Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

Towards Natural Human Control and Navigation of Autonomous Wheelchairs

Approximately 2.2 million people in the United States depend on a wheelchair to assist with their mobility. Often times, the wheelchair user can maneuver around using a conventional joystick. Visually impaired or wheelchair patients with restricted hand mobility, such as stroke, arthritis, limb injury, Parkinson’s, cerebral palsy or multiple sclerosis, prevent them from using traditional joystick controls. The resulting mobility limitations force these patients to rely on caretakers to perform everyday tasks. This minimizes the independence of the wheelchair user. Modern day speech recognition systems can be used to enhance user experiences when using electronic devices. By expanding the motorized wheelchair control interface to include the detection of user speech commands, the independence is given back to the mobility impaired. A speech recognition interface was developed for a smart wheelchair. By integrating navigation commands with a map of the wheelchair’s surroundings, the wheelchair interface is more natural and intuitive to use. Complex speech patterns are interpreted for users to command the smart wheelchair to navigate to specified locations within the map. Pocketsphinx, a speech toolkit, is used to interpret the vocal commands. A language model and dictionary were generated based on a set of possible commands and locations supplied to the speech recognition interface. The commands fall under the categories of speed, directional, or destination commands. Speed commands modify the relative speed of the wheelchair. Directional commands modify the relative direction of the wheelchair. Destination commands require a known location on a map to navigate to. The completion of the speech input processer and the connection between wheelchair components via the Robot Operating System make map navigation possible

SmartWheels: Detecting urban features for wheelchair users’ navigation

People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed. In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and in-the-wild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier

An innovative system to assist the mobility of people with motor disabilities

International audiencePeople with motor disabilities require assistance for navigating form one location to another. In order to improve the integration of wheelchair users into their daily life and work, we propose a real time adaptive planning algorithm for routing the user through an obstacle free optimal path. Our application is based on an augmented reality system for the assistance of wheelchair people (ARSAWP) and uses augmented reality (AR) smart glasses. The main goal is to support the development of indoor and outdoor navigation systems devoted to wheelchair users. In this paper we detail the design, the implementation and the evaluation of the proposed application, which was implemented in java for the Android operational system. Two types of database are used (local database and remote database). The information about navigation is displayed on AR glasses which give the user the possibility to interact with the system according to the external environment. The prototype is designed for use within the University of Lille campus

Is Green Infrastructure a Game Changer for Sustainable Regional Development? A Scenario Approach for Stuttgart Region

There are numerous challenges for municipalities and regions: affordable housing, overburdened infrastructure, air quality, increasing pressure on open spaces, expansion of renewable energies, climate adaptation. Certainly, the list is not complete. Moreover, these issues – as a typical characteristic of complex situations – interact with one another in various ways. As one of Germany’s most dynamic and densely populated locations the Stuttgart Region is particularly affected by these challenges. Therefore, protecting and developing the landscape is a longstanding concern of its overall spatial planning strategy (e.g. development-axes, regional green corridors, landscape park, public transport policy). In view of the sheer number of tasks, the ongoing dynamics as well as strongsectoral policy instruments, the question can be raised as to how far green infrastructure can be a game changer for a substantive transformation towards sustainability. Against this background, a scenario-approach is carried out aiming for the integration of various knowledge-areas into a supra-sectoral and strategic view on regional transformation. Taking the example of the Stuttgart Region, the diversity and interdependencies of land use are taken into consideration and synthesised in form of a qualitative system analysis. The scenario development is part of the RAMONA-project, which is funded by the Federal Ministry of Education and Research (BMBF) in the framework of the “Stadt-Land-Plus”-measure. The project deals with the intervention regulation under the Nature Conservation Act1 (“Eingriffsregelung”) and inherent opportunities for urban and regional development. The scenario based approach therefore starts with open-space-indicators such as “degree of imperviousness”, “compensation measures” as well as “green infrastructures” and puts them into a wider perspective of socio-technical development (e. g. settlement structure, infrastructure, traffic volume, agriculture or health) in order to obtain comprehensive pictures of the Stuttgart Region in the year 2050