Realidade aumentada móvel aplicada na navegação indoor para cadeirantes

Physical deficiency is an obstacle to the afflicted individual, as they are deprived from realizing routine activities, without the help of others. For many, the use of wheelchairs is fundamental to providing mobility and social inclusion. However, these individuals still come up against a series of challenges in order to improve their life quality. Among the many difficulties, one in particular is highlighted: navigation in indoor environments buildings, such as localizing the shortest and best route for arriving at a desired destination. In the context of the Information Society, the use of pervasive computation and intelligent environments have application potential in supporting navigation assisted by mobile devices. In this scenario, it is noted that there exist a sparse quantity of applications capable of attending to the special needs of wheelchair users. Therefore, this study considers the hypothesis that other technologies, such as Mobile Augmented Reality (AR), possess the potential to facilitate the navigation of wheelchair users in indoor environments. In light of the above, the main motive behind this research study is to investigate computational techniques that support the use of indoor navigation based on Mobile AR, especially those which possess total control over their upper limbs. In order to achieve such, this work study proposes an architecture to support the development of these applications. Experiments were performed with wheelchair user volunteers. These interacted with an application via touch or voice commands in order to navigate within a test environment. This environment proposes the use of navigation arrows through use of AR. The features implemented onto the proposed architecture were capable of providing significant benefits for indoor navigation. Especially, when compared to traditional techniques.Tese (Doutorado)A deficiência física é um obstáculo aos portadores, uma vez que os mesmos são privados de realizar atividades rotineiras, sem auxílio de outros. Para muitos, o uso de cadeiras de rodas é fundamental para proporcionar mobilidade e inclusão social. No entanto, cadeirantes ainda enfrentam uma série de desafios para melhorar sua qualidade de vida. Entre as muitas dificuldades, uma em especial se destaca: a navegação em ambientes internos (indoor) de edificações, tais como a localização do menor e melhor caminho para chegar ao seu destino final. No contexto da Sociedade da Informação, o uso de computação pervasiva e de ambientes inteligentes tem potencial de aplicação no apoio à navegação suportada por dispositivos móveis. Neste cenário, observa-se parca quantidade de aplicações capazes de atender as necessidades especiais de cadeirantes. Portanto, este trabalho considera a hipótese de que outras tecnologias como a Realidade Aumentada (RA) Móvel, possui potencial para facilitar a navegação de cadeirantes em ambientes fechados. Diante disso, a motivação principal desta pesquisa é investigar técnicas computacionais que suportem o uso da navegação indoor de cadeirantes, baseada na RA Móvel, especialmente os que possuem total controle dos membros superiores. Para tanto, este trabalho propõe uma arquitetura para suportar o desenvolvimento destas aplicações. Experimentos foram realizados com voluntários cadeirantes. Estes interagiram com a aplicação por meio de comandos de toque ou de voz, para navegar dentro de um ambiente de teste. Este ambiente propõe o uso de setas de navegação com o uso de RA. As características implementadas na arquitetura proposta foram capazes de proporcionar benefícios significativos para navegação indoor de cadeirantes. Principalmente, quando comparado com técnicas tradicionais. Palavras-chave: realidade aumentada móvel, navegação indoor, cadeirantes

Erfassung, Erkennung und qualitative Analyse von menschlicher Bewegung

Visionen vom Internet of Things und der nahtlosen Einbettung der virtuellen Welt in den physischen Alltag des Menschen sind durch ubiquitäre Vernetzung, stationäre und mobile Computer sowie miniaturisierte Sensorik längst Realität geworden. Zusammen mit Algorithmen des Data Minings und der künstlichen Intelligenz werden so kontextsensitive Dienste und vernetzte Alltagsgegenstände geschaffen, welche einen immensen Mehrwert im privaten, kommerziellen und industriellen Raum schaffen. Im Rahmen dieses Szenarios vielbeachtete Forschungsgebiete sind die Erschließung von menschlichem Kontext und die menschliche Aktivitätserkennung mithilfe von mobiler Sensorik. Während es auf dem Gebiet der rein quantitativen Erkennung von menschlicher Aktivität bereits viele Verfahren zur Vorhersage und Erkennung von Bewegungsereignissen auf Basis von Bewegungs- oder Tiefeninformationen sowie visueller Sensorik gibt, sind Konzepte zur feingranularen, automatisierten Analyse mit qualitativem Schwerpunkt bislang kaum existent. Typische Anwendungsgebiete für diese sind zum Beispiel die Identifikation von Notfallsituationen im medizinischen Bereich oder die Erkennung von Fehlstellungen und Anomalien bei physischer, menschlicher Aktivität. Um solche Fragestellungen aus dem Bereich der Erfassung, Erkennung und qualitativen Analyse von menschlicher Bewegungsaktivität zu adressieren, wird in dieser Arbeit zunächst ein ganzheitliches, verteiltes Sensorsystem, welches auf Basis von Bewegungsinformationen menschliche Bewegungsaktivität untersucht, spezifiziert. Anschließend wird ein Vorgehen zur automatisierten und qualitativen Analyse individueller, wiederkehrender, menschlicher Bewegungsereignisse, mithilfe eines adaptiven Segmentierungsverfahrens und eines Konzepts zur Formalisierung und Diskretisierung von subjektiven Qualitätsmerkmalen in menschlichen Bewegungsabläufen, vorgestellt. Im Anschluss steht die qualitative Untersuchung von nicht vorhersehbarer, menschlicher Bewegungsaktivität im Fokus. Hierzu werden neue Konzepte zur Segmentierung und zur generischen Projektion der physischen, menschlichen Leistung des Menschen auf diskrete Merkmalsvektoren vorgestellt. Zusammengefasst stellt die vorliegende Arbeit ein umfassendes Paket zur generischen Untersuchung von menschlicher Bewegungsaktivität vor. Mit diesem lassen sich quantitative und qualitative Analysen von Bewegungsaktivität für sowohl wiederkehrende als auch für nicht vorhersehbare, menschliche Bewegungsereignisse effizient umsetzen.The visions created by the Internet of Things, which encompass the seamless embedding of the virtual world into daily human life have become reality by now. One important reason for that is the ubiquitous availability of fast communication connections, of stationary as well as of mobile computers, and of miniaturized sensors and wearables. Thereby, the combination of this infrastructure along with data mining algorithms and concepts of artificial intelligence allow the creation of context-aware services and interconnected items of daily life, which provide vast added value for potential users. Related to that, the recognition of human activity by using mobile sensors as a basis and the extraction of human context are currently much-noticed aspects within this field of research. By now, concepts focusing on quantitative recognition of human activity are a well studied area while examinations targeting qualitative analysis are currently handled only sparsely. Typical szenarios of usage for such concepts, which target qualitative analysis, are, e.g., the detection of emergency cases within medical environments or of anomalies within human movement during the conduction of physical activities. In order to target research questions related to these qualitative topics, a holistic, distributed sensor system, which is capable of capturing and analyzing human motion is developed within this work at first. Subsequently, a concept for automated, qualitative assessment of individual, recurrent human motion events is presented. Therefore, it makes usage of a new adaptive segmentation algorithm and proposes an advance for formalization and discretization of subjective characteristics of quality within physical human activities. Afterwards, this work deals with the qualitative analysis of non-recurrent human motion. For this purpose, new concepts of segmentation are necessary and a new approach for generic projection of physical human performance onto discrete feature vectors is introduced. To sum up, this thesis presents a comprehensive package for generic analysis of human motion activity. Therefore, it enables the detailed and efficient examination of recurrent as well as of non-recurrent human motion and moreover, allows a qualitative as well as a quantitative focus of analysis