# Developing an Objective and Quantifiable Measure of Itch Using Artificial Intelligence and Radio Signals

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $276,902

## Abstract

PROJECT SUMMARY
Chronic itch affects 13% of the population and is associated with over $90 billion in annual population-
expenditures in the US. It has a profound negative impact on quality of life, and is often as debilitating as chronic
pain. Yet, there are currently no FDA-approved treatments for chronic itch. A major obstacle in assessing
therapeutics for itch is the difficulty in measuring it, which hinders assessment of outcomes in the clinic and the
development of new drugs. The current clinical standard for quantifying itch relies on patients’ self-assessment
of the severity of their itch on a scale of 0 to 10, which is: 1) highly subjective and hard to generalize across
patients, 2) lacks sensitivity to small changes, and 3) is difficult to use in vulnerable populations such as children
and those with cognitive impairment. Thus, clinical research on itch has an urgent need for a new objective,
accurate, and low overhead method for quantifying itch. Furthermore, given that disturbed sleep is a major factor
leading to diminished quality of life for chronic itch patients, the new method should ideally also assess sleep
quality. The overall objective of our proposal is to provide an objective, sensitive, and reliable metric for
measuring both itch and its impact on sleep. The central hypothesis of this proposal is that a novel, wireless
sensor can be employed to effectively capture scratching activity and associated itch morbidity, and also
measure its impact on sleep. Our approach is based on a non-obtrusive wireless device that sits in the
background at home, much like a Wi-Fi router. It analyses the radio signals that bounce off people's bodies using
novel machine learning models to infer people’s sleep quality and scratching motion -- and it does it in a touchless
manner without asking patients to wear sensors, or incur any burden. The Katabi lab invented this sensor
technology and has already demonstrated its ability to measure sleep stages, respiration signal, heart rate, falls,
gait and other behaviors in humans. Further, the Katabi and Kim labs have preliminary data that demonstrate
the feasibility of extending this method to monitor scratching in a touchless manner in chronic itch patients. The
specific aims of this proposal will assess the accuracy, sensitivity, and specificity of this novel method in
measuring nocturnal scratching in chronic itch patients, its performance in comparison to the current clinical
standard based on patients’ self-assessment of their condition, and its ability to track changes over time in the
same patient. It will also leverage the device’s ability to monitor sleep to assess the impact of itch on patients’
sleep quality, and the relationship between sleep metrics (e.g., sleep onset, sleep efficiency, and sleep stages)
and scratching severity. The rationale for this proposal is that the ability to quantify itch and its impact on sleep
in an objective, sensitive method that is widely applicable, includin...

## Key facts

- **NIH application ID:** 10365429
- **Project number:** 1R01AR080392-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Dina Katabi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $276,902
- **Award type:** 1
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10365429

## Citation

> US National Institutes of Health, RePORTER application 10365429, Developing an Objective and Quantifiable Measure of Itch Using Artificial Intelligence and Radio Signals (1R01AR080392-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10365429. Licensed CC0.

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