An Investigation into the Detection of Human Scratching Activity Based on Deep Learning Models

Because pruritus is often overlooked and undertreated in the clinical setting, a major unmet need is objective measures of behaviors associated with scratching in order to quantify itch severity and frequency since scratch directly correlates to itch. Such methods to measure itch and how itch severity changes over time are needed to objectively study and understand pruritus, develop and assess the efficacy of new medications, quantify disease severity in patients, and monitor treatment response. Wearable sensors in the form of wrist actigraphy, which detects wrist movements over time using micro-accelerometers, are the most studied and tested method to detect scratching events. To address these issues, 7 deep learning models will be used to train and test for scratch detection, including: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) – Gated Recurrent Unit (GRU), RNN – Long Short-Term Memory (LSTM), CNN & RNN – GRU (end-to-end), CNN & RNN – LSTM (end-to-end), CNN & RNN – GRU (parallel) and CNN & RNN – LSTM (parallel). The final results show accurately detect scratching using deep learning (CNN achieved a high accuracy of 0.996) in various situations and can provide useful information (time, frequency, scratched body part, etc.) regarding the scratching behavior in day and nighttime in order to better quantify pruritus for use in the medical field.