Frequency Response of Polymer Sensor for Measuring Finger Scratching Motion

Okuyama, Takeshi; Hatakeyama, Kazuki; Tanaka, Mami

doi:10.14243/jsaem.23.618

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Examination of the data showed that the two data profiles were similar, as they both consisted of the subject moving their arm rapidly side-to-side. Okuyama et al [ 21 ] presented absolute angles of the index finger when a single subject was scratching, and the team initially wanted to incorporate angles of all the fingers. However, due to having multiple subjects with varying finger lengths, it would be tedious to derive absolute angles of every finger for every subject.…”

Section: Discussionmentioning

confidence: 99%

“…This allows us to detect if each of the fingers were in the “bent” or “extended” state ( Figure 4 , also in Red). It is known that when one scratches, their fingers curl with the fingernails at an approximate angle of 80 degrees with reference to the palm [ 21 ]. Altogether, the data processing generates 13 sets of processed data using 8 sets of raw data, which will be fed into the model for training and verification.…”

Section: Methodsmentioning

confidence: 99%

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A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis

Au,

Leow,

et al. 2023

Sensors

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In this work, a lightweight compliant glove that detects scratching using data from microtubular stretchable sensors on each finger and an inertial measurement unit (IMU) on the palm through a machine learning model is presented: the SensorIsed Glove for Monitoring Atopic Dermatitis (SIGMA). SIGMA provides the user and clinicians with a quantifiable way of assaying scratch as a proxy to itch. With the quantitative information detailing scratching frequency and duration, the clinicians would be able to better classify the severity of itch and scratching caused by atopic dermatitis (AD) more objectively to optimise treatment for the patients, as opposed to the current subjective methods of assessments that are currently in use in hospitals and research settings. The validation data demonstrated an accuracy of 83% of the scratch prediction algorithm, while a separate 30 min validation trial had an accuracy of 99% in a controlled environment. In a pilot study with children (n = 6), SIGMA accurately detected 94.4% of scratching when the glove was donned. We believe that this simple device will empower dermatologists to more effectively measure and quantify itching and scratching in AD, and guide personalised treatment decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis

Au,

Leow,

et al. 2023

Sensors

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show abstract

“…Previous studies have explored different approaches for objectively measuring scratching behavior, including finger flexion [ 5 ], sound detection [ 6 ], and actigraphy assessment of sleep quality and quantity [ 7 , 8 ]. However, none of these methods have demonstrated sufficient accuracy and practicality for routine use in clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy

Xing,

Song,

Crouthamel

et al. 2024

Sensors

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Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.

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Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks

Moreau¹,

Anderer²,

Ross³

et al. 2018

IEEE J. Biomed. Health Inform.

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Atopic dermatitis is a chronic inflammatory skin condition affecting both children and adults and is associated with pruritus. A method for objectively quantifying nocturnal scratching events could aid in the development of therapies for atopic dermatitis and other pruritic disorders. High-resolution wrist actigraphy (three-dimensional accelerometer sensors sampled at 20 Hz) is a noninvasive method to record movement. This paper presents an algorithm to detect nocturnal scratching events based on actigraphy data. The twofold process consists of segmenting the data into "no motion," "single handed motion," and "both handed motion" followed by discriminating motion segments into scratching and other motion using a bidirectional recurrent neural network classifier. The performance was compared against manually scored infrared video data collected from 24 subjects (6 healthy controls and 18 atopic dermatitis patients) demonstrating an score of 0.68 and a rank correlation of 0.945. The algorithm clearly outperformed a published reference method based on wrist actigraphy ( score of 0.09 and a rank correlation of 0.466). The results suggest that scratching movements can be discriminated from other nocturnal movements accurately.

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Frequency Response of Polymer Sensor for Measuring Finger Scratching Motion

Cited by 3 publications

References 16 publications

A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis

A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy

Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks

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