Over 2 billion people across the world are affected by some visual impairment - mostly related to optical issues, and this number is estimated to grow. Often, particularly in the elderly, more than one condition can affect the eyes at the same time, e.g., myopia and presbyopia. Bifocal or multifocal lenses can be used, these however may become uncomfortable or disturbing and are not adapted to the user. There is therefore a need and opportunity for a new type of glasses able to adaptively change the lenses' focus. This paper explores the feasibility of recording the eye accommodation process in a non-invasive way using a wearable device. This can provide a way to measure eye convergence in real-time to determine what a person's eye is focused on. In this study, Electro-oculography (EoG) is used to observe eye muscle activity and estimate eye movement. To assess this, a group of 11 participants we each asked to switch their gaze from a near to far target and vice versa, whilst their EoG was measured. This revealed two distinct waveforms: one for the transition from a far to near target, and one for the transition from a near to far target. This informed the design of a correlation-based classifier to detect which signals are related to a far to near, or near to far transition. This achieved a classification accuracy of 97.9±1.37% across the experimental results gathered from our 11 participants. This pilot data provides a basic starting point to justify future device development.