A hierarchal framework for recognising activities of daily life

PhDIn today’s working world the elderly who are dependent can sometimes be neglected by society. Statistically, after toddlers it is the elderly who are observed to have higher accident rates while performing everyday activities. Alzheimer’s disease is one of the major impairments that elderly people suffer from, and leads to the elderly person not being able to live an independent life due to forgetfulness. One way to support elderly people who aspire to live an independent life and remain safe in their home is to find out what activities the elderly person is carrying out at a given time and provide appropriate assistance or institute safeguards. The aim of this research is to create improved methods to identify tasks related to activities of daily life and determine a person’s current intentions and so reason about that person’s future intentions. A novel hierarchal framework has been developed, which recognises sensor events and maps them to significant activities and intentions. As privacy is becoming a growing concern, the monitoring of an individual’s behaviour can be seen as intrusive. Hence, the monitoring is based around using simple non intrusive sensors and tags on everyday objects that are used to perform daily activities around the home. Specifically there is no use of any cameras or visual surveillance equipment, though the techniques developed are still relevant in such a situation. Models for task recognition and plan recognition have been developed and tested on scenarios where the plans can be interwoven. Potential targets are people in the first stages of Alzheimer’s disease and in the structuring of the library of kernel plan sequences, typical routines used to sustain meaningful activity have been used. Evaluations have been carried out using volunteers conducting activities of daily life in an experimental home environment. The results generated from the sensors have been interpreted and analysis of developed algorithms has been made. The outcomes and findings of these experiments demonstrate that the developed hierarchal framework is capable of carrying activity recognition as well as being able to carry out intention analysis, e.g. predicting what activity they are most likely to carry out next

Gesture recognition using principal component analysis, multi-scale theory, and hidden Markov models

In this thesis, a dynamic gesture recognition system is presented which requires no special hardware other than a Web cam . The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical m ulti-scale theory and Discrete Hidden Markov Models (DHMMs). We use a hierarchical decision tree based on multi-scale theory. Firstly we convolve all members of the training data with a Gaussian kernel, w h ich blu rs d iffe ren c e s b e tw e en images and reduces their separation in feature space. Th is reduces the number of eigen vectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space in to several clusters using the £-means algorithm. Then the level of b lurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided in to clusters and the process is repeated. We thus produce a tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures with very little computational cost. The output of the decision tree is then input in to the DHMM recogniser to recognise temporal information

Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based AttentionModule (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAMResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research

Read My Lips: Continuous Signer Independent Weakly Supervised Viseme Recognition

Abstract. This work presents a framework to recognise signer indepen-dent mouthings in continuous sign language, with no manual annotations needed. Mouthings represent lip-movements that correspond to pronun-ciations of words or parts of them during signing. Research on sign lan-guage recognition has focused extensively on the hands as features. But sign language is multi-modal and a full understanding particularly with respect to its lexical variety, language idioms and grammatical structures is not possible without further exploring the remaining information chan-nels. To our knowledge no previous work has explored dedicated viseme recognition in the context of sign language recognition. The approach is trained on over 180.000 unlabelled frames and reaches 47.1 % precision on the frame level. Generalisation across individuals and the influence of context-dependent visemes are analysed

The selection and evaluation of a sensory technology for interaction in a warehouse environment

