Virtual Reality Innovation Impact

Behavior Analysis using Virtual Reality. As technology advances, virtual reality (VR) has emerged as a valuable tool for workforce evaluation and understanding. Specifically, using VR in behavior analysis can provide invaluable insights into how teams work together and allow organizations to optimize their operations to achieve maximum efficiency and improved outcomes. In addition, VR-based training can help individuals gain a better understanding of complex data sets, allowing them to use decision intelligence (DI) more effectively and accurately. At the forefront, 4Cast is incorporating VR into their decision intelligence models, unlocking critical insights that allow teams to make informed decisions in real-time. One of the key benefits of using VR-based decision intelligence models is that they can take into account the way people think and interact, which is critical when conducting mission-oriented tasks. This is especially important in hazardous and extreme operations, where quick decisions based on accurate data can make all the difference. By providing a comprehensive solution for accurate predictions and data-driven insights, VR-based behavior analysis can help organizations optimize their operations and achieve better outcomes.

Apple’s Vision Pro was released in the UK last month & it’s still a perfect case study in screen psychology. The Vision Pro is more than just a fancy VR gadget. It's a glimpse into the future of how we interact with screens. Here are 3 insights we can learn from early users: 1. Location-based content: Users group content by location. Work apps in the workspace, entertainment in the living room, & cooking tutorials in the kitchen. 2. Interactivity: It's not just about watching content. Users can interact with their environment - typing on virtual keyboards, moving screens & checking off to-do lists. 3. Unlimited screens: Users can (& will!) place virtual screens anywhere. When given complete freedom, we choose to have plenty of screens. These trends are not just about virtual reality. They reflect a broader shift in how we use screens, even in a virtual world. It’s easy to see how this connects to the workplace. Homes are dynamic - we’re up & moving about to complete tasks - & we want access to relevant information everywhere we go. For the deskless workforce, it’s exactly the same. Workers need the latest, relevant information available to them at all times. The best way to do that is with screens. #Innovation #Technology #ContentCreation #ScreensThatCommunicate

XR user input is fragmented across devices - but over time, standards emerged. This infographic summarizes the evolution of XR input over the last 12 years. Made it while preparing talks on eye-hand symbiosis and thought it might be useful to share. It focuses on the modern XR era that began with Oculus and the subsequent wave of consumer XR devices. The control schemes largely fall into four categories: an initial experimental phase, controller, hand tracking, and an emerging eye-hand paradigm. One common question: what is the best paradigm? It depends. Each interaction method offers different trade-offs: head pointing is precise and reliable, controllers are precise and fast [1,3], hand tracking is natural but can be tiring and less precise [2], and eye tracking reduces physical effort and can enable faster selection [3-5]. However, controllers require dedicated hardware, hand input can lead to arm fatigue [2], and in gaze + pinch, users coordinate two modalities [6]. Hence, newer paradigms don’t necessarily replace older ones. Controllers remain essential for high-precision and games, hand input enables expressive interaction, and gaze + pinch suits basic spatial UIs. Another question: how did we get to Gaze + pinch? It is the first widely deployed paradigm that uses the eyes as an active input modality in consumer systems. This indicates another, more fundamental shift: interaction is no longer primarily driven by manual input, but by attention. If you want to know more about the characteristics of the new eye-hand input paradigm, check out my recent posts on Gaze + Pinch Design Principles and Issues: Gaze + pinch Principles: https://lnkd.in/gVZtMPZc Gaze + pinch Issues: https://lnkd.in/gnCy6n2W References [1] Head vs controller vs gaze pointing: https://lnkd.in/gX5ukCuY [2] Gorilla arm / mid-air fatigue: https://lnkd.in/gbqCTnCz [3] Head vs gaze performance: https://lnkd.in/gUenV3e3 [4] Gaze vs hand ray: https://lnkd.in/gtaaprwG [5] Gaze vs hand ray: https://lnkd.in/dnpV4cWc [6] Gaze + pinch principles: https://lnkd.in/dqxCNxxD Source: Most info I obtained from the amazing VRcompare. There’s a lot more details for each device, check it out: https://vr-compare.com/

