ActiviSee: Activity Transition Detection for Human Users through Wearable Sensor-augmented Glasses

Abstract

Falls are the biggest threat among all events to elderly people and patients and pose a serious risk to their well-being, confidence, and mortality. Visual impairments which distort depth perceptions are often identified as major reasons for falls. Single and/or multi-focal length glasses used to correct myopia (blurriness of distance vision) and presbyopia (blurriness of near vision) cause distorted depth perceptions if not used properly. Users, especially older adults, need to change between glasses while transitioning between sedentary to mobile activities and vice versa. Forgetting to change into proper glasses may lead to fall. In this paper, we plan to develop a novel system called Activisee which uses sensor-augmented glasses to automatically detect activity transitions and alert users to change into appropriate glasses whenever necessary. Although, wireless sensor systems have long been used for fall detection and prevention through human activity recognition, using sensor-augmented glasses for activity transition detection has never been studied. Results using data collected from 23 human users performing a set of five distinct activities show that Activisee can successfully detect transitions from sedentary to mobile activities with up to 92% accuracy.