In recent years, Human-Computer Interaction (HCI) has become a significant part of modern life as it has improved human performance in the completion of daily tasks in using computerised systems. The increase in the variety of bio-sensing and wearable technologies on the market has propelled designers towards designing more efficient, effective and fully natural User-Interfaces (UI), such as the Brain-Computer Interface (BCI) and the Muscle-Computer Interface (MCI). BCI and MCI have been used for various purposes, such as controlling wheelchairs, piloting drones, providing alphanumeric inputs into a system and improving sports performance. Various challenges are experienced by workers in a warehouse environment. Because they often have to carry objects (referred to as hands-full) it is difficult to interact with traditional devices. Noise undeniably exists in some industrial environments and it is known as a major factor that causes communication problems. This has reduced the popularity of using verbal interfaces with computer applications, such as Warehouse Management Systems. Another factor that effects the performance of workers are action slips caused by a lack of concentration during, for example, routine picking activities. This can have a negative impact on job performance and allow a worker to incorrectly execute a task in a warehouse environment. This research project investigated the current challenges workers experience in a warehouse environment and the technologies utilised in this environment. The latest automation and identification systems and technologies are identified and discussed, specifically the technologies which have addressed known problems. Sensory technologies were identified that enable interaction between a human and a computerised warehouse environment. Biological and natural behaviours of humans which are applicable in the interaction with a computerised environment were described and discussed. The interactive behaviours included the visionary, auditory, speech production and physiological movement where other natural human behaviours such paying attention, action slips and the action of counting items were investigated. A number of modern sensory technologies, devices and techniques for HCI were identified with the aim of selecting and evaluating an appropriate sensory technology for MCI. iii MCI technologies enable a computer system to recognise hand and other gestures of a user, creating means of direct interaction between a user and a computer as they are able to detect specific features extracted from a specific biological or physiological activity. Thereafter, Machine Learning (ML) is applied in order to train a computer system to detect these features and convert them to a computer interface. An application of biomedical signals (bio-signals) in HCI using a MYO Armband for MCI is presented. An MCI prototype (MCIp) was developed and implemented to allow a user to provide input to an HCI, in a hands-free and hands-full situation. The MCIp was designed and developed to recognise the hand-finger gestures of a person when both hands are free or when holding an object, such a cardboard box. The MCIp applies an Artificial Neural Network (ANN) to classify features extracted from the surface Electromyography signals acquired by the MYO Armband around the forearm muscle. The MCIp provided the results of data classification for gesture recognition to an accuracy level of 34.87% with a hands-free situation. This was done by employing the ANN. The MCIp, furthermore, enabled users to provide numeric inputs to the MCIp system hands-full with an accuracy of 59.7% after a training session for each gesture of only 10 seconds. The results were obtained using eight participants. Similar experimentation with the MYO Armband has not been found to be reported in any literature at submission of this document. Based on this novel experimentation, the main contribution of this research study is a suggestion that the application of a MYO Armband, as a commercially available muscle-sensing device on the market, has the potential as an MCI to recognise the finger gestures hands-free and hands-full. An accurate MCI can increase the efficiency and effectiveness of an HCI tool when it is applied to different applications in a warehouse where noise and hands-full activities pose a challenge. Future work to improve its accuracy is proposed

SiAM-dp : an open development platform for massively multimodal dialogue systems in cyber-physical environments

