Craig Hennessey’s research while affiliated with British Columbia Institute of Technology and other places

Neuroscience communication is increasingly taking place on multidirectional social media platforms, creating new opportunities but also calling for critical ethical considerations. Twitter, one of the most popular social media applications in the world, is a leading platform for the dissemination of all information types, including emerging areas of neuroscience such as optogenetics, a technique aimed at the control of specific neurons. Since its discovery in 2005, optogenetics has been featured in the public eye and discussed extensively on social media, but little is known about how this new technique is portrayed and who the users participating in the conversation are. To address this gap, we conducted content analysis of a sample of 1000 tweets mentioning “optogenetics” over a one-year period between 2014 and 2015. We found that academic researchers are the largest group contributing to the conversation, that the tweets often contain links to third-party websites from news organizations and peer-reviewed journals, and that common thematic motifs include the applications of optogenetics specifically for the control of brain activity and the treatment of disease. We also found that the majority of the tweets are neutral in their tone regarding optogenetics. As Twitter serves as a current and dynamic forum for exchange about advances in neuroscience, the conversation about optogenetics on this engaging platform can inform socially-responsive knowledge dissemination efforts in this area.

Social media is broadening opportunities to engage in discussions about biomedical advances such as stem cell research. However, little is known about how information pertaining to stem cells is disseminated on platforms such as Twitter. To fill this gap, we conducted a content analysis of tweets containing (i) a stem cell keyword, and (ii) a keyword related to either spinal cord injury (SCI) or Parkinson disease (PD). We found that the discussion about stem cells and SCI or PD revolves around different aspects of the research process. We also found that the tone of most tweets about stem cells is either positive or neutral. The findings contribute new knowledge about Twitter as a connecting platform for many voices and as a key tool for the dissemination of information about stem cells and disorders of the central nervous system.

Online social media is widespread, easily accessible and attracts a global audience with a widening demographic. As a large proportion of adults now seek health information online and through social media applications, communication about health has become increasingly interactive and dynamic. Online health information has the potential to significantly impact public health, especially as the population gets older and the prevalence of dementia increases. However, little is known about how information pertaining to age-associated diseases is disseminated on popular social media platforms. To fill this knowledge gap, we examined empirically: (i) who is using social media to share information about dementia, (ii) what sources of information about dementia are promoted, and (iii) which dementia themes dominate the discussion. We data-mined the microblogging platform Twitter for content containing dementia-related keywords for a period of 24 hours and retrieved over 9,200 tweets. A coding guide was developed and content analysis conducted on a random sample (10%), and on a subsample from top users' tweets to assess impact. We found that a majority of tweets contained a link to a third party site rather than personal information, and these links redirected mainly to news sites and health information sites. As well, a large number of tweets discussed recent research findings related to the prediction and risk management of Alzheimer's disease. The results highlight the need for the dementia research community to harness the reach of this medium and its potential as a tool for multidirectional engagement.

The demand for improved human computer interaction will lead to increasing adoption of eye tracking in everyday devices. For interaction with devices such as Smart TVs, the eye tracker must operate in more challenging environments such as the home living room. In this paper we present a non-contact eye tracking system that allows for freedom of viewer motion in a living room environment. A pan and tilt mechanism is used to orient the eye tracker, guided by face tracking information from a wide-angle camera. The estimated point of gaze is corrected for viewer movement in realtime, avoiding the need for recalibration. The proposed technique achieves comparable accuracy to desktop systems near the calibration position of less than 1° of visual angle and accuracy of less than 2° of visual angle when the viewer moved a large distance, such as standing or sitting on the other side of the couch. The system performance achieved was more than sufficient to operate a novel, hands-free Smart TV interface.

There is no standard software interface in the eye-tracking industry, making it difficult for developers to integrate eye-gaze into their applications. The combination of high cost eye-trackers and lack of applications has resulted in a slow adoption of the technology. To expand the adoption of eye-gaze in everyday applications, we present an eye-gaze specific application programming interface that is platform and language neutral, based on open standards, easily used and extended and free of cost.

