Instillation Checking Using Long Short-Term Memories for Ophthalmology Patients

In this paper, a checking system for eye lotion instillations of ophthalmology patients is proposed, using a neural network. It first estimates tilt angles of the eye dropper bottle from acceleration values measured by a triaxial sensor. A sequence of such estimated values is next presented to a discrimination model after network learning, and its output value is considered to be a certainty degree indicating whether an eye lotion is applied at the time zone corresponding to the sequence. The final judgement for the instillation is made by conducting thresholding of the certainty degree. The proposed method employs either a long short-term memory (LSTM for short) or a bidirectional long short-term memory (B_LSTM for short) to construct the discrimination model. Experimental results using practical data reveal that the B_LSTM-based model achieves favorable metric values compared to the LSTM-based model. In addition, it is explored whether the metric values depend on the interval between the time points when the tilt values are measured.