Cyber-physical environments enhance natural environments of daily life such as homes, factories, offices, and cars by connecting the cybernetic world of computers and communication with the real physical world. While under the keyword of Industrie 4.0, cyber-physical environments will take a relevant role in the next industrial revolution, and they will also appear in homes, offices, workshops, and numerous other areas. In this new world, classical interaction concepts where users exclusively interact with a single stationary device, PC or smartphone become less dominant and make room for new occurrences of interaction between humans and the environment itself. Furthermore, new technologies and a rising spectrum of applicable modalities broaden the possibilities for interaction designers to include more natural and intuitive non-verbal and verbal communication. The dynamic characteristic of a cyber-physical environment and the mobility of users confronts developers with the challenge of developing systems that are flexible concerning the connected and used devices and modalities. This implies new opportunities for cross-modal interaction that go beyond dual modalities interaction as is well known nowadays. This thesis addresses the support of application developers with a platform for the declarative and model based development of multimodal dialogue applications, with a focus on distributed input and output devices in cyber-physical environments. The main contributions can be divided into three parts: - Design of models and strategies for the specification of dialogue applications in a declarative development approach. This includes models for the definition of project resources, dialogue behaviour, speech recognition grammars, and graphical user interfaces and mapping rules, which convert the device specific representation of input and output description to a common representation language. - The implementation of a runtime platform that provides a flexible and extendable architecture for the easy integration of new devices and components. The platform realises concepts and strategies of multimodal human-computer interaction and is the basis for full-fledged multimodal dialogue applications for arbitrary device setups, domains, and scenarios. - A software development toolkit that is integrated in the Eclipse rich client platform and provides wizards and editors for creating and editing new multimodal dialogue applications.Cyber-physische Umgebungen (CPEs) erweitern natürliche Alltagsumgebungen wie Heim, Fabrik, Büro und Auto durch Verbindung der kybernetischen Welt der Computer und Kommunikation mit der realen, physischen Welt. Die möglichen Anwendungsgebiete hierbei sind weitreichend. Während unter dem Stichwort Industrie 4.0 cyber-physische Umgebungen eine bedeutende Rolle für die nächste industrielle Revolution spielen werden, erhalten sie ebenfalls Einzug in Heim, Büro, Werkstatt und zahlreiche weitere Bereiche. In solch einer neuen Welt geraten klassische Interaktionskonzepte, in denen Benutzer ausschließlich mit einem einzigen Gerät, PC oder Smartphone interagieren, immer weiter in den Hintergrund und machen Platz für eine neue Ausprägung der Interaktion zwischen dem Menschen und der Umgebung selbst. Darüber hinaus sorgen neue Technologien und ein wachsendes Spektrum an einsetzbaren Modalitäten dafür, dass sich im Interaktionsdesign neue Möglichkeiten für eine natürlichere und intuitivere verbale und nonverbale Kommunikation auftun. Die dynamische Natur von cyber-physischen Umgebungen und die Mobilität der Benutzer darin stellt Anwendungsentwickler vor die Herausforderung, Systeme zu entwickeln, die flexibel bezüglich der verbundenen und verwendeten Geräte und Modalitäten sind. Dies impliziert auch neue Möglichkeiten in der modalitätsübergreifenden Kommunikation, die über duale Interaktionskonzepte, wie sie heutzutage bereits üblich sind, hinausgehen. Die vorliegende Arbeit befasst sich mit der Unterstützung von Anwendungsentwicklern mit Hilfe einer Plattform zur deklarativen und modellbasierten Entwicklung von multimodalen Dialogapplikationen mit einem Fokus auf verteilte Ein- und Ausgabegeräte in cyber-physischen Umgebungen. Die bearbeiteten Aufgaben können grundlegend in drei Teile gegliedert werden: - Die Konzeption von Modellen und Strategien für die Spezifikation von Dialoganwendungen in einem deklarativen Entwicklungsansatz. Dies beinhaltet Modelle für das Definieren von Projektressourcen, Dialogverhalten, Spracherkennergrammatiken, graphischen Benutzerschnittstellen und Abbildungsregeln, die die gerätespezifische Darstellung von Ein- und Ausgabegeräten in eine gemeinsame Repräsentationssprache transformieren. - Die Implementierung einer Laufzeitumgebung, die eine flexible und erweiterbare Architektur für die einfache Integration neuer Geräte und Komponenten bietet. Die Plattform realisiert Konzepte und Strategien der multimodalen Mensch-Maschine-Interaktion und ist die Basis vollwertiger multimodaler Dialoganwendungen für beliebige Domänen, Szenarien und Gerätekonfigurationen. - Eine Softwareentwicklungsumgebung, die in die Eclipse Rich Client Plattform integriert ist und Entwicklern Assistenten und Editoren an die Hand gibt, die das Erstellen und Editieren von neuen multimodalen Dialoganwendungen unterstützen

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Machine learning approaches to video activity recognition: from computer vision to signal processing

244 p.La investigación presentada se centra en técnicas de clasificación para dos tareas diferentes, aunque relacionadas, de tal forma que la segunda puede ser considerada parte de la primera: el reconocimiento de acciones humanas en vídeos y el reconocimiento de lengua de signos.En la primera parte, la hipótesis de partida es que la transformación de las señales de un vídeo mediante el algoritmo de Patrones Espaciales Comunes (CSP por sus siglas en inglés, comúnmente utilizado en sistemas de Electroencefalografía) puede dar lugar a nuevas características que serán útiles para la posterior clasificación de los vídeos mediante clasificadores supervisados. Se han realizado diferentes experimentos en varias bases de datos, incluyendo una creada durante esta investigación desde el punto de vista de un robot humanoide, con la intención de implementar el sistema de reconocimiento desarrollado para mejorar la interacción humano-robot.En la segunda parte, las técnicas desarrolladas anteriormente se han aplicado al reconocimiento de lengua de signos, pero además de ello se propone un método basado en la descomposición de los signos para realizar el reconocimiento de los mismos, añadiendo la posibilidad de una mejor explicabilidad. El objetivo final es desarrollar un tutor de lengua de signos capaz de guiar a los usuarios en el proceso de aprendizaje, dándoles a conocer los errores que cometen y el motivo de dichos errores