Head-mounted sensors and wearable computing for automatic tunnel vision assessment

As the second leading cause of blindness worldwide, glaucoma impacts a large population of individuals over 40. Although visual acuity often remains unaffected in early stages of the disease, visual field loss, expressed by tunnel vision condition, gradually increases. Glaucoma often remains undetected until it has moved into advanced stages. In this paper, we introduce a wearable system for automatic tunnel vision detection using head-mounted sensors and machine learning techniques. We develop several tasks, including reading and observation, and estimate visual field loss by analyzing user's head movements while performing the tasks. An integrated computational module takes sensor signals as input, passes the data through several automatic data processing phases, and returns a final result by merging task-level predictions. For validation purposes, a series of experiments is conducted with 10 participants using tunnel vision simulators. Our results demonstrate that the proposed system can detect mild and moderate tunnel visions with an accuracy of 93.3% using a leave-one-subject-out analysis.