Miniaturised and Flexible Patch for Continuous Tremor Monitoring Using Machine Learning

Tremor is an involuntary, rhythmic, oscillatory movement of a body part. It is a common symptom in neuro-logical disorders and significantly impacts daily life. Traditional clinical diagnosis of tremor includes a physical examination of the patient and a review of their medical history. It often fails to assess tremor accurately as the prevalence of tremor changes throughout the day. Therefore, continuous free-living monitoring of tremor symptoms with wearable technology can provide more comprehensive, objective and ecologically valid information compared to traditional clinical diagnoses. In this paper, a miniaturised flexible wearable patch-type device is presented to continuously assess tremor with an accelerometer and a gyroscope, whose recorded data is sent wirelessly to a mobile phone. The proposed patch is 30 mm x 20mm x 8mm in size and only 2 g, which makes it convenient for long periods of monitoring with wearables. Its miniaturised size facilitates an ergonomical attachment to any part of the body while offering an impressive measurement range, i.e., acceleration reaches up to ±8 g and angular velocity measures up to ±2000 degrees per second (dps). This study evaluates the efficacy of three Machine Learning (ML) models, Linear Discriminant Analysis (LDA), Logistic Regression, and AdaBoost, in classifying tremor patterns using data collected from six healthy volunteers and simulated with tremor occurrences by the proposed device. Preliminary results indicate LDA achieved the highest accuracy (68.30 %) and Fl-Score (66.46 %), suggesting its potential effectiveness in tremor pattern recognition, though limited by the small sample size and simulation method.