Dysphagia patients need to carefully control their intake liquid's viscosity to reduce choking and aspiration risks. However, accurate liquid viscosity measurement requires expensive rheometers still unavailable in daily life. Though the existing approximate testing methods are low-cost, they are not convenient for everyday use as they require either tedious procedures or dedicated apparatus. This paper presents ViscoCam, the first liquid viscosity classification system for dysphagia patients or carers, which only requires a smartphone. It is easy to operate, widely deployable, and robust for daily use. ViscoCam classifies visually indistinguishable liquid of various viscosity levels by exploiting the fact that the sloshing motion of viscous liquid decays faster than thin liquid. To perform a measurement, the user shakes a cup of liquid and their smartphone to induce the liquid sloshing motion. Then, ViscoCam senses the cup's motion using the smartphone's built-in accelerometer or microphone and infers liquid viscosity from the fluid surface motion captured by flashlight camera. To combat changes in camera position, lighting conditions, and liquid sloshing motion, a 3D convolutional neural network is trained to extract reliable motion features for classification. We evaluate ViscoCam's performance in classifying three levels in the IDDSI standard, which is the most up-to-date and internationally adopted one for dysphagia patients. Results show that ViscoCam achieves an overall accuracy of 96.52% in controlled cases. It is robust to unseen liquid heights or container sizes, and >81% accuracy is maintained under extreme testing cases.
Development of Gas Viscosity Measurement System with Vibrating Wire Method
