Pupillometry for Arm and Hand Motor Intent Detection

Rehabilitation robots and assistive devices that detect the motor intent of their users can provide more intuitive and effective control. Pupil dilation occurs when people perform motor activities, but its utility for detecting motor intent has not been explored previously. In this work, a human participant research study is conducted to determine if pupillometric data can be used to differentiate between a person's intent to pick up or observe an object. Thirty participants were recruited to perform 120 trials of picking up and observing objects while their pupil dilation was recorded by an eye tracking headset. Features were extracted from the time series data and used to train a neural network classifier. The classifier was tested using leave-one-out cross-validation. The classifier achieved an average accuracy of 59.4% and F1 score of 0.578 across the thirty test datasets. The performance varied significantly depending on the participant used for testing, suggesting that the pupillometric approach to intent detection may be better suited to some participants than others. Future work should determine whether intent detection can be improved with more advanced machine learning methods, such as convolutional neural networks (CNN), and whether intent detection can be performed in real time.