ADC Dynamic Performance and Python-Based Processing Platform for EMG Wearable Sensors
Public DepositedThis thesis describes investigation of the dynamic performance of an analog-to-digital converter (ADC) for wireless wearable systems and separately the development of a Python-based application for electromyography (EMG) data acquisition and processing. To assist in the investigation of ADC dynamic performance, a MATLAB implementation of a time-domain sinusoid fitting technique was developed for effective number of bits (ENOB) estimation. Performance of the ADC was measured against power consumption for various resolution and oversampling configurations on Nordic Semiconductor’s nRF52840 system-on-a-chip (SoC). Results showed oversampling to provide a significant improvement in ENOB, though at a substantial relative cost in power consumption. The Python-based application for EMG was developed to facilitate the acquisition, processing, and visualization of biometric data with a user-friendly graphic user interface. The application was developed with a modular design, utilizing object-oriented coding and open-source libraries, to ease future development.
- Creator
- Contributors
- Degree
- Publisher
- Identifier
- etd-121305
- Keyword
- Advisor
- Committee
- Defense date
- Year
- 2024
- Sponsor
- UN Sustainable Development Goals
- Date created
- 2024-04-22
- Resource type
- Source
- etd-121305
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ADC_Dynamic_Performance_and_Python-Based_Processing_Platform.pdf | Public | Download |
Permanent link to this page: https://digital.wpi.edu/show/pg15bj951