A smart glove for Parkinson’s severity detection via low-frequency motion features and on-device machine learning

Parkinson’s disease (PD) is a progressive neurological disorder affecting fine motor control, yet current assessments rely heavily on brief clinical observations, lacking continuous and objective measurement tools. This study aims to develop a low-cost, real-time system for classifying PD severity using motion data captured by a wearable smart glove. We address three key limitations in existing research: the absence of context-aware monitoring during daily activities, limited use of low-frequency tremor analysis for severity detection, and the lack of embedded models that report system-level performance metrics such as inference time and memory usage. Our glove integrates an Arduino Nano BLE microcontroller and BMI270 IMU sensor to record triaxial hand motion during resting, spoon holding, and eating tasks. Using a 4-second sliding window, we extract a compact set of time-domain (RMS, standard deviation, SMA, ZCR) and frequency-domain (dominant frequency, bandpower) features. These are used to train four lightweight machine learning models – Decision Tree, Logistic Regression, Naïve Bayes, and a Shallow Neural Network – evaluated via subject-independent validation. The Naïve Bayes model achieved the best trade-off with 93.67% test accuracy and real-time inference capability on-device. Our contributions include a reproducible embedded pipeline for PD assessment, integration of quantized models via TensorFlow Lite Micro, SHAP-based interpretability for model transparency, and benchmarking against recent studies. This work demonstrates the feasibility of deploying interpretable, real-time AI models on microcontrollers for practical PD severity monitoring in home or clinical environments.