Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

A secure IoT-based modern healthcare system with fault-tolerant decision making process

The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system

Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. However, there is a lack of research work on applying game theory solutions to the HAR problems. This review paper explores the potential of game theory as a solution for HAR tasks, and bridges the gap between game theory and HAR research work by suggesting novel game-theoretic approaches for HAR problems. The contributions of this work include exploring how game theory can improve the accuracy and robustness of HAR models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing HAR methods. The objective is to provide insights into the potential of game theory as a solution for sensor-based HAR, and contribute to develop a more accurate and efficient recognition system in the future research directions

Intelligent ultrasound hand gesture recognition system

With the booming development of technology, hand gesture recognition has become a hotspot in Human-Computer Interaction (HCI) systems. Ultrasound hand gesture recognition is an innovative method that has attracted ample interest due to its strong real-time performance, low cost, large field of view, and illumination independence. Well-investigated HCI applications include external digital pens, game controllers on smart mobile devices, and web browser control on laptops. This thesis probes gesture recognition systems on multiple platforms to study the behavior of system performance with various gesture features. Focused on this topic, the contributions of this thesis can be summarized from the perspectives of smartphone acoustic field and hand model simulation, real-time gesture recognition on smart devices with speed categorization algorithm, fast reaction gesture recognition based on temporal neural networks, and angle of arrival-based gesture recognition system. Firstly, a novel pressure-acoustic simulation model is developed to examine its potential for use in acoustic gesture recognition. The simulation model is creating a new system for acoustic verification, which uses simulations mimicking real-world sound elements to replicate a sound pressure environment as authentically as possible. This system is fine-tuned through sensitivity tests within the simulation and validate with real-world measurements. Following this, the study constructs novel simulations for acoustic applications, informed by the verified acoustic field distribution, to assess their effectiveness in specific devices. Furthermore, a simulation focused on understanding the effects of the placement of sound devices and hand-reflected sound waves is properly designed. Moreover, a feasibility test on phase control modification is conducted, revealing the practical applications and boundaries of this model. Mobility and system accuracy are two significant factors that determine gesture recognition performance. As smartphones have high-quality acoustic devices for developing gesture recognition, to achieve a portable gesture recognition system with high accuracy, novel algorithms were developed to distinguish gestures using smartphone built-in speakers and microphones. The proposed system adopts Short-Time-Fourier-Transform (STFT) and machine learning to capture hand movement and determine gestures by the pretrained neural network. To differentiate gesture speeds, a specific neural network was designed and set as part of the classification algorithm. The final accuracy rate achieves 96% among nine gestures and three speed levels. The proposed algorithms were evaluated comparatively through algorithm comparison, and the accuracy outperformed state-of-the-art systems. Furthermore, a fast reaction gesture recognition based on temporal neural networks was designed. Traditional ultrasound gesture recognition adopts convolutional neural networks that have flaws in terms of response time and discontinuous operation. Besides, overlap intervals in network processing cause cross-frame failures that greatly reduce system performance. To mitigate these problems, a novel fast reaction gesture recognition system that slices signals in short time intervals was designed. The proposed system adopted a novel convolutional recurrent neural network (CRNN) that calculates gesture features in a short time and combines features over time. The results showed the reaction time significantly reduced from 1s to 0.2s, and accuracy improved to 100% for six gestures. Lastly, an acoustic sensor array was built to investigate the angle information of performed gestures. The direction of a gesture is a significant feature for gesture classification, which enables the same gesture in different directions to represent different actions. Previous studies mainly focused on types of gestures and analyzing approaches (e.g., Doppler Effect and channel impulse response, etc.), while the direction of gestures was not extensively studied. An acoustic gesture recognition system based on both speed information and gesture direction was developed. The system achieved 94.9% accuracy among ten different gestures from two directions. The proposed system was evaluated comparatively through numerical neural network structures, and the results confirmed that incorporating additional angle information improved the system's performance. In summary, the work presented in this thesis validates the feasibility of recognizing hand gestures using remote ultrasonic sensing across multiple platforms. The acoustic simulation explores the smartphone acoustic field distribution and response results in the context of hand gesture recognition applications. The smartphone gesture recognition system demonstrates the accuracy of recognition through ultrasound signals and conducts an analysis of classification speed. The fast reaction system proposes a more optimized solution to address the cross-frame issue using temporal neural networks, reducing the response latency to 0.2s. The speed and angle-based system provides an additional feature for gesture recognition. The established work will accelerate the development of intelligent hand gesture recognition, enrich the available gesture features, and contribute to further research in various gestures and application scenarios

