1. 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

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 (C body ) 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). V rms = V p / 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 perspiration affects 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 StepDateActivity 1October 23, 2013EMG prototype tooling and hardware setup 2November 14, 2013More research in capacitance sensing tech 3December 3, 2013Phase 1: Tooling, equipment, hardware setup, development environment setup 4December 31, 2013Phase 1: Refinement and accuracy improvements 5January 16, 2013Phase 2: Tooling, equipment, hardware setup, development environment setup 6February 4, 2013Phase 2: Hardware and Software integration 7February 13, 2013Phase 2: Hardware initial design and build of prototype 8March 20, 2013Phase 2 Prototype data analysis 9March 2, 2013Marketing and business model development 10March 30, 2013Refinement and accuracy improvements

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

12. Thanks