Awarded October 2017
Shadows are everywhere around us but we rarely notice them. Nevertheless, shadows do give us a wealth of information about the relative locations of the objects that cast them and the surfaces on which they fall. Our visual systems pick up this information to help us understand the layout of the scene around us and then suppress the shadows to help analyze the objects that we do pay attention to. In this project, we will examine the cues that human and machine vision systems can use to detect shadows and the depth information they reveal. We propose a specific application in screening out shadows from video images of traffic where we want to monitor the number of vehicles and where shadows often lead to errors in counting. There is also potential to use the shape and size of the shadow to improve estimates of vehicle dimensions.
In order to remain balanced and to navigate, the brain must combine information from multiple sensory sources –notably vision and movement information from the inner ear (vestibular inputs). The brain also has to instantly decide how much importance to place on each input to deal with common situations in which sensory inputs may become unstable (e.g., when vision is blurred) or inconsistent (e.g., while walking through a moving train). With age, there is often a decline in our senses, and changes in the way that sensory information is combined, sometimes resulting in errors. In this project we will assess whether older adults can process their self-movement as effectively as younger adults and whether changes in performance can be explained by changes in how they combine multisensory inputs. We will also assess whether performance can be improved by training. To characterize how healthy adults from 18-85 years old are able to integrate and adjust the processing of visual and vestibular inputs important for safe mobility. To assess whether training can make this integrative process more effective. A motion platform will provide physical motion, and an immersive virtual reality display will provide visual self-motion information. The directions provided by each of these simultaneous motions will be varied. Participants will decide if the directions agree or not and the amount of integration will be calculated. We will attempt to improve performance by providing feedback. This research will advance basic vision science and result in real-world applications associated with promoting safe mobility in older adults. The fundamental knowledge generated will be used to develop a technology-based, multisensory falls screening tool and falls prevention training application that will be deployable using widely available immersive VR systems.
There are very few robotic systems that are fully autonomous. Rather such systems areintended to interact with and respond to instructions given to them by human operators. As a consequence human-robot interaction and technologies to support such interaction have become key research problems in the robotics field. An enabling technology in the development of human-robotic interaction systems has been the development of cloud-based technologies for speech understanding and utterance generation. The IAHRI project proposes to exploit advances in cloud-based speech understanding and utterance generation to better understand effective interaction between humans and robots and to develop a general set of software tools that support naïve human interaction with autonomous systems. In particular it proposes to exploit one of a number of cloud-based speech understanding systems (e.g., Amazon’s Alexa) and to combine the capabilities of such systems with a cloud-based rendering mechanism to support a 3D avatar or puppet that is synchronized to the utterances of the system. The general nature of the approach allows the avatar to be customized in terms of the simulated emotional state of the autonomous system and the state of the humans with whom the system is interacting. The resulting human-robot system will be evaluated in terms of acceptance and utility in an academic setting. Furthermore, through our industrial partners we plan to deploy a human-robot interaction system based on these general tools on both the CloudConstable platform as well as the VirtualMe platform from Crosswing. These industrial implementations will not only inform the development of the software infrastructure planned in the IAHRI project, but they will also provide a unique opportunity to communicate the results of this project to a much wider audience. The CloudConstable platform, for example will provide the opportunity to integrate the IAHRI system within an IBM Watson AI XPRIZE consumer-oriented solution.
Computer vision technologies have become widely employed in the biomedical domain. Such technology is preferred for its objectivity, efficiency and precision. The objective of this project is to finalize development of the modified-Tinetti Mobility Assessment Tool (MAT) for assessment of mobility deterioration following natural aging, neurodegeneration, or brain injury. This is a validation of the algorithms for our non-invasive, unique, and purely objective computer vision video-tool. This tool was recently advanced from an early laboratory-based beta stage to one with upgraded hardware that accurately tracked humans. The system has now gone from a prototype to a operationally validated prototype able to perform a completely automated Modified Tinetti gait/balance test on adults. In this final phase of the project we will finalize the algorithms of the system by comparing its performance with independent clinician manual assessment and our own functional assessment tools in adults who have no clinical issues, have suffered concussion, or are in early-stage dementia. Validating the functionality, in particular its ability to accurately discriminate healthy from clinically anomalous posture and gait, and finalizing the user interface of this device will allow PhD Associates to advance a patent application and approach investors for commercialization with a market-ready product. PhD Associates will also be able to approach practitioners with an easy, precise, and low cost tool to estimate fall risk and incapacity from gait and balance (mobility) measurements. This work is important for the advancement in computer-vision assisted medical assessment, which will impact Canadian research in the field of rehabilitation, diagnosis, and function assessment. Another target market for the MAT is the personal injury law and the insurance industry, both of which would benefit from simple, computerized quick assessment of functional mobility resulting from an accident.
Sometimes in order to understand how something works, it needs to be taken apart to see how components work together. The same reverse engineering approach can be used to understand the brain and its components. One technique that allows a researcher to do this is with non-invasive brain stimulation. Noninvasive brain stimulation is a relatively new therapeutic tool for treatment of clinical depression. It is also a new experimental approach in cognitive neuroscience to understand brain networks and function. In both cases, the mechanisms of how brain stimulation works are not known. The goal of this research is to understand what is going on in the brain during non-invasive brain stimulation. How is the brain affected at the site of brain stimulation and how are the networks to which is connected affected? The approach will use brain imaging techniques (magnetic resonance imaging, MRI) to measure local and global effects of brain stimulation. This research relates to the goals of VISTA by providing fundamental advances to vision science with potential application to health technologies. The findings from this work will inform and guide policy for non-invasive neuromodulation use in clinical and laboratory settings.
Validation of novel neurofeedback training engine for improving brain health in aging and neurodevelopmental disorders
PI: W. Dale Stevens
There is a clear gap between modern neuroscience research and translation into effective products that can be self-administered by the public. Areas that require immediate attention given heavy caregiver burden and health care costs are memory impairments in aging and emotional regulation deficits in Autism Spectrum Disorder (ASD). Extensive evidence shows that neurofeedback training (NFT), an established technique for self-regulating brain waves, can have long-lasting effects on brain function in aging and ASD. It is now possible to administer NFT using accessible and inexpensive brain sensing technology. The challenge is to create highly-interactive visual interfaces that engage users to comply with extended NFT, critical for inducing neuroplasticity. xSensa Labs has developed mobile NFT for cognitive and emotional impairments in aging and ASD, respectively. Feedback during training is typically provided in visual form, yet there have been no comprehensive studies comparing different visual interfaces and their subsequent interactions with training efficacy. In a new partnership with York University, Mitacs Elevate, Unionville Home Society, and potentially VISTA, xSensa seeks to rigorously test its NFT engine and user interface. Over-arching aims of the proposal are to replicate previous laboratory findings and examine visual environments that are best-suited for augmenting NFT outcomes. Compelling work shows that greater immersion in nature even in its minimalist form (e.g. viewing photographs) can improve brain function. This project will compare three dynamic interfaces: an immersive natural landscape based on real footage, immersive 3D computer-generated artificial environment, and non-immersive standard interface with objects moving in 2D. The main hypothesis is that in both aging and ASD, real nature will lead to faster acquisition of NFT strategies, improvements in memory and attention for aging, and reduction in anxiety and enhanced mood in ASD. After validation, these innovative products will create large-scale impact on brain health in Canada and globally.