This technology is a smart stroller that determines the presence of obstacles by measuring distances periodically using n ultrasonic sensors attached to the front, organizing them into a k x n matrix, and calculating the similarity between a k-dimensional feature vector—created by multiplying a weight matrix—and a reference vector representing obstacle conditions. If an obstacle is detected, the stroller handle vibrates to alert the caregiver.
While the growth of the infant market has led to the development of autonomous strollers, safety technology to detect and avoid or stop for obstacles is essential. Camera-based obstacle detection is inefficient for strollers, creating a need for technology that uses distance data to provide early warnings and emergency braking.
This technology uses vector similarity, combining cosine similarity and Euclidean distance, to evaluate distance data from multiple sensors, making it less sensitive to single-sensor errors. It adjusts handle vibration based on proximity using a vibration intensity table and engages the brakes if feature vector components fall below a threshold. Applicable to smart strollers, autonomous strollers, and low-speed mobility aids like wheelchairs, it prevents collisions even when the caregiver's view is obstructed.
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