The University of Waterloo’s researchers have developed a revolutionary tool called MotionBlocks, which makes virtual reality (VR) more accessible to people with mobility limitations. Traditional VR games require large and dramatic movements, such as raising one’s arms above the head or quickly side-stepping, making it difficult or impossible for individuals who use wheelchairs or have limited mobility. However, MotionBlocks changes this narrative by allowing users to customize the game’s controls to fit their unique abilities.

According to Johann Wentzel, a recent PhD graduate in Computer Science and lead author of the research, VR games are an essential social space, especially for younger generations. However, the existing technology creates a barrier for people with disabilities, excluding them from these social spaces. The researchers’ goal is to break down this barrier by providing a customizable solution that enables everyone to enjoy VR experiences.

The development process involved three phases. First, the team invited 10 individuals with limited mobility to play games, share their frustrations, and discuss possible solutions. This feedback was then used to create MotionBlocks, which modifies how VR game platforms understand controller inputs. The tool is designed to be user-friendly and flexible, allowing users to pick a simple shape and tune it to capture the type of movements they can make.

For example, a user with limited mobility might configure a small circular range of motion on a desk or big hemispherical arm swings for BeatSaber. MotionBlocks figures out how to translate these movements to the ones prescribed by the game. The researchers tested MotionBlocks in collaboration with eight study participants, and the feedback was overwhelmingly positive.

The users reported greater enjoyment, less fatigue, and a feeling that something was finally designed for people with disabilities. While the researchers have no current plans to sell or release MotionBlocks, they hope it will serve as a proof-of-concept for the VR industry. Their ultimate goal is to see motion accessibility features integrated into every VR system on the market, making it possible for everyone to access VR in a way that works for their needs.

The research was presented at the CHI Conference on Human Factors in Computing Systems, held in Japan from April 26 to May 1, 2025.

The article delves into a groundbreaking study led by two undergraduate medicine students at the University of Galway, where they investigated how cardioprotective glucose-lowering therapies impact dementia risk. Published in JAMA Neurology, this research analyzed 26 clinical trials involving over 160,000 participants to determine if diabetes medications can prevent cognitive decline.

While most glucose-lowering therapies didn’t show a significant association with reduced dementia risk, one class of drugs – GLP-1 receptor agonists (GLP-1Ras) – revealed a substantial reduction in dementia risk. This discovery has crucial implications for public health, particularly as diabetes and dementia prevalence continue to rise.

Dr. Catriona Reddin, senior author and researcher at the University of Galway, expressed her enthusiasm for this research, stating that it represents a significant contribution to understanding how some diabetes medications affect brain health. She noted that diabetes is a known risk factor for dementia, but whether glucose-lowering therapies can prevent cognitive decline has remained unclear.

Professor Martin O’Donnell, Dean of the College of Medicine, Nursing and Health Sciences at University of Galway, commended the undergraduate medicine students for leading this high-impact study. He emphasized the importance of research as a core component of their undergraduate program, ensuring that students engage in meaningful studies shaping global healthcare.

This pioneering work highlights the potential benefits of GLP-1Ras in preventing dementia and underscores the significance of continued research into diabetes medications’ effects on brain health. The findings have far-reaching implications for public health and demonstrate the value of student-led research in advancing our understanding of complex health issues.

The increasing reliance on artificial intelligence (AI) models in various fields of science, including chemistry, biology, and medicine, has raised concerns about the potential risks associated with these powerful tools. Researchers from the University of Bonn have now published a study highlighting the importance of explainability and plausibility checks when using AI procedures in scientific research.

The study’s lead author, Prof. Dr. Jürgen Bajorath, emphasizes that “AI models are black boxes” and should not be blindly trusted. He warns against over-interpreting the results of these models, as they can often provide seemingly plausible explanations for their conclusions without actually understanding the underlying mechanisms.

One of the key challenges in using AI tools is the lack of transparency and interpretability of their decision-making processes. While AI algorithms are incredibly powerful, they can be difficult to understand and may rely on irrelevant features or correlations. This is particularly concerning when it comes to applications in fields like chemistry and medicine, where accuracy and reliability are crucial.

To address this issue, researchers have been working on developing “explainable” AI methods that can provide insights into the decision-making processes of these models. However, even with these tools, there is still a need for careful evaluation and critical thinking when using AI results in scientific research.

The study’s authors stress that experiments are often required to validate the findings of AI models and to determine their relevance to real-world applications. They also highlight the importance of plausibility checks, which involve assessing whether the features or correlations identified by the AI model can actually be responsible for the desired chemical or biological properties.

In conclusion, while AI tools have the potential to revolutionize various fields of science, it is essential to approach their use with caution and critical thinking. By understanding the limitations and strengths of these models, researchers can ensure that they are used responsibly and effectively, ultimately advancing scientific knowledge and improving our world.

The study’s findings emphasize the importance of explainability and plausibility checks when using AI procedures in scientific research, highlighting the need for careful evaluation and critical thinking to avoid misinterpretation of AI results.