Wearable Parkinson’s Tremor Monitoring Using a PNC Based Flexible Capacitive Sensor with an AC Bridge and Two-Step Calibration

Muzammil Khan; Faiza Ayyob

doi:10.63313/FE.9002

Authors

Muzammil Khan College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China Author
Faiza Ayyob School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China Author

DOI:

https://doi.org/10.63313/FE.9002

Keywords:

Parkinson’s disease, flexible capacitive sensor, porous nanocomposite, AC bridge, calibration, wearable monitoring

Abstract

Wearable monitoring of Parkinson’s Disease (PD) tremor demands sensors that combine high low-pressure sensitivity with readouts robust to lead and parasitic capacitances. We present a flexible Porous-Nanocomposite (PNC) capacitive pressure sensor that’s integrated into an Alternating Current (AC) De-Sauty bridge, along with a two-step calibration process that effectively cancels out lead and parasitic effects. This innovation allows for picofarad-level resolution in a wearable design. The device features a carbon nanotube (CNT)-doped porous dielectric paired with Ag/PI electrodes, and it utilizes a guarded bridge front end that connects to an instrumentation amplifier and Micro Controller Unit (MCU) equipped with an Analogue to Digital Converter (ADC). Onboard Digital Signal Processing (DSP) handles band-pass filtering (around 3-8 Hz) and Welch spectral estimation for detecting tremors. In our benchtop calibration and tests with consenting volunteers, the system demonstrated an absolute sensitivity of about 2.2pF·kPa⁻¹, with a detection limit close to 14-16 Pa, and it showed a reliable dynamic response within the 4-6 Hz tremor range while keeping baseline noise low and ensuring repeatability under cyclic loading. These findings highlight a compact, low-power solution ideal for continuous fingertip tremor monitoring, paving the way for further IRB-approved clinical validation to explore its potential for diagnosis, symptom tracking, and personalized therapy assessment.

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