Remote eye-gaze tracking provides a means for nonintrusive tracking of the point-of-gaze (POG) of a user. For application as a user interface for the disabled, a remote system that is noncontact, reliable, and permits head motion is very desirable. The system-calibration-free pupil-corneal reflection (P-CR) vector technique for POG estimation is a popular method due to its simplicity, however, accuracy has been shown to be degraded with head displacement. Model-based POG-estimation methods were developed, which improve system accuracy during head displacement, however, these methods require complex system calibration in addition to user calibration. In this paper, the use of multiple corneal reflections and point-pattern matching allows for a scaling correction of the P-CR vector for head displacements as well as an improvement in system robustness to corneal reflection distortion, leading to improved POG-estimation accuracy. To demonstrate the improvement in performance, the enhanced multiple corneal reflection P-CR method is compared to the monocular and binocular accuracy of the traditional single corneal reflection P-CR method, and a model-based method of POG estimation for various head displacements.

Binocular eye-gaze tracking can be used to estimate the point-of-gaze (POG) of a subject in real-world 3-D space using the vergence of the eyes. In this paper, a novel noncontact model-based technique for 3-D POG estimation is presented. The noncontact system allows people to select real-world objects in 3-D physical space using their eyes, without the need for head-mounted equipment. Remote 3-D POG estimation may be especially useful for persons with quadriplegia or Amyotrophic Lateral Sclerosis. It would also enable a user to select 3-D points in space generated by 3-D volumetric displays, with potential applications to medical imaging and telesurgery. Using a model-based POG estimation algorithm allows for free head motion and a single stage of calibration. It is shown that an average accuracy of 3.93 cm was achieved over a workspace volume of 30 x 23 x 25 cm (W x H x D) with a maximum latency of 1.5 s due to the digital filtering employed. The users were free to naturally move and reorient their heads while operating the system, within an allowable headspace of 3 cm x 9 cm x 14 cm.

The precision of point-of-gaze (POG) estimation during a fixation is an important factor in determining the usability of a noncontact eye-gaze tracking system for real-time applications. The objective of this paper is to define and measure POG fixation precision, propose methods for increasing the fixation precision, and examine the improvements when the methods are applied to two POG estimation approaches. To achieve these objectives, techniques for high-speed image processing that allow POG sampling rates of over 400 Hz are presented. With these high-speed POG sampling rates, the fixation precision can be improved by filtering while maintaining an acceptable real-time latency. The high-speed sampling and digital filtering techniques developed were applied to two POG estimation techniques, i.e., the high-speed pupil-corneal reflection (HS P-CR) vector method and a 3-D model-based method allowing free head motion. Evaluation on the subjects has shown that when operating at 407 frames per second (fps) with filtering, the fixation precision for the HS P-CR POG estimation method was improved by a factor of 5.8 to 0.035 degrees (1.6 screen pixels) compared to the unfiltered operation at 30 fps. For the 3-D POG estimation method, the fixation precision was improved by a factor of 11 to 0.050 degrees (2.3 screen pixels) compared to the unfiltered operation at 30 fps.

Eye-gaze tracking devices are typically used to estimate the point-of-gaze (POG) of a subject on a 2D surface such as a computer screen. Using model based methods for POG estimation we have developed a system based on the vergence of the eyes which can be used to estimate the POG on a real-world volumetric display.

Eye-gaze as a form of human machine interface holds great promise for improving the way we interact with machines. Eye- gaze tracking devices that are non-contact, non-restrictive, accurate and easy to use will increase the appeal for including eye-gaze information in future applications. The system we have developed and which we describe in this paper achieves these goals using a single high resolution camera with a fixed field of view. The single camera system has no moving parts which results in rapid reacquisition of the eye after loss of tracking. Free head motion is achieved using multiple glints and 3D modeling techniques. Accuracies of under 1° of visual angle are achieved over a field of view of 14x12x20 cm and over various hardware configurations, camera resolutions and frame rates.