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Machine Learning-Based Self-Induced Scratch Intensity Detection Using Feature Optimization and Multi-Channel
Muhammad Omar Cheema1, Alina Akhlaq2, Zia Mohy Ud Din1
1Department of Biomedical Engineering, Air University, Islamabad 44000, Pakistan.
Bioengineering (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a novel method using forearm muscle activity (electromyography) to accurately detect and quantify scratching intensity in patients with chronic pruritus. This approach offers improved reliability over existing technologies for better diagnosis and management.
Area of Science:
- Biomedical Engineering
- Dermatology
- Rehabilitation Technology
Background:
- Chronic pruritus significantly impacts quality of life, leading to skin damage and infections.
- Current scratch monitoring technologies (accelerometers, gyroscopes, cameras) lack reliability in assessing scratching intensity.
- Accurate quantification of scratching is crucial for effective management of dermatological conditions.
Purpose of the Study:
- To develop a method for accurately detecting and quantifying self-induced scratching intensity using muscle activity.
- To identify specific forearm muscles that generate consistent signals during scratching.
- To explore the potential of electromyography (EMG) for real-time scratching feedback.
Main Methods:
- Acquired and preprocessed 3-channel EMG signals from three identified forearm muscles.
- Analyzed EMG signals using time- and frequency-domain features.
- Optimized 45 extracted features using recursive feature elimination and cross-validation.
Main Results:
- Identified three forearm muscles providing consistent and distinctive EMG signals during scratching.
- Achieved a highest optimized accuracy of 0.8628 for scratch detection using a combination of muscle EMG signals.
- Demonstrated the feasibility of using EMG for quantifying scratching intensity.
Conclusions:
- Muscle activity measurement via EMG shows promise for accurate scratch detection and quantification.
- This approach can enhance the diagnosis and management of scratching intensity in dermatological patients.
- The developed model may support future wearable devices for alerts and advanced therapy.