Authors:
R. Shelishiyah, G. Saranya, Raja Brahmendra Chowdary Veerepalli, A. Shyam Sundhar, Ezgi Demir, Omar Muhammed Neda

Addresses:
Department of Biomedical Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India. Department of Computer Science, Texas State University, San Marcos, Texas, United States of America. Department of Information and Communication, Tampere University, Tampere, Pirkanmaa, Finland. Department of Artificial Intelligence, Lancaster University, Lancaster, Lancashire, England, United Kingdom. Department of Electrical Power Engineering, Sunni Diwan Endowment, Baghdad, Iraq.

Abstract:

Early detection of Parkinson’s disease and proper monitoring of hand movement after fractures are important for improving patient treatment and recovery. In many cases, doctors rely on visual observation and periodic checkups, which may not provide continuous and accurate information. To address this gap, a dual-function smart wearable glove detects early signs of Parkinson’s disease and monitors hand rehabilitation after fractures. The proposed glove uses the XIAO NRF52840 Sense microcontroller. Two force-sensitive resistors (FSRs) are placed on the index finger and thumb to measure grip strength, which is an important indicator of hand function. A MEMS motion sensor captures hand movements, tremors, and motion patterns. For Parkinson’s detection, users perform a spiral drawing task, and the system processes the collected sensor data using a deep learning model trained with Edge Impulse. The model classifies the user’s condition into Non-Parkinson, Stage 1 (Moderate), and Stage 2 (Severe). For post-fracture rehabilitation, the glove continuously records grip force and motion data over time to track improvements in hand coordination and control. An RFID reader and ESP32 module store patient identity and test results, enabling continuous monitoring and personalized assessment. The developed dual-function glove is low-cost, portable, and easy to use, making it suitable for both clinical and home-based monitoring. It provides an objective method for neurological screening and rehabilitation tracking, helping doctors and patients with early diagnosis and recovery evaluation.

Keywords: Wearable Glove; Parkinson’s Disease Detection; Hand Rehabilitation Monitoring; Xiao Nrf52840 Sense; Force-Sensitive Resistors; MEMS Sensor; Spiral Drawing Test.

Received on: 23/07/2025, Revised on: 04/10/2025, Accepted on: 29/10/2025, Published on: 03/06/2026

DOI: 10.69888/FTSBE.2026.000790

FMDB Transactions on Sustainable Biomedical Engineering, 2026 Vol. 1 No. 2, Pages: 86-95

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