Development of Surface EMG Sensor Network and its Application System GRRC Int. Workshop 2008 Youngjin Choi Hanyang University

Contents Introduction Development of EMG Sensor Arm Motion Tracking Algorithm Experimental Results Conclusion Future Works

What is EMG EMG(ElectroMyoGram) EMG(ElectroMyoGram) - is one of various bioelectrical signals generated from the human body such as ECG, EEG, EOG, ENG. - has been actively studied for the human muscle analysis, motion imitation, etc. - is one of the best basis signals to develop the bio-mechanical system by transferring the robotics technologies to the rehabilitation engineering Application research filed Application research filed - diagnosis in the medical science - sports science - rehabilitation engineering

Literature Surveys Existing studies about the EMG Existing studies about the EMG - using the learning method (D. Nishikawa, “EMG Prosthetic Hand Controller Using Real-Time Learning Method”) - using the AR model (J. Zhao, “ Levenberg-MarQuardt based neural network control for a five-fingered prosthetic hand”) - using the Hill model (E. Cavallaro, “Hill-based model as a myoprocessor for a neural controlled powered exoskeleton arm-parameters optimization”) - using the pattern recognition for the motion generation of real hand (Y. Su,“Towards and EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning System”) - using the ARMAX model for the tele-operation (P. K. Artemiadis, “EMG-based Teleoperation of a Robot Arm in Planar Catching Movements using ARMAX Model and Trajectory Monitoring Techniques”)

Development of EMG Sensor I Signal Input 1000 times Amplifier High Pass Filter for DC voltage rejection Low Pass Filter for noise rejection Voltage Adder for AD conversion Analog Switch for op-amp drift prevention - Circuit design for signal acquisition

Development of EMG Sensor II Characteristics -4 Channel EMG Measurement -Fast Microprocessor -150M[Hz] -High Resolution -12bit ADC -EMG Sensor Network

Surface-EMG Waveforms The electrodes are for the acquisition of biceps and deltoideus EMG muscle signals. The biceps muscle signal is used for the elbow joint angle extraction. The deltoideus muscle signal is used for the shoulder forward directional angle extraction The maximum amplitude level is about 300[uV]. The EMG signals are amplified by about 1000 times for the signal processing.

Range of Motion (ROM) Biceps brachiiBiceps brachii ROM of an elbow jointROM of an elbow joint 0~145 degrees0~145 degrees DeltoideusDeltoideus ROM of a shoulder jointROM of a shoulder joint 0~180 degrees0~180 degrees

Motion Tracking Algorithm I Taking RMS values Taking RMS values - take the RMS value for EMG[k] grouped by 64 samples

Motion Tracking Algorithm II Taking LPF Taking LPF - filter out the signal by using the 1[Hz] low-pass filter

Motion Tracking Algorithm III Scaling function Scaling function -LPF signals are not proportional to the flexion and extension angles of elbow and shoulder joints -So, we make use of the curve-fitting method -For this, we assume the 3 rd order scaling function for 4-point curve-fitting

Motion Tracking Algorithm IV  Pre-angle Process - Taking RMS - Taking LPF - Applying scaling function

Motion Tracking Algorithm V Optimization ProcessOptimization Process Finally, the recursive-least-squares method is applied to the pre-angle for self-adaptation In this case, the tap-weights are adjusted by itself during the optimization procedures.

Coupling Effect b/w Biceps and Deltoideus -Though the signal measured at a deltoideus muscle must be dominant for the shoulder joint motion, the sEMG signal measured from the biceps brachii has the coupling effect with the shoulder motion -So, we remedy the equation by subtracting the Deltoideus from Biceps brachii Biceps brachii sEMG Deltoideus sEMG

Block Diagram of Entire Algorithm

Experimental Results

Experimental Video The initialization procedure is firstly performed. At the specific joint angles measured from inclinometer, we measure the EMG signal values for the biceps and deltoideus muscles, respectively. Then, we can get the scaling function for elbow joint and shoulder joint angle calculations. And then, we perform the real-time motion tracking simulation. As we can see the video, the experimental results show the real-time good tracking performance for human arm motion.

Concluding Remarks We have developed EMG measurement sensorWe have developed EMG measurement sensor We have suggested the real-time motion tracking algorithm based on the surface EMG signal processingWe have suggested the real-time motion tracking algorithm based on the surface EMG signal processing We have showed the validity of the suggested algorithm through the experimentsWe have showed the validity of the suggested algorithm through the experiments

Future Works EMG sensor networking Applications ▫Master (human) arm device for tele-operation ▫Prosthetic arm for an amputee

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