Many studies have reported that glaucoma patients experience mobility issues, such as walking slowly and bumping into obstacles frequently. However, little is known to date about how a person's gait is impacted due to glaucoma. This paper presents design and development of a gait analysis approach using a shoe-integrated sensing system and accompanying machine learning techniques to quantitatively examine gait patterns in glaucoma patients. The customized sensor platform is utilized in a clinical trial conducted with nine glaucoma patients and ten age-matched healthy participants. The signal processing and machine learning algorithms automatically detect effective gait cycles and extract both steady-state and spatio-temporal gait features from the signal segments. We perform machine learning algorithms to distinguish glaucoma patients from healthy controls, and identify several prominent features with high discriminability between the two groups. The results demonstrate that classification algorithms can be used to identify the gait patterns of glaucoma patients with an accuracy higher than 94% in a 10-m-walk test. It is also demonstrated that gait features such as evenness of the sway speed along medio-lateral direction between the two feet are significantly different (p-value < 0.001) between older adults with and without glaucoma. These results suggest that emerging solutions, such as wearable sensing technologies, can be used for continuous and real-time assessment of gait and mobility problems in individuals with low vision, and may open new avenues for using changes in gait patterns for preventing life threatening situations such as falls.
- Gait analysis
- machine learning
- wearable sensors
ASJC Scopus subject areas
- Electrical and Electronic Engineering