A skin-conformable wireless sensor to objectively quantify symptoms of pruritus

Abstract

Itch is a common clinical symptom and major driver of disease-related morbidity across a wide range of medical conditions. A substantial unmet need is for objective, accurate measurements of itch. In this article, we present a noninvasive technology to objectively quantify scratching behavior via a soft, flexible, and wireless sensor that captures the acousto-mechanic signatures of scratching from the dorsum of the hand. A machine learning algorithm validated on data collected from healthy subjects (n = 10) indicates excellent performance relative to smartwatch-based approaches. Clinical validation in a cohort of predominately pediatric patients (n = 11) with moderate to severe atopic dermatitis included 46 sleep-nights totaling 378.4 hours. The data indicate an accuracy of 99.0% (84.3% sensitivity, 99.3% specificity) against visual observation. This work suggests broad capabilities relevant to applications ranging from assessing the efficacy of drugs for conditions that cause itch to monitoring disease severity and treatment response.

Fig. 1. Overview of the ADAM sensor and signal outputs.

(A) Image of the ADAM sensor in various states of deformation to highlight its soft, flexible construction. (B) Results of measurements of spatiotemporal patterns of motions and vibratory signatures of scratching, with a focus on five different frequency ranges. The experiments involve four accelerometers, one each located at the fingertip, finger, dorsum of the hand, and the wrist, during scratching by articulating the arm for 10 s and then scratching with only the finger for the last 10 s. The color bars represent the mean power of the signal per frequency in each frequency band. The left and right values indicate the minimum and maximum of the respective plot. Upper spatial patterns are from scratching by articulating the arm, and lower spatial patterns are from scratching with only the finger. (C) Path of scratching and resulting data (raw and filtered) data from the sensor. (D) Comparison of signals captured during scratching with five different mounting locations as time series and spectrogram plots. The dorsal hand is an effective mounting location for capturing scratching with only the fingers. Photo credit: Keum San Chun, University of Texas at Austin.

Fig. 2. Representative data collected by the ADAM sensor.

(A) Sample time series data and spectrogram corresponding to scratching activities. Two modes of scratching are conducted on two body parts, dorsum of the hand (DH) and forearm (FA), in two intensities of high (H) and low (L). (B) Time series data of scratching activity in which five different body parts include head (hairy skin), arm (normal skin), abdomen (soft skin), knee (bony prominence skin), and the leg (hard skin). (C) Spectrogram of each time series data in (B). The signals due to the scratching activities have the energy in the 0- to 800-Hz frequency range. (D) Time series data in a wide range of nonscratching activities including simulated moving fingers in the air, waving hand, text messaging, typing on the keyboard, and clicking the mouse. (E) Spectrogram of each time series data in (D). The signals due to nonscratching activities have energy mainly in the range less than 200 Hz.

Fig. 3. Comparison of results obtained with an ADAM device on the hand and an Apple Watch with the Itch Tracker mobile application.

Time series data and the spectrograms for various scratch (skin over the knee where there is a bony prominence, skin of the soft abdomen, and skin of complex surface head) and nonscratch activities (waving of hand and scratching motion in the air), with a 5-s pause between each activity. (A) Scratching by articulating the whole wrist. The Itch Tracker misclassifies hand waving as scratching and fails to detect scratching on the head. (B) Scratching by articulating only the fingers. The ADAM sensor reliably discriminates scratching the skin using only articulation of the fingers. The Itch Tracker cannot detect scratches using only fingers, given its mounting location on the wrist and its inability to record high-frequency information. Photo credit: Keum San Chun, University of Texas at Austin.

Fig. 4. Overview of datasets, signal processing pipelines, and validation methods.

(A) The algorithm validation data were collected from 10 healthy normal volunteers. (B) Activities performed by each participant and their corresponding activity durations. (C) Preprocessing subsequently applied to the sets and segmented into frames by applying a sliding window of 1 s. From each frame, a set of features was extracted. With the feature set extracted from the training frames, a random forest (RF) classifier was trained. The trained classifier was applied to the test feature set for prediction and validation. (D) Time series data of our validation cohort with a single subject performing various scratching and nonscratching activities include 5-s pause periods between each activity. (E) The output probability for scratching is characterized as “1:scratch” if the probability exceeds 50%. The blue line is the probability of scratching for each frame, and the red line indicates the final classified result, where 0 and 1 correspond to nonscratch and scratch, respectively.

Fig. 5. Validation data summary and extracted features.

(A) The clinical validation was conducted in a natural home environment with predominately pediatric AD patients (median age, 10.5 years). An IR camera was used to record scratching behavior of human subjects and was manually graded by two clinical research staff members. (B) A total of 11 AD patients were recruited, generating a total of 46 nights of data in a pooled analysis. (C) A total of nine features were used for LOSO-CV. They are listed in order of their feature importance obtained from the RF classifier. Photo credit: Jan-Kai Chang, Wearifi Inc.