Wearable impedance oculography: a new method for eye motion classification

Electrooculography (EOG) for eye movement detection and condition monitoring are affected by variable baseline drifts and are susceptible to motion artifact-induced noise. To address these limitations, this work introduces an eye movement classification approach, covering blink, saccade, smooth pursuit, vergence, and vestibulo-ocular movements, using impedance measurement from an eye wearable system. Impedance oculography (IOG) data were collected from subjects performing the specified eye movements using a spectacle-mounted two-electrode system connected to a IOG measurement set up, which, with appropriate modifications can be realized as a compact wearable device. The collected data were processed through baseline drift correction, wavelet filtering, and windowing. Notably, the proposed approach eliminates the need for explicit feature extraction for identifying the inherent spatial characteristics of IOG signal for activity classification. Here, a convolution neural network with stratified 5-fold cross validation was implemented to classify the eye movements, with 80% of the data used for training and 20% for testing. The IOG data collected from subjects over extended durations indicted uniform baseline drift, demonstrating the superiority of the IOG over EOG for eye motion signal acquisitions. High class-specific accuracies of 95%, 97%, 97%, 100% and 93% for blink, saccade, smooth pursuit, vergence and vestibulo-ocular movements, respectively, confirm the efficacy of the proposed method for accurate eye-movement classification in wearable eye-tracking systems.