Designing and Implementing a High Density Electromyogram Sensor Array

1. Joshua Hernandez, EE Conor O'Reilly, EE Designing and Implementing a High Density Electromyogram Sensor Array Advisor - Professor Hanson

2. Our Goal is to… • Extract data representing activity of individual fingers from surface EMG data, by • Developing and implementing a sensor array, to be worn on the forearm, capable of providing both spatial and temporal EMG data, and • Developing a processing algorithm to utilize the spatial and/or temporal data to decompose the raw EMG data into its SMU constituents and interpret finger activity.

3. Electromyogram Fundamentals • There are two basic types of electromyogram: • Intramuscular EMG • Needle electrode is inserted directly into the desired muscle • Direct electrical connection established to muscle tissue • Records only local motor unit (MU) action potentials (MUAPs) • High signal to noise ratio • Surface EMG • Electrodes are placed on skin over the desired muscle • Indirect electrical connection - signal must pass through the skin • Records a summation of MUAPs over a large area • Potential for a low signal to noise ratio

4. Why Use a Surface EMG? • Intramuscular EMG requires needles. • Needles are no fun. • A surface EMG (sEMG) is non-invasive • Therefore, it is fun. 

5. Inherent Problems with sEMG • Time Delay • Nerve impulse takes time to propagate through the MU • Conduction Velocity (CV) is typically 1 - 10 meters/sec • Muscle fibers within an MU contract at slightly different times • Delay due to CV causes distortion of the recorded signal • Signals from multiple MUs present in signal • A single muscle often has multiple MUs • Crosstalk • Electrode may record signals from nearby muscles

6. So what are we dealing with? We have: 0.5 s of sEMG data using a normal double differential (NDD) electrode configuration recorded from the tibialis anterior of a healthy subject during a 30% maximum voluntary contraction We want: A bunch of these

7. Sensor Array • Placement • Inter-electrode Distance • Motor Unit Innervation Zones

8. A 61-electrode array recording biceps contraction. (A) is filtered raw data, (BCD) have been decomposed.

9. Illustration showing the relation of electrode position to MUAP characteristics.

10. Sensor Array (cont.) • Design • Materials • Pyralux • Stainless Steel Pads • Circuitry • CAD • Need CAD software that supports large boards • Cost • Health • Efficiency • Signal Quality

11. Sensor Array (cont.) • Construction • Printer/Pyralux • Stainless Steel Pads • Amplifiers • Testing • Placement • Spatial Resolution • Signal Quality • Determine Proper Sampling Rate

12. Printing & Etching • wet chemical etching process: used a Xerox™ Phaser® 8650N solid ink printer to print wax directly onto a sheet of DuPont™ Pyralux® AP9121R

13. Block Diagram Sensor & Amplifiers Computer μC Board MATLAB IMPORT Filtering Processing Algorithm Wireless Link A/D μC Wireless Link Determine finger value Decomp. of Signals SMU Classification Control

14. Computer Connection • A/D Hardware • Number of Channels Needed • Bit Rate/ Depth • Multiplexing? • Connection • Microprocessor • Microproccessing • USB connection • Possible Future radio Link • Drivers • Import to MATLAB

15. Receiving the Signals The Vernier EKG (Electrocardiogram or ECG) Sensor

16. Processing etc… • Digital Filter • 60 Hz notch • Additional Noise Filtering (20Hz < Signal <2kHz) • SMU Decomposition • MUAP Classification • Decompose Signal • Assign Finger Values • Processing Algorithm • Wavelets • Mexican Hat • Reqs. Unipolar input • Amplitude/C.V. • Reqs. Bipolar i/p • Support Vector Machine • linearly classifies the features into related groupings

17. Processing of Data Data Prep and Feature identification: Identification Algorithm :

18. Questions?

19. Special Thanks to… • Konstantin Avdaschenko, EE • Demarcus Hamm, EE • Travis Hoh, Neuroscience • Prof. Hanson, EE • Prof. Hedrick, EE • Prof. Catravas, EE • Prof. Olberg, Neuroscience • Prof. Rice, Biology