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ENG4000 Muscle Sensing for Data Analysis and Treatment

ENG4000 Muscle Sensing for Data Analysis and Treatment Aysar Khalid, Hassan Chehaitli , Mohammad Aryanpour Group 5 Course Director & Advisor: Prof. E. Ghafar-Zadeh Mentor: Mourad Amara. Motivation. Detecting muscle electrical activity using sensors

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ENG4000 Muscle Sensing for Data Analysis and Treatment

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  1. ENG4000 Muscle Sensing for Data Analysis and Treatment Aysar Khalid, Hassan Chehaitli, Mohammad AryanpourGroup 5 Course Director & Advisor: Prof. E. Ghafar-Zadeh Mentor: Mourad Amara

  2. Motivation • Detecting muscle electrical activity using sensors • Autonomous solution that provides data on muscle fatigue, injury, form/posture • Current sensing technology is limited in accuracy • Applications for seniors/elderly health, athletes, ‘quantified’ self

  3. Background • Current techniques (EMG) produces weak signal and expensive • EMG can be thought as 1 dimensional (only measures signal not leg shape) • Capacitance signals can detect changes of leg shapes during motion (Zheng et. al, 2013)

  4. Methodology • Mask leg shape (Cbody) with an array of electrodes • Apply constant frequency signal with constant voltage on one side of electrode mask (electrode1) • Pick up signal from other side electrode (electrode2) • Need a matching impedance (Z) to ensure max power transfer between both electrodes

  5. Methodology (continued) • Raw signal is a sinusoid wave with constant frequency (100 kHz) and varying A -> unsuited for direct sampling • Thus signal is converted to root mean square (RMS) voltage before the analog-to-digital converter (ADC). • Vrms = Vp / sqrt(2) Schematic of test circuit

  6. Progress Results To Date • Currently testing EMG sensor (Advancer Tech) with 3.3V Arduino Pro Micro • EMG signal proved inaccurate and very slow to change to movement of subject • EMG sensor was expensive ($50)

  7. Mismatch of MCU input between the signal voltage and current • Created a current divider and voltage divider to solve this

  8. Discussion From the last presentation • Subject’s body perspirationaffects measurements • Material should be waterproof, need a thermoplastic material • Band location on subject varies • More testing to identify best location

  9. Conclusion ; Next Step • Got software test for EMG prototype fine tuned • Identified good potential mcu to use for production prototype (arm-based cortex) and vendor • Battery life (need one week usage) • Noise interference

  10. Time Management

  11. Acknowledgements Thanks to All the People and Organizations who technically or financially supported your project

  12. Thanks

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