  This paper presents a sparse Change-Based Convolutional Long Short-Term
Memory (CB-ConvLSTM) model for event-based eye tracking, key for
next-generation wearable healthcare technology such as AR/VR headsets. We
leverage the benefits of retina-inspired event cameras, namely their
low-latency response and sparse output event stream, over traditional
frame-based cameras. Our CB-ConvLSTM architecture efficiently extracts
spatio-temporal features for pupil tracking from the event stream,
outperforming conventional CNN structures. Utilizing a delta-encoded recurrent
path enhancing activation sparsity, CB-ConvLSTM reduces arithmetic operations
by approximately 4.7$\times$ without losing accuracy when tested on a
\texttt{v2e}-generated event dataset of labeled pupils. This increase in
efficiency makes it ideal for real-time eye tracking in resource-constrained
devices. The project code and dataset are openly available at
\url{https://github.com/qinche106/cb-convlstm-eyetracking}.
