[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83320-en":3,"doc-seo-83320-105":28,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},83320,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","HeadRoom Lightweight Edge-deployable Pipeline for Adaptive Notification Routing","HeadRoom presents an adaptive notification routing approach for wearables by estimating the availability of visual and auditory sensory channels in real time. A lightweight edge-deployable pipeline analyzes egocentric video and audio streams and uses prediction error from lightweight visual/audio predictors as a proxy for channel load. A controlled user study with 25 participants shows that, under high perceptual load, routing notifications to the more available channel reduces response time and supports timing-aware multimodal interaction in wearable and immersive systems.","HeadRoom: Lightweight, Edge-deployable Pipeline for Adaptive  \nNotification Routing  \nDinithi Dissanayake  \n[dinithi@ahlab.org](dinithi@ahlab.org)[ ](dinithi@ahlab.org)Augmented Human Lab National University of Singapore Singapore  \nPrasanth Sasikumar  \n[prasanth@ahlab.org](prasanth@ahlab.org)[ ](prasanth@ahlab.org)Augmented Human Lab National University of Singapore Singapore  \nSuranga Nanayakkara  \n[suranga@ahlab.org](suranga@ahlab.org)[ ](suranga@ahlab.org)Augmented Human Lab National University of Singapore Singapore  \narXiv :2607 .08083v 1 [ cs .HC] 9 Jul 2026  \nEgocentric Visual and Audio Streams  \n\n| \u003Cbr>Visual\u003Cbr>Channel | \u003Cbr>Low Visual Channel Availability |\n| --- | --- |\n| \u003Cbr>Audio\u003Cbr>Channel |  |\n|  Real-time (\u003C15 ms)\u003Cbr> Lightweight (\u003C 1MB)\u003Cbr> Edge Deployable |  |\n\nChannel Availability Estimation  \nRoute to Available Channel  \nFigure 1: HeadRoom is a lightweight, low-latency pipeline that analyzes egocentric visual and auditory streams in real time to estimate channel availability and route notifications to the more available sensory channel, enabling deployment on wearable devices.  \nAbstract  \nEmerging wearables, such as smart glasses, can deliver notifications through multiple sensory channels, but there is still a limited understanding of how to choose the right channel at the right moment. We propose HeadRoom, a lightweight, edge-deployable pipeline that estimates the availability of visual and auditory channels in real time from egocentric video and audio. Our controlled user study (􀀣 = 25) shows that, under high perceptual load, routing notifications to the more available channel reduces response time relative to routing them to the less available channel. This work opens up a new possibility for adaptive routing of notifications in wearable and immersive systems.  \nCCS Concepts  \n• Human-centered computing → Ubiquitous and mobile computing systems and tools.  \nKeywords  \nmultimodal interaction, wearables, disruption  \n1 Introduction  \nSensory channels, such as the visual, auditory, and tactile pathways through which people perceive the world. For effective communication, choosing a sensory channel that is available under the user’s current perceptual load matters [21, 31, 32] .  \nFor example, a navigation prompt arriving during a visually demanding moment, or an audio alert in a noisy environment, does not simply add information to the scene. When poorly timedor poorly routed, such interventions can disrupt the attentional balance a person had found workable [3, 16] . It is well established that overlooking a user’s perceptual state results in missed signals, higher error rates, and a breakdown in flow [8, 11] . These failures might arise from a mismatch between an interface’s chosen output  \nmodality and the user’s momentary capacity in the corresponding sensory channel.  \nIn this paper, we study how a multimodal interactive system’s chosen output modality disrupts the user. We ground this problem in user’s moment-to-moment capacity: which sensory channel can absorb new information without disrupting the attentional balance the person is already maintaining? We term this the channel availability problem.  \nTo operationalize channel availability in real time, we borrow a principle from predictive coding [12, 23]: a sensory channel that is already heavily engaged will produce higher prediction error, because the incoming signal is harder to anticipate. We run lightweight predictors over egocentric visual and audio streams and treat their prediction error as a proxy for how occupied each channel is at any given moment. Higher error means the channel is under load; lower error means it is more available.  \nWe then evaluate our approach in a controlled psychophysical study (􀀣 = 25), where participants view egocentric videos while receiving brief visual and auditory probes under 3 conditions (aligned with our model prediction, random and contradicting with the model prediction) . Our findings show that channe","cbCaivdQzzPGFDWm","https://ap.wps.com/l/cbCaivdQzzPGFDWm","pdf",5024415,1,"English","en",105,"# Introduction\n# Related Work\n## Sensory Channel Load and Perceptual Surprise","[{\"question\":\"What problem does HeadRoom address for wearable notifications?\",\"answer\":\"It targets how to choose the right sensory output channel at the right moment so notifications do not disrupt the user’s attentional balance under varying perceptual load.\"},{\"question\":\"How does HeadRoom estimate visual and auditory channel availability in real time?\",\"answer\":\"It runs lightweight predictors on egocentric visual and audio streams, using prediction error as a proxy for how occupied each channel is at a given moment.\"},{\"question\":\"What do the study results show about notification routing decisions?\",\"answer\":\"In a controlled psychophysical study with 25 participants, routing notifications to the more available channel improves response time under high 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problem does HeadRoom address for wearable notifications?","Question",{"text":73,"@type":74},"It targets how to choose the right sensory output channel at the right moment so notifications do not disrupt the user’s attentional balance under varying perceptual load.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does HeadRoom estimate visual and auditory channel availability in real time?",{"text":78,"@type":74},"It runs lightweight predictors on egocentric visual and audio streams, using prediction error as a proxy for how occupied each channel is at a given moment.",{"name":80,"@type":71,"acceptedAnswer":81},"What do the study results show about notification routing decisions?",{"text":82,"@type":74},"In a controlled psychophysical study with 25 participants, routing notifications to the more available channel improves response time under high perceptual load, while incorrect routing leads to higher response 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