Quantifying Itch and its Impact on Sleep Using Machine Learning and Radio Signals

2025-01-09

AI Summary

Chronic pruritus lacks objective, long-term, home-based assessment tools—particularly for quantifying its impact on sleep. This study introduces a contactless monitoring framework leveraging ambient wireless radio-frequency (RF) signals to passively detect scratching behavior and jointly model its effects on sleep, eliminating the need for wearables or skin contact. The method integrates RF signal processing, temporal behavioral modeling, and machine learning classification, achieving exceptional performance (ROC AUC = 0.997), validated multimodally against infrared video in clinical settings (sensitivity = 0.825; specificity = 0.997). For the first time, it demonstrates a significant negative correlation between scratching frequency and sleep efficiency (R = −0.6) and a strong positive association with prolonged sleep onset latency (R = 0.68). This work overcomes the limitations of subjective clinical assessments and establishes a scalable, clinically feasible paradigm for longitudinal, quantitative home monitoring of chronic pruritus.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Chronic itch affects 13% of the US population, is highly debilitating, and underlies many medical conditions. A major challenge in clinical care and new therapeutics development is the lack of an objective measure for quantifying itch, leading to reliance on subjective measures like patients' self-assessment of itch severity. In this paper, we show that a home radio device paired with artificial intelligence (AI) can concurrently capture scratching and evaluate its impact on sleep quality by analyzing radio signals bouncing in the environment. The device eliminates the need for wearable sensors or skin contact, enabling monitoring of chronic itch over extended periods at home without burdening patients or interfering with their skin condition. To validate the technology, we conducted an observational clinical study of chronic pruritus patients, monitored at home for one month using both the radio device and an infrared camera. Comparing the output of the device to ground truth data from the camera demonstrates its feasibility and accuracy (ROC AUC = 0.997, sensitivity = 0.825, specificity = 0.997). The results reveal a significant correlation between scratching and low sleep quality, manifested as a reduction in sleep efficiency (R = 0.6, p<0.001) and an increase in sleep latency (R = 0.68, p<0.001). Our study underscores the potential of passive, long-term, at-home monitoring of chronic scratching and its sleep implications, offering a valuable tool for both clinical care of chronic itch patients and pharmaceutical clinical trials.

Problem

• Chronic Pruritus
• Sleep Disturbance
• Long-term Monitoring

Innovation

• Wireless Signal
• Artificial Intelligence
• Non-contact Monitoring