Virtual reality in neurologic rehabilitation of spatial disorientation

BACKGROUND: Topographical disorientation (TD) is a severe and persistent impairment of spatial orientation and navigation in familiar as well as new environments and a common consequence of brain damage. Virtual reality (VR) provides a new tool for the assessment and rehabilitation of TD. In VR training programs different degrees of active motor control over navigation may be implemented (i.e. more passive spatial navigation vs. more active). Increasing demands of active motor control may overload those visuo-spatial resources necessary for learning spatial orientation and navigation. In the present study we used a VR-based verbally-guided passive navigation training program to improve general spatial abilities in neurologic patients with spatial disorientation. METHODS: Eleven neurologic patients with focal brain lesions, which showed deficits in spatial orientation, as well as 11 neurologic healthy controls performed a route finding training in a virtual environment. Participants learned and recalled different routes for navigation in a virtual city over five training sessions. Before and after VR training, general spatial abilities were assessed with standardized neuropsychological tests. RESULTS: Route finding ability in the VR task increased over the five training sessions. Moreover, both groups improved different aspects of spatial abilities after VR training in comparison to the spatial performance before VR training. CONCLUSIONS: Verbally-guided passive navigation training in VR enhances general spatial cognition in neurologic patients with spatial disorientation as well as in healthy controls and can therefore be useful in the rehabilitation of spatial deficits associated with TD

ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning

Spatial prediction of the radio propagation environment of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be categorized as 'reactive' spatial prediction, where the predictions are made based on a small set of measurements from an active transmitter whose radio environment is to be predicted. Emerging spectrum-sharing paradigms would benefit from 'proactive' spatial prediction of the radio environment, where the spatial predictions must be done for a transmitter for which no measurement has been collected. This paper proposes a novel, supervised deep learning-based framework, ProSpire, that enables spectrum sharing by leveraging the idea of proactive spatial prediction. We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference. ProSpire relies on the crowdsourcing of transmitters and receivers during their normal operations to address some of the aforementioned challenges. The core component of ProSpire is a deep learning-based image-to-image translation method, which we call RSSu-net. We generate several diverse datasets using ray tracing software and numerically evaluate ProSpire. Our evaluations show that RSSu-net performs reasonably well in terms of signal strength prediction, 5 dB mean absolute error, which is comparable to the average error of other relevant methods. Importantly, due to the merits of RSSu-net, ProSpire creates proactive boundaries around transmitters such that they can be activated with 97% probability of not causing interference. In this regard, the performance of RSSu-net is 19% better than that of other comparable methods.Comment: 9 page

Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

Integrating Spatial Working Memory and Remote Memory: Interactions between the Medial Prefrontal Cortex and Hippocampus

In recent years, two separate research streams have focused on information sharing between the medial prefrontal cortex (mPFC) and hippocampus (HC). Research into spatial working memory has shown that successful execution of many types of behaviors requires synchronous activity in the theta range between the mPFC and HC, whereas studies of memory consolidation have shown that shifts in area dependency may be temporally modulated. While the nature of information that is being communicated is still unclear, spatial working memory and remote memory recall is reliant on interactions between these two areas. This review will present recent evidence that shows that these two processes are not as separate as they first appeared. We will also present a novel conceptualization of the nature of the medial prefrontal representation and how this might help explain this area’s role in spatial working memory and remote memory recall

Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. In this paper, we propose a feedback-efficient active preference learning approach, FAPL, that distills human comfort and expectation into a reward model to guide the robot agent to explore latent aspects of social compliance. We further introduce hybrid experience learning to improve the efficiency of human feedback and samples, and evaluate benefits of robot behaviors learned from FAPL through extensive simulation experiments and a user study (N=10) employing a physical robot to navigate with human subjects in real-world scenarios. Source code and experiment videos for this work are available at:https://sites.google.com/view/san-fapl.Comment: To appear in IROS 202

Teaching Space Syntax Through Reflective Practice

12-15 June 200