Surface Classification and Predictive Path Analysis for Hazard Avoidance in Wearable Display Devices
Abstract
Wearable electronic devices face challenges in identifying surface hazards, such as ice, potholes, or cliffs, while providing relevant safety information is prioritized for the wearer. Traditional methods often struggle with detecting translucent or low-contrast hazards and can result in information overload. This disclosure describes a method for hazard identification and avoidance that utilizes signal artifacts from time-of-flight sensors combined with predictive path analysis. Surface composition is classified by analyzing the noisiness and reflection patterns of sensor signals, which allows for the differentiation of materials like snow, ice, or water from diffuse surfaces like gravel. Concurrently, a projected path is generated using gait modeling and foot position observation to determine the likelihood of encountering an identified hazard. This approach improves pedestrian safety by providing contextually relevant warnings and adaptive visual feedback, such as highlighting obstacles or providing high-gain visual feeds during a detected fall, thereby reducing the risk of injury.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yu, Michael Christopher, "Surface Classification and Predictive Path Analysis for Hazard Avoidance in Wearable Display Devices", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10074