💎 30 years of #VRpsychology — what do we actually know? A new paper published in Nature Human Behaviour — Five canonical findings from 30 years of psychological experimentation in virtual reality — summarizes three decades of experiments in virtual reality. The research was conducted by scientists from Stanford University and Michigan State University, led by Jeremy N. Bailenson. The team analyzed hundreds of studies and meta-analyses to identify the five most reliable psychological effects of VR. Here are the key findings: 1. VR works best when users actively do, not just watch ➡︎ Strong sense of presence triggers real emotional and physiological reactions ➡︎ Most effective for procedural training and exposure therapy ➡︎ Less consistent impact in communication and passive experiences I➡︎ mmersion matters most when physical and emotional engagement is required 2. Avatars change behavior (and even attitudes) ➡︎ Users experience “body ownership” over virtual bodies ➡︎ Avatar height, attractiveness, or body type influences behavior (Proteus effect) ➡︎ VR perspective-taking increases empathy (race, disability, age simulations) ➡︎ Effects can persist after removing the headset 3. VR improves procedural and spatial learning more than abstract knowledge ➡︎ Ideal for step-by-step operational training ➡︎ Strong impact on spatial understanding and navigation ➡︎ Physical interaction improves retention and decision-making ➡︎ Poorly designed VR can cause cognitive overload 4. Body tracking creates powerful analytics — and privacy challenges ➡︎ VR tracks head, hands, gaze, gestures, and behavior patterns ➡︎ Data can reveal engagement, stress, and intent ➡︎ Motion signatures can identify users with >90% accuracy in minutes ➡︎ Raises important privacy and ethics considerations 5. Distance perception in VR is often inaccurate ➡︎ Users typically underestimate distances ➡︎ Can affect training involving precision or movement ➡︎ Influenced by field of view, rendering, and hardware limitations ➡︎ Training scenarios must be carefully designed and validated After 10+ years implementing VR in large enterprises, most of these findings match real-world deployments — especially around procedural training, behavior change, and spatial learning. A few (like motion-based identification risks) are still underappreciated. 🔴 The conclusion is clear: VR is powerful — but only when designed around human psychology, not just technology. At ConnectedRealities.eu , we combine behavioral science with immersive design to build #VR #AR training for enterprises — from safety procedures and onboarding to high-pressure decision simulations. How are you currently using VR — as a “nice-to-have” experiment, or as a core tool for training and behavior change? I'm curious to hear your thoughts.

VR training rarely fails because of hardware. It fails because of incorrect assumptions about how people learn and perform under pressure. One common mistake is treating VR as a visual product rather than a training system. High-end graphics without cognitive load, uncertainty, and time pressure do little to improve operational performance. Real value comes from forcing decisions under stress, not from visual realism alone. Another issue is over-centralization. Training content is often developed as a fixed, centrally managed library. In operational environments, relevance erodes quickly. Scenarios must be adaptable, locally configurable, and continuously updated by instructors close to real-world operations. Human behavior is also frequently oversimplified. Non-player characters tend to act predictably, which results in training compliance instead of judgment. Trainees quickly learn how to “solve” scenarios rather than respond authentically, undermining transfer to real situations. Finally, VR is often disconnected from the broader training cycle. Without a structured after-action review, measurable performance data (Moneyball, anyone?), and repeated exposure across increasing stress levels, VR becomes a one-off experience rather than a capability-building tool. Effective VR training is not about immersion for its own sake. It is about strengthening decision-making, improving coordination under pressure, and accelerating learning loops between experience, reflection, and adaptation.

New paper alert! Gaze Inputs for Targeting: The Eyes Have It, Not With a Cursor Eye tracking is enabling people to use gaze for interactions in AR, VR and other domains. Can eye tracking enable users to target and select elements as well or better than controller or head-based targeting? Here, we explored visual feedback methods (none, cursor, outline, and resize) that let a person know where the system thinks their gaze is pointing. We tested this with different sizes of objects and we also assessed signal quality requirements. We found that: 1) high-quality eye tracking outperforms controllers in throughput and matches them in movement time and subjective measures; 2) cursors are the least preferred form of visual feedback; and 3) even with degraded eye tracking accuracy, gaze input remains comparable to controllers and outperforms head tracking for larger elements. For more details, you can access the full paper, open access, with the link in the comments. Thanks to a great team of collaborators at Meta Reality Labs Research: Ajoy Fernandes, Immo Schuetz, and Scott Murdison and support for study logistics from Joseph Zhang, Carina Thiemann, Duane Sawyer, Joel Shook and Ken Koh.

