arXiv:2607. 03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users.
arXiv:2607. 16222v1 Announce Type: cross Abstract: This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency.
arXiv:2607. 02371v1 Announce Type: cross Abstract: Over 285 million people worldwide live with a visual impairment, for whom everyday tasks such as avoiding obstacles, locating personal belongings, recognizing familiar faces, or handling cash remain persistent obstacles to personal autonomy.
This paper introduces an Edge AI system that classifies sleep and wake states on constrained devices using a multimodal pipeline on an ESP32‑S3 microcontroller. It fuses inertial head‑movement sensing with visual pose classification, running in parallel under FreeRTOS to meet real‑time constraints. The two‑stage detection achieves 96.5 % accuracy for motion‑based detection and 89 % for pose classification, proving robust binary sleep‑wake classification in mobile scenarios.
For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it.
arXiv:2609.24526v2 Announce Type: replace Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints...
arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and...
arXiv:2606. 24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise.
arXiv:2504. 17331v3 Announce Type: replace-cross Abstract: Locomotion plays a crucial role in shaping the user experience within virtual reality environments.
RevalExo is a new benchmark for locomotion mode recognition that focuses on functional daily activities performed by older adults and clinical cohorts. It includes 27 participants from three groups—healthy older adults, stroke survivors, and older adults with probable sarcopenia—recorded with lower-body IMUs and, for a subset, synchronized egocentric video. The dataset offers 10.1 hours of frame‑level annotations across 11 locomotion modes, and the authors evaluate unimodal, multimodal, cross‑population, and cross‑modal recognition challenges, finding that sensor fusion improves performance but transitions and generalization remain difficult.
arXiv:2609.21828v1 Announce Type: cross Abstract: Blind and low-vision users often face challenges when locating and physically acquiring objects in unfamiliar indoor environments. Existing vision-la...
The paper surveys the evolution of smart glasses from simple capture devices to first‑person intelligence platforms that integrate human perception, context, and action. It introduces a unified framework that formalizes data flow, hardware capabilities, and seven foundational capabilities, and presents an L0‑L5 hierarchy for capture to embodied action. The study also maps nine application scenes, proposes a nine‑dimensional deployment framework, and outlines an evidence ladder for evaluation and trustworthiness.