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.Comment: Accepted for Publication in IEEE Access DOI: 10.1109/ACCESS.2020.303771

Ami-deu : un cadre sémantique pour des applications adaptables dans des environnements intelligents

Cette thèse vise à étendre l’utilisation de l'Internet des objets (IdO) en facilitant le développement d’applications par des personnes non experts en développement logiciel. La thèse propose une nouvelle approche pour augmenter la sémantique des applications d’IdO et l’implication des experts du domaine dans le développement d’applications sensibles au contexte. Notre approche permet de gérer le contexte changeant de l’environnement et de générer des applications qui s’exécutent dans plusieurs environnements intelligents pour fournir des actions requises dans divers contextes. Notre approche est mise en œuvre dans un cadriciel (AmI-DEU) qui inclut les composants pour le développement d’applications IdO. AmI-DEU intègre les services d’environnement, favorise l’interaction de l’utilisateur et fournit les moyens de représenter le domaine d’application, le profil de l’utilisateur et les intentions de l’utilisateur. Le cadriciel permet la définition d’applications IoT avec une intention d’activité autodécrite qui contient les connaissances requises pour réaliser l’activité. Ensuite, le cadriciel génère Intention as a Context (IaaC), qui comprend une intention d’activité autodécrite avec des connaissances colligées à évaluer pour une meilleure adaptation dans des environnements intelligents. La sémantique de l’AmI-DEU est basée sur celle du ContextAA (Context-Aware Agents) – une plateforme pour fournir une connaissance du contexte dans plusieurs environnements. Le cadriciel effectue une compilation des connaissances par des règles et l'appariement sémantique pour produire des applications IdO autonomes capables de s’exécuter en ContextAA. AmI- DEU inclut également un outil de développement visuel pour le développement et le déploiement rapide d'applications sur ContextAA. L'interface graphique d’AmI-DEU adopte la métaphore du flux avec des aides visuelles pour simplifier le développement d'applications en permettant des définitions de règles étape par étape. Dans le cadre de l’expérimentation, AmI-DEU comprend un banc d’essai pour le développement d’applications IdO. Les résultats expérimentaux montrent une optimisation sémantique potentielle des ressources pour les applications IoT dynamiques dans les maisons intelligentes et les villes intelligentes. Notre approche favorise l'adoption de la technologie pour améliorer le bienêtre et la qualité de vie des personnes. Cette thèse se termine par des orientations de recherche que le cadriciel AmI-DEU dévoile pour réaliser des environnements intelligents omniprésents fournissant des adaptations appropriées pour soutenir les intentions des personnes.Abstract: This thesis aims at expanding the use of the Internet of Things (IoT) by facilitating the development of applications by people who are not experts in software development. The thesis proposes a new approach to augment IoT applications’ semantics and domain expert involvement in context-aware application development. Our approach enables us to manage the changing environment context and generate applications that run in multiple smart environments to provide required actions in diverse settings. Our approach is implemented in a framework (AmI-DEU) that includes the components for IoT application development. AmI- DEU integrates environment services, promotes end-user interaction, and provides the means to represent the application domain, end-user profile, and end-user intentions. The framework enables the definition of IoT applications with a self-described activity intention that contains the required knowledge to achieve the activity. Then, the framework generates Intention as a Context (IaaC), which includes a self-described activity intention with compiled knowledge to be assessed for augmented adaptations in smart environments. AmI-DEU framework semantics adopts ContextAA (Context-Aware Agents) – a platform to provide context-awareness in multiple environments. The framework performs a knowledge compilation by rules and semantic matching to produce autonomic IoT applications to run in ContextAA. AmI-DEU also includes a visual tool for quick application development and deployment to ContextAA. The AmI-DEU GUI adopts the flow metaphor with visual aids to simplify developing applications by allowing step-by-step rule definitions. As part of the experimentation, AmI-DEU includes a testbed for IoT application development. Experimental results show a potential semantic optimization for dynamic IoT applications in smart homes and smart cities. Our approach promotes technology adoption to improve people’s well-being and quality of life. This thesis concludes with research directions that the AmI-DEU framework uncovers to achieve pervasive smart environments providing suitable adaptations to support people’s intentions

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201