Abstract
Many studies have reported that older adults with glaucoma experience mobility issues due to gait difficulties. These include walking slowly and bumping into obstacles, which increase the risk of falls in glaucoma patients. In this paper, we design and develop a shoe-integrated sensing system as well as signal processing and machine learning algorithms to objectively quantify gait patterns in glaucoma patients. The sensor platform was utilized in a randomized clinical trial involving 9 glaucoma patients and 10 age-matched healthy participants performing a series of gait tests. Sensor signals are collected wirelessly and processed on a local computer. With the captured data, we develop data analysis techniques to make a comparison between gait characteristics in older adults with or without glaucoma. Our results demonstrate that the proposed system achieved an accuracy of more than 90% in distinguishing gait patterns of those with glaucoma from healthy individuals for various gait analysis tests.
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