Weekend Research Deep Dive #05 — AI-Enhanced XR for Learning & Training (2024–2025) Continuing the weekend series where I break down one high-value research area for builders, educators, and XR/AI practitioners. This week’s theme: How AI-driven personalization, adaptive feedback, and multimodal interaction are transforming XR learning from static experiences into responsive learning systems. 🔹 This week’s reads 1. Evaluating eXtended Reality (XR) and Desktop Modalities for AI Education   Feijoo-Garcia et al., 2025   https://lnkd.in/gEp5zHxx Shows that immersive XR environments outperform desktop learning for AI education in engagement and retention, highlighting the role of spatial interaction in deeper cognitive processing. 2. LLM-Based Adaptive Feedback in XR Learning   Gianni et al., 2025   https://lnkd.in/g78BBHpf Introduces an AI-driven XR framework that adapts feedback and difficulty in real time, improving learner motivation while raising important design and ethical considerations. 3. Multimodal Natural Interaction for Wearable XR   Wang, 2025   https://lnkd.in/gidn4zJ6 Reviews AI-enabled interaction methods such as gaze, gesture, and voice, showing how natural input expands immersion and reduces interaction friction in learning environments. 🔹 Why it’s worth your coffee AI + XR is moving beyond immersion toward adaptive learning systems. The research points to three key shifts: 1. Adaptive learning loops   XR systems increasingly adjust guidance, pacing, and difficulty based on learner behavior. 2. Cognitive-aware design   AI enables XR experiences that manage cognitive load instead of overwhelming users. 3. Measurable learning outcomes   Behavior traces and interaction data make skill progression observable and assessable. 3 takeaways for practitioners: • Start with pedagogy first — XR + AI delivers value only when aligned with clear learning objectives.   • Use multimodal interaction intentionally — gaze, gesture, and voice should simplify learning, not distract.   • Track learning outcomes alongside engagement — immersion alone does not guarantee understanding. Question for the community: If you were designing an AI-enhanced XR learning system today, where would you focus first? (A) AI-guided tutoring   (B) Adaptive difficulty & feedback   (C) Multimodal interaction   (D) Learning analytics & assessment #XR #AI #HCI #EdTech #ImmersiveLearning #SpatialComputing #Research

🧠 We integrated AI + VR into a rehabilitation product for elderly patients and built a working system in just 3 months. This wasn’t a tech demo. It was a real, HIPAA-compliant product, with adaptive therapy logic, fatigue detection, and clinical metrics that made sense to doctors. If you think innovation has to be expensive, slow, or chaotic, let me show you what’s possible with structure and clarity. ▸ The context We were building a recovery product for post-stroke and cognitively impaired patients, many of them 65+ and easily overwhelmed. The goal: enable patients to complete therapy in a VR headset, while the system:  • adapts to their mental and physical fatigue in real time  • protects patient data (HIPAA-compliant)  • provides clear, actionable metrics to their doctors ▸ What I did — step by step 1. Mapped the recovery architecture with clinicians Before any design work, I led deep sessions with medical advisors. Together we defined: – how fatigue shows up in behavior – which signals the system should monitor – what insights doctors want to see (and what’s noise) 2. Defined the data model for AI Working with engineers and data leads, we identified key indicators: – reaction time – movement accuracy – micro-pauses – behavioral pattern shifts This became the foundation of an adaptive AI model that “understood” when a patient was tired and adjusted therapy accordingly. 3. Prototyped adaptive flow scenarios In the VR interface, I designed logic for: – auto-simplifying tasks during cognitive overload – switching to recovery mode when fatigue peaked – soft visual + verbal transitions to reduce stress 4. Built real-time clinical metrics The dashboard didn’t just show “progress.” It visualized: – concentration trends – error patterns – fatigue zones – recovery curves over time All in a format doctors could use, no noise, no clutter. 5. Integrated HIPAA by design I ensured we: – Separated PHI from analytics data – Encrypted all headset-to-cloud channels – Applied strict role-based access control – Removed raw video/audio logs from storage – Passed internal HIPAA audit before pilot launch ▸ The outcome – MVP ready in 12 weeks – 2 adaptive VR therapy flows live – Clinician-validated dashboard – Zero HIPAA violations in pilot phase – High engagement from patients 65+, even with no prior digital experience ▸ What matters ✅ Innovation isn’t adding AI Real innovation is adaptive structure + clinical value + architectural clarity And this approach isn’t just for healthcare. Whether you’re building in AI, VR, SaaS, or enterprise B2B, the logic holds: → Architecture → Data → Adaptation → Transparency → Compliance Only then: interface, scaling, and growth. ⸻ ▸ If your product is complex, regulated, or underperforming, I can help you structure it into a system that scales. Without reinventing everything. Without risking control. Follow Tanya R. for more. ⤷ Lead UX/UI Product Designer ♻️ Repost this to share with others!

