max power Spectrum.…”

Section: (B) Mixed-domain Feature Extractionmentioning

confidence: 99%

Using Jaccard Distance Measure for Unsupervised Activity Recognition with Smartphone Accelerometers

Wang

et al. 2017

Web and Big Data

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Abstract. The rapid popularity of smartphones has led to a growing research interest in human activity recognition (HAR) with the mobile devices. Accelerometer is the most commonly used sensor of smartphone for HAR. Most supervised HAR methods have been developed. However, it is very difficult to collect the annotated or labeled training data for HAR. So, developing of effective unsupervised methods for HAR is very necessary. The accuracy of the unsupervised activity recognition can be greatly affected by feature extraction methods and distance measures.Although Euclidean distance measure is commonly used in activity recognition, it is not suitable for measuring distance when the number of features is very large, which is usually the case in HAR.Jaccard distance is a distance measure based on mutual information theory and can better represent the differences between nonnegative feature vectors than Euclidean distance. In this work, the Jaccard distance measure is applied to HAR for the first time. In the experiments, the results of the Jaccard distance measure and the Eucildean distance measure are compared, using three different feature extraction methods. Two different evaluation methods are used to comprehensively analyze the final results: (a) C-Index before clustering, (b) FM-index after using five different clustering methods which are Spectral Cluster, Single-Linkage, Ward-Linkage, Average-Linkage, and K-Medoids. Experiments show that, almost for every combination of the feature extraction methods and the evaluation methods, the Jaccard distance is consistently better than the Euclidean distance for unsupervised HAR.

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Body Location Independent Activity Monitoring

Cited by 18 publications

References 9 publications

Utilizando Acelerômetros de Smartphones para Prevenção de Acidentes de Trânsito

Utilizando Acelerômetros de Smartphones para Prevenção de Acidentes de Trânsito

Multi-Factor Authentication: A Survey

Using Jaccard Distance Measure for Unsupervised Activity Recognition with Smartphone Accelerometers

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