🤖👓 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐑𝐨𝐛𝐨𝐭𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐌𝐞𝐭𝐚𝐯𝐞𝐫𝐬𝐞 𝐨𝐟 𝐌𝐨𝐭𝐢𝐨𝐧 What if teaching a robot to handle a wrench, stack a shelf, or guide a patient’s hand didn’t require lines of code—but instead, a headset? Across labs and factories, VR-empowered headsets are becoming a bridge between human expertise and robotic capability. Instead of manually programming every grasp or path, operators can step into immersive virtual environments, demonstrate the task naturally, and let the robot learn from their movements in real time. This approach isn’t just faster. It opens the door for: ⚡ Rapid skill transfer from human to machine 🧠 Better data for training embodied AI models 🌍 Remote collaboration—an expert in Detroit can “teach” a robot in Singapore 🦺 Safer learning, since robots can practice in virtual worlds before entering the real one As robots move into more complex, unstructured environments—construction sites, warehouses, even homes—the combination of VR and telepresence could be the key to scaling human-robot collaboration. We’re not just programming machines anymore. We’re coaching them. That’s a profound shift. 🎯 Selected Articles on the Topic: “𝐇𝐨𝐥𝐨-𝐃𝐞𝐱: 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐃𝐞𝐱𝐭𝐞𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐈𝐦𝐦𝐞𝐫𝐬𝐢𝐯𝐞 𝐌𝐢𝐱𝐞𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲” - A novel framework that lets a human teacher in VR teleoperate a robotic hand to collect demonstrations. The system learns dexterous tasks (in-hand rotation, bottle opening, etc.) from those demonstrations. (https://lnkd.in/ewQkvRmP) “𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐀𝐧 𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐕𝐑 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐨𝐟 𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐫𝐨𝐛𝐨𝐭𝐬” - Focuses on a VR setup for expert demonstration (via hand/pose tracking) to train construction robots using behavior cloning + RL. (https://lnkd.in/e2wTTRiy) “𝐕𝐑 𝐂𝐨-𝐋𝐚𝐛: 𝐀 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐟𝐨𝐫 𝐇𝐮𝐦𝐚𝐧–𝐑𝐨𝐛𝐨𝐭 𝐃𝐢𝐬𝐚𝐬𝐬𝐞𝐦𝐛𝐥𝐲 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐃𝐚𝐭𝐚 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧” - Develops a VR training system for human-robot collaborative tasks (e.g. disassembly), bridging simulation and real robot control via ROS, body tracking, and predictive models. (https://lnkd.in/egQre5Na) “𝐎𝐧 𝐭𝐡𝐞 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐨𝐟 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐑𝐞𝐚𝐥𝐢𝐭𝐲-𝐛𝐚𝐬𝐞𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐒𝐞𝐭𝐮𝐩” - Compares VR training vs conventional training approaches in robotic setup tasks, showing that VR-trained participants had better spatial awareness and reproducibility. (https://lnkd.in/eeHArQFQ) 👉 I’d love to hear: where do you see VR-based robot training making the biggest impact first—manufacturing, healthcare, or somewhere unexpected?

𝐀𝐒𝐂𝐄 𝐢𝟑𝐂𝐄 𝟐𝟎𝟐𝟓 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝟯 𝗮𝗻𝗱 𝟰 Yuming Zhang shared two impactful studies on immersive VR-based training systems for worker-robotics interaction in construction, supported by NSF Awards# 2402008 and 2410255. 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗛𝗮𝗽𝘁𝗶𝗰 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗶𝗻 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗜𝗺𝗺𝗲𝗿𝘀𝗶𝘃𝗲 𝗘𝘅𝗼𝘀𝗸𝗲𝗹𝗲𝘁𝗼𝗻 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗶𝗻 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 A compelling presentation was given by Yuming, introducing a VR-based training platform that incorporates haptic feedback to address the limitations of conventional visual and auditory guidance in worker-exoskeleton interaction. A simulated construction environment was developed to evaluate how tactile cues affect task performance. Results indicate that haptic feedback improves posture accuracy, reduces task duration, and enhances perceived usability, highlighting the role of multisensory interaction in promoting safer and more intuitive exoskeleton use. 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗟𝗼𝗮𝗱 𝗶𝗻 𝗪𝗼𝗿𝗸𝗲𝗿–𝗨𝗻𝗺𝗮𝗻𝗻𝗲𝗱 𝗚𝗿𝗼𝘂𝗻𝗱 𝗩𝗲𝗵𝗶𝗰𝗹𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 Yuming also shared insightful findings from a study focused on impact of VR-training on cognitive load during UGV operations. To address the cognitive demands of operating UGVs under dynamic construction conditions, a VR training platform was developed for interface-based UGV control. The study employed both subjective and physiological measures to assess cognitive load before and after training. Findings demonstrate that immersive VR training improves operational accuracy and reduces cognitive effort, supporting safer and more efficient human–robot collaboration. Congratulations to Yuming for driving forward innovation in immersive training and construction automation. Further details from these studies will be published in the ASCE i3CE 2025